endpoint_request_func.py 25.4 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""The request function for API endpoints."""

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import io
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import json
import os
import sys
import time
import traceback
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from collections.abc import Awaitable
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from dataclasses import dataclass, field
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from typing import Any, Literal, Protocol
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import aiohttp
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import regex as re
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from tqdm.asyncio import tqdm

AIOHTTP_TIMEOUT = aiohttp.ClientTimeout(total=6 * 60 * 60)


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class StreamedResponseHandler:
    """Handles streaming HTTP responses by accumulating chunks until complete
    messages are available."""

    def __init__(self):
        self.buffer = ""

    def add_chunk(self, chunk_bytes: bytes) -> list[str]:
        """Add a chunk of bytes to the buffer and return any complete
        messages."""
        chunk_str = chunk_bytes.decode("utf-8")
        self.buffer += chunk_str

        messages = []

        # Split by double newlines (SSE message separator)
        while "\n\n" in self.buffer:
            message, self.buffer = self.buffer.split("\n\n", 1)
            message = message.strip()
            if message:
                messages.append(message)

        # if self.buffer is not empty, check if it is a complete message
        # by removing data: prefix and check if it is a valid JSON
        if self.buffer.startswith("data: "):
            message_content = self.buffer.removeprefix("data: ").strip()
            if message_content == "[DONE]":
                messages.append(self.buffer.strip())
                self.buffer = ""
            elif message_content:
                try:
                    json.loads(message_content)
                    messages.append(self.buffer.strip())
                    self.buffer = ""
                except json.JSONDecodeError:
                    # Incomplete JSON, wait for more chunks.
                    pass

        return messages


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@dataclass
class RequestFuncInput:
    """The input for the request function."""
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    prompt: str | list[str]
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    api_url: str
    prompt_len: int
    output_len: int
    model: str
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    model_name: str | None = None
    logprobs: int | None = None
    extra_headers: dict | None = None
    extra_body: dict | None = None
    multi_modal_content: dict | list[dict] | None = None
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    ignore_eos: bool = False
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    language: str | None = None
    request_id: str | None = None
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@dataclass
class RequestFuncOutput:
    """The output of the request function including metrics."""
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    generated_text: str = ""
    success: bool = False
    latency: float = 0.0
    output_tokens: int = 0
    ttft: float = 0.0  # Time to first token
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    itl: list[float] = field(default_factory=list)  # list of inter-token latencies
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    tpot: float = 0.0  # avg next-token latencies
    prompt_len: int = 0
    error: str = ""
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    start_time: float = 0.0
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class RequestFunc(Protocol):
    def __call__(
        self,
        request_func_input: RequestFuncInput,
        session: aiohttp.ClientSession,
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        pbar: tqdm | None = None,
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    ) -> Awaitable[RequestFuncOutput]: ...
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def _validate_api_url(
    api_url: str,
    api_name: str,
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    expected_suffixes: str | set[str],
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) -> None:
    if isinstance(expected_suffixes, str):
        expected_suffixes = {expected_suffixes}

    expected_suffixes = {*expected_suffixes, "profile"}

    if not api_url.endswith(tuple(expected_suffixes)):
        raise ValueError(f"{api_name} URL must end with one of: {expected_suffixes}.")


def _update_payload_common(
    payload: dict[str, Any],
    request_func_input: RequestFuncInput,
) -> None:
    if request_func_input.ignore_eos:
        payload["ignore_eos"] = request_func_input.ignore_eos
    if request_func_input.extra_body:
        payload.update(request_func_input.extra_body)


def _update_headers_common(
    headers: dict[str, Any],
    request_func_input: RequestFuncInput,
) -> None:
    if request_func_input.extra_headers:
        headers |= request_func_input.extra_headers
    if request_func_input.request_id:
        headers["x-request-id"] = request_func_input.request_id


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def _get_headers(content_type: str | None = None) -> dict[str, str]:
    headers = {}
    if content_type:
        headers["Content-Type"] = content_type
    api_key = os.environ.get("OPENAI_API_KEY")
    if api_key:
        headers["Authorization"] = f"Bearer {api_key}"
    return headers


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async def async_request_openai_completions(
    request_func_input: RequestFuncInput,
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    session: aiohttp.ClientSession,
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    pbar: tqdm | None = None,
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) -> RequestFuncOutput:
    """The async request function for the OpenAI Completions API.

    Args:
        request_func_input: The input for the request function.
        pbar: The progress bar to display the progress.

    Returns:
        The output of the request function.
    """
    api_url = request_func_input.api_url
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    _validate_api_url(api_url, "OpenAI Completions API", "completions")
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    payload = {
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        "model": request_func_input.model_name
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        if request_func_input.model_name
        else request_func_input.model,
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        "prompt": request_func_input.prompt,
        "repetition_penalty": 1.0,
        "max_tokens": request_func_input.output_len,
        "logprobs": request_func_input.logprobs,
        "stream": True,
        "stream_options": {
            "include_usage": True,
        },
    }
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    _update_payload_common(payload, request_func_input)

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    headers = _get_headers()
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    _update_headers_common(headers, request_func_input)
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    output = RequestFuncOutput()
    output.prompt_len = request_func_input.prompt_len

    generated_text = ""
    st = time.perf_counter()
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    output.start_time = st
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    most_recent_timestamp = st
    try:
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        async with session.post(url=api_url, json=payload, headers=headers) as response:
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            if response.status == 200:
                first_chunk_received = False
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                handler = StreamedResponseHandler()

                async for chunk_bytes in response.content.iter_any():
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                    chunk_bytes = chunk_bytes.strip()
                    if not chunk_bytes:
                        continue

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                    messages = handler.add_chunk(chunk_bytes)
                    for message in messages:
                        # NOTE: SSE comments (often used as pings) start with
                        # a colon. These are not JSON data payload and should
                        # be skipped.
                        if message.startswith(":"):
                            continue
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                        chunk = message.removeprefix("data: ")
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                        if chunk != "[DONE]":
                            data = json.loads(chunk)
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                            # NOTE: Some completion API might have a last
                            # usage summary response without a token so we
                            # want to check a token was generated
                            if choices := data.get("choices"):
                                # Note that text could be empty here
                                # e.g. for special tokens
                                text = choices[0].get("text")
                                timestamp = time.perf_counter()
                                # First token
                                if not first_chunk_received:
                                    first_chunk_received = True
                                    ttft = time.perf_counter() - st
                                    output.ttft = ttft
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                                # Decoding phase
                                else:
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                                    output.itl.append(timestamp - most_recent_timestamp)
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                                most_recent_timestamp = timestamp
                                generated_text += text or ""
                            elif usage := data.get("usage"):
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                                output.output_tokens = usage.get("completion_tokens")
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                if first_chunk_received:
                    output.success = True
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                else:
                    output.success = False
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                    output.error = (
                        "Never received a valid chunk to calculate TTFT."
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                        "This response will be marked as failed!"
                    )
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                output.generated_text = generated_text
                output.latency = most_recent_timestamp - st
            else:
                output.error = response.reason or ""
                output.success = False
    except Exception:
        output.success = False
        exc_info = sys.exc_info()
        output.error = "".join(traceback.format_exception(*exc_info))
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    if pbar:
        pbar.update(1)
    return output


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def _get_chat_content(
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    request_func_input: RequestFuncInput,
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    mm_position: Literal["first", "last"] = "last",
) -> list[dict[str, Any]]:
    text_contents = [{"type": "text", "text": request_func_input.prompt}]
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    mm_contents = []
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    if request_func_input.multi_modal_content:
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        mm_content = request_func_input.multi_modal_content
        if isinstance(mm_content, list):
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            mm_contents.extend(request_func_input.multi_modal_content)
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        elif isinstance(mm_content, dict):
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            mm_contents.append(request_func_input.multi_modal_content)
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        else:
            raise TypeError(
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                "multi_modal_content must be a dict or list[dict] for openai-chat"
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            )
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    if mm_position == "first":
        return mm_contents + text_contents

    return text_contents + mm_contents


async def async_request_openai_chat_completions(
    request_func_input: RequestFuncInput,
    session: aiohttp.ClientSession,
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    pbar: tqdm | None = None,
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    mm_position: Literal["first", "last"] = "last",
) -> RequestFuncOutput:
    api_url = request_func_input.api_url
    _validate_api_url(api_url, "OpenAI Chat Completions API", "chat/completions")

    content = _get_chat_content(request_func_input, mm_position=mm_position)

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    payload = {
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        "model": request_func_input.model_name
        if request_func_input.model_name
        else request_func_input.model,
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        "messages": [
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            {"role": "user", "content": content},
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        ],
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        "max_completion_tokens": request_func_input.output_len,
        "stream": True,
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        "stream_options": {
            "include_usage": True,
        },
    }
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    _update_payload_common(payload, request_func_input)

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    headers = _get_headers("application/json")
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    _update_headers_common(headers, request_func_input)
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    output = RequestFuncOutput()
    output.prompt_len = request_func_input.prompt_len

    generated_text = ""
    ttft = 0.0
    st = time.perf_counter()
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    output.start_time = st
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    most_recent_timestamp = st
    try:
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        async with session.post(url=api_url, json=payload, headers=headers) as response:
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            if response.status == 200:
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                handler = StreamedResponseHandler()
                async for chunk_bytes in response.content.iter_any():
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                    chunk_bytes = chunk_bytes.strip()
                    if not chunk_bytes:
                        continue

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                    messages = handler.add_chunk(chunk_bytes)
                    for message in messages:
                        # NOTE: SSE comments (often used as pings) start with
                        # a colon. These are not JSON data payload and should
                        # be skipped.
                        if message.startswith(":"):
                            continue

                        chunk = message.removeprefix("data: ")
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                        if chunk != "[DONE]":
                            timestamp = time.perf_counter()
                            data = json.loads(chunk)
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                            if choices := data.get("choices"):
                                content = choices[0]["delta"].get("content")
                                # First token
                                if ttft == 0.0:
                                    ttft = timestamp - st
                                    output.ttft = ttft
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                                # Decoding phase
                                else:
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                                    output.itl.append(timestamp - most_recent_timestamp)
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                                generated_text += content or ""
                            elif usage := data.get("usage"):
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                                output.output_tokens = usage.get("completion_tokens")
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                            most_recent_timestamp = timestamp
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                output.generated_text = generated_text
                output.success = True
                output.latency = most_recent_timestamp - st
            else:
                output.error = response.reason or ""
                output.success = False
    except Exception:
        output.success = False
        exc_info = sys.exc_info()
        output.error = "".join(traceback.format_exception(*exc_info))

    if pbar:
        pbar.update(1)
    return output


async def async_request_openai_audio(
    request_func_input: RequestFuncInput,
    session: aiohttp.ClientSession,
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    pbar: tqdm | None = None,
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) -> RequestFuncOutput:
    # Lazy import without PlaceholderModule to avoid vllm dep.
    import soundfile

    api_url = request_func_input.api_url
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    _validate_api_url(api_url, "OpenAI Audio API", {"transcriptions", "translations"})
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    content = [{"type": "text", "text": request_func_input.prompt}]
    payload = {
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        "model": request_func_input.model_name
        if request_func_input.model_name
        else request_func_input.model,
        "max_completion_tokens": request_func_input.output_len,
        "stream": True,
        "language": "en",
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        # Flattened due to multipart/form-data
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        "stream_include_usage": True,
        "stream_continuous_usage_stats": True,
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    }
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    _update_payload_common(payload, request_func_input)

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    headers = _get_headers()
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    _update_headers_common(headers, request_func_input)
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    # Send audio file
    def to_bytes(y, sr):
        buffer = io.BytesIO()
        soundfile.write(buffer, y, sr, format="WAV")
        buffer.seek(0)
        return buffer

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    mm_audio = request_func_input.multi_modal_content
    if not isinstance(mm_audio, dict) or "audio" not in mm_audio:
        raise TypeError("multi_modal_content must be a dict containing 'audio'")
    with to_bytes(*mm_audio["audio"]) as f:
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        form = aiohttp.FormData()
        form.add_field("file", f, content_type="audio/wav")
        for key, value in payload.items():
            form.add_field(key, str(value))
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        output = RequestFuncOutput()
        output.prompt_len = request_func_input.prompt_len

        generated_text = ""
        ttft = 0.0
        st = time.perf_counter()
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        output.start_time = st
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        most_recent_timestamp = st
        try:
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            async with session.post(
                url=api_url, data=form, headers=headers
            ) as response:
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                if response.status == 200:
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                    handler = StreamedResponseHandler()

                    async for chunk_bytes in response.content.iter_any():
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                        chunk_bytes = chunk_bytes.strip()
                        if not chunk_bytes:
                            continue

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                        messages = handler.add_chunk(chunk_bytes)
                        for message in messages:
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                            chunk = message.decode("utf-8").removeprefix("data: ")
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                            if chunk != "[DONE]":
                                timestamp = time.perf_counter()
                                data = json.loads(chunk)

                                if choices := data.get("choices"):
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                                    content = choices[0]["delta"].get("content")
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                                    # First token
                                    if ttft == 0.0:
                                        ttft = timestamp - st
                                        output.ttft = ttft

                                    # Decoding phase
                                    else:
                                        output.itl.append(
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                                            timestamp - most_recent_timestamp
                                        )
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                                    generated_text += content or ""
                                elif usage := data.get("usage"):
                                    output.output_tokens = usage.get(
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                                        "completion_tokens"
                                    )
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                                most_recent_timestamp = timestamp
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                    output.generated_text = generated_text
                    output.success = True
                    output.latency = most_recent_timestamp - st
                else:
                    output.error = response.reason or ""
                    output.success = False
        except Exception:
            output.success = False
            exc_info = sys.exc_info()
            output.error = "".join(traceback.format_exception(*exc_info))

    if pbar:
        pbar.update(1)
    return output


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async def _run_pooling_request(
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    session: aiohttp.ClientSession,
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    api_url: str,
    payload: dict[str, Any],
    headers: dict[str, Any],
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    pbar: tqdm | None = None,
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) -> RequestFuncOutput:
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    output = RequestFuncOutput()
    st = time.perf_counter()
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    output.start_time = st
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    try:
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        async with session.post(url=api_url, headers=headers, json=payload) as response:
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            if response.status == 200:
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                output.ttft = output.latency = time.perf_counter() - st
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                if payload.get("encoding_format", "float") == "bytes":
                    metadata = json.loads(response.headers["metadata"])
                    usage = metadata.get("usage", {})
                else:
                    data = await response.json()
                    usage = data.get("usage", {})

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                output.success = True
                output.generated_text = ""
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                output.prompt_len = usage.get("prompt_tokens", 0)
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            else:
                output.success = False
                output.error = response.reason or ""
    except Exception as e:
        output.success = False
        output.error = str(e)

    if pbar:
        pbar.update(1)
    return output


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async def async_request_openai_embeddings(
    request_func_input: RequestFuncInput,
    session: aiohttp.ClientSession,
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    pbar: tqdm | None = None,
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) -> RequestFuncOutput:
    api_url = request_func_input.api_url
    _validate_api_url(api_url, "OpenAI Embeddings API", "embeddings")

    payload = {
        "model": request_func_input.model_name
        if request_func_input.model_name
        else request_func_input.model,
        "input": request_func_input.prompt,
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        # Many embedding models have short context length,
        # this is to avoid dropping some of the requests.
        "truncate_prompt_tokens": -1,
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    }
    _update_payload_common(payload, request_func_input)

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    headers = _get_headers("application/json")
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    _update_headers_common(headers, request_func_input)

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    return await _run_pooling_request(
        session,
        api_url,
        payload=payload,
        headers=headers,
        pbar=pbar,
    )


async def async_request_vllm_rerank(
    request_func_input: RequestFuncInput,
    session: aiohttp.ClientSession,
    pbar: tqdm | None = None,
) -> RequestFuncOutput:
    api_url = request_func_input.api_url
    _validate_api_url(api_url, "vLLM score API", "rerank")

    assert (
        isinstance(request_func_input.prompt, list)
        and len(request_func_input.prompt) > 1
    )

    payload = {
        "model": request_func_input.model_name
        if request_func_input.model_name
        else request_func_input.model,
        "query": request_func_input.prompt[0],
        "documents": request_func_input.prompt[1:],
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        # Many reranker models have short context length,
        # this is to avoid dropping some of the requests.
        "truncate_prompt_tokens": -1,
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    }

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    headers = _get_headers("application/json")
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    _update_headers_common(headers, request_func_input)

    return await _run_pooling_request(
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        session,
        api_url,
        payload=payload,
        headers=headers,
        pbar=pbar,
    )


async def async_request_openai_embeddings_chat(
    request_func_input: RequestFuncInput,
    session: aiohttp.ClientSession,
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    pbar: tqdm | None = None,
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    mm_position: Literal["first", "last"] = "last",
) -> RequestFuncOutput:
    api_url = request_func_input.api_url
    _validate_api_url(api_url, "OpenAI Embeddings API", "embeddings")

    content = _get_chat_content(request_func_input, mm_position=mm_position)

    payload = {
        "model": request_func_input.model_name
        if request_func_input.model_name
        else request_func_input.model,
        "messages": [
            {"role": "user", "content": content},
        ],
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        # Many embedding models have short context length,
        # this is to avoid dropping some of the requests.
        "truncate_prompt_tokens": -1,
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    }
    _update_payload_common(payload, request_func_input)

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    headers = _get_headers("application/json")
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    _update_headers_common(headers, request_func_input)

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    return await _run_pooling_request(
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        session,
        api_url,
        payload=payload,
        headers=headers,
        pbar=pbar,
    )


def _try_extract_request_idx(request_func_input: RequestFuncInput):
    if request_func_input.request_id:
        match = re.search(r"(\d+)$", request_func_input.request_id)
        if match:
            try:
                return int(match.group(1))
            except ValueError:
                pass

    return None


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def _preprocess_clip(request_func_input: RequestFuncInput):
    if request_func_input.multi_modal_content:
        # Image input
        request_func_input.prompt = ""


def _preprocess_vlm2vec(request_func_input: RequestFuncInput):
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    if request_func_input.multi_modal_content:
        request_idx = _try_extract_request_idx(request_func_input)

        # Adjust the ratio manually if needed.
        use_image_only_prompt = request_idx is None or request_idx % 2 == 0

        if use_image_only_prompt:
            # Image input
            request_func_input.prompt = "Represent the given image."
        else:
            # Text+Image input
            request_func_input.prompt = (
                f"Represent the given image with the following question: "
                f"{request_func_input.prompt}"
            )

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async def async_request_openai_embeddings_clip(
    request_func_input: RequestFuncInput,
    session: aiohttp.ClientSession,
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    pbar: tqdm | None = None,
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) -> RequestFuncOutput:
    _preprocess_clip(request_func_input)

    return await async_request_openai_embeddings_chat(
        request_func_input,
        session,
        pbar=pbar,
    )


async def async_request_openai_embeddings_vlm2vec(
    request_func_input: RequestFuncInput,
    session: aiohttp.ClientSession,
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    pbar: tqdm | None = None,
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) -> RequestFuncOutput:
    _preprocess_vlm2vec(request_func_input)

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    return await async_request_openai_embeddings_chat(
        request_func_input,
        session,
        pbar=pbar,
        mm_position="first",
    )


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async def async_request_infinity_embeddings(
    request_func_input: RequestFuncInput,
    session: aiohttp.ClientSession,
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    pbar: tqdm | None = None,
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) -> RequestFuncOutput:
    api_url = request_func_input.api_url
    _validate_api_url(api_url, "Infinity Embeddings API", "embeddings")

    payload = {
        "model": request_func_input.model_name
        if request_func_input.model_name
        else request_func_input.model,
    }

    if request_func_input.prompt:
        payload["input"] = request_func_input.prompt
    else:
        mm_content = request_func_input.multi_modal_content
        assert isinstance(mm_content, dict)

        mm_type = mm_content["type"]
        payload["input"] = mm_content[mm_type]["url"]
        payload["modality"] = mm_type.split("_", 1)[0]

    _update_payload_common(payload, request_func_input)

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    headers = _get_headers("application/json")
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    _update_headers_common(headers, request_func_input)

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    return await _run_pooling_request(
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        session,
        api_url,
        payload=payload,
        headers=headers,
        pbar=pbar,
    )


async def async_request_infinity_embeddings_clip(
    request_func_input: RequestFuncInput,
    session: aiohttp.ClientSession,
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    pbar: tqdm | None = None,
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) -> RequestFuncOutput:
    _preprocess_clip(request_func_input)

    return await async_request_infinity_embeddings(
        request_func_input,
        session,
        pbar=pbar,
    )


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# TODO: Add more request functions for different API protocols.
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ASYNC_REQUEST_FUNCS: dict[str, RequestFunc] = {
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    "vllm": async_request_openai_completions,
    "openai": async_request_openai_completions,
    "openai-chat": async_request_openai_chat_completions,
    "openai-audio": async_request_openai_audio,
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    "openai-embeddings": async_request_openai_embeddings,
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    "openai-embeddings-chat": async_request_openai_embeddings_chat,
    "openai-embeddings-clip": async_request_openai_embeddings_clip,
    "openai-embeddings-vlm2vec": async_request_openai_embeddings_vlm2vec,
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    # Infinity embedding server: https://github.com/michaelfeil/infinity
    "infinity-embeddings": async_request_infinity_embeddings,
    "infinity-embeddings-clip": async_request_infinity_embeddings_clip,
    # (Infinity embedding server does not support vlm2vec)
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    "vllm-rerank": async_request_vllm_rerank,
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}
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OPENAI_COMPATIBLE_BACKENDS = [
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    k
    for k, v in ASYNC_REQUEST_FUNCS.items()
    if v in (async_request_openai_completions, async_request_openai_chat_completions)
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]