endpoint_request_func.py

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

import io
import json
import os
import sys
import time
import traceback
from dataclasses import dataclass, field
from typing import Optional, Union

import aiohttp
from tqdm.asyncio import tqdm

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


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


@dataclass
class RequestFuncInput:
    """The input for the request function."""
    prompt: str
    api_url: str
    prompt_len: int
    output_len: int
    model: str
    model_name: Optional[str] = None
    logprobs: Optional[int] = None
    extra_body: Optional[dict] = None
    multi_modal_content: Optional[Union[dict, list[dict]]] = None
    ignore_eos: bool = False
    language: Optional[str] = None
    request_id: Optional[str] = None


@dataclass
class RequestFuncOutput:
    """The output of the request function including metrics."""
    generated_text: str = ""
    success: bool = False
    latency: float = 0.0
    output_tokens: int = 0
    ttft: float = 0.0  # Time to first token
    itl: list[float] = field(
        default_factory=list)  # list of inter-token latencies
    tpot: float = 0.0  # avg next-token latencies
    prompt_len: int = 0
    error: str = ""


async def async_request_openai_completions(
    request_func_input: RequestFuncInput,
    session: aiohttp.ClientSession,
    pbar: Optional[tqdm] = None,
) -> 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
    assert api_url.endswith(
        ("completions", "profile")
    ), "OpenAI Completions API URL must end with 'completions' or 'profile'."

    payload = {
        "model": request_func_input.model_name
        if request_func_input.model_name else request_func_input.model,
        "prompt": request_func_input.prompt,
        "temperature": 0.0,
        "repetition_penalty": 1.0,
        "max_tokens": request_func_input.output_len,
        "logprobs": request_func_input.logprobs,
        "stream": True,
        "stream_options": {
            "include_usage": True,
        },
    }
    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)
    headers = {
        "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"
    }
    if request_func_input.request_id:
        headers["x-request-id"] = request_func_input.request_id

    output = RequestFuncOutput()
    output.prompt_len = request_func_input.prompt_len

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

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

                    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: ")

                        if chunk != "[DONE]":
                            data = json.loads(chunk)

                            # 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

                                # Decoding phase
                                else:
                                    output.itl.append(timestamp -
                                                    most_recent_timestamp)

                                most_recent_timestamp = timestamp
                                generated_text += text or ""
                            elif usage := data.get("usage"):
                                output.output_tokens = usage.get(
                                    "completion_tokens")
                if first_chunk_received:
                    output.success = True
                else:
                    output.success = False
                    output.error = (
                        "Never received a valid chunk to calculate TTFT."
                        "This response will be marked as failed!")
                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))

    if pbar:
        pbar.update(1)
    return output


async def async_request_openai_chat_completions(
    request_func_input: RequestFuncInput,
    session: aiohttp.ClientSession,
    pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
    api_url = request_func_input.api_url
    assert api_url.endswith(("chat/completions", "profile")), (
        "OpenAI Chat Completions API URL must end with 'chat/completions'.")

    content = [{"type": "text", "text": request_func_input.prompt}]
    if request_func_input.multi_modal_content:
        mm_content = request_func_input.multi_modal_content
        if isinstance(mm_content, list):
            content.extend(mm_content)
        elif isinstance(mm_content, dict):
            content.append(mm_content)
        else:
            raise TypeError(
                "multi_modal_content must be a dict or list[dict] "
                "for openai-chat"
            )
    payload = {
        "model":
        request_func_input.model_name
        if request_func_input.model_name else request_func_input.model,
        "messages": [
            {
                "role": "user",
                "content": content
            },
        ],
        "temperature":
        0.0,
        "max_completion_tokens":
        request_func_input.output_len,
        "stream":
        True,
        "stream_options": {
            "include_usage": True,
        },
    }
    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)
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
    }
    if request_func_input.request_id:
        headers["x-request-id"] = request_func_input.request_id

    output = RequestFuncOutput()
    output.prompt_len = request_func_input.prompt_len

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

                    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: ")

                        if chunk != "[DONE]":
                            timestamp = time.perf_counter()
                            data = json.loads(chunk)

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

                                # Decoding phase
                                else:
                                    output.itl.append(timestamp -
                                                    most_recent_timestamp)

                                generated_text += content or ""
                            elif usage := data.get("usage"):
                                output.output_tokens = usage.get(
                                    "completion_tokens")

                            most_recent_timestamp = timestamp

                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,
    pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
    # Lazy import without PlaceholderModule to avoid vllm dep.
    import soundfile

    api_url = request_func_input.api_url
    assert api_url.endswith(("transcriptions", "translations")), (
        "OpenAI Chat Completions API URL must end with 'transcriptions' ")
    "or `translations`."

    content = [{"type": "text", "text": request_func_input.prompt}]
    payload = {
        "model":
        request_func_input.model_name
        if request_func_input.model_name else request_func_input.model,
        "temperature":
        0.0,
        "max_completion_tokens":
        request_func_input.output_len,
        "stream":
        True,
        "language":
        "en",
        # Flattened due to multipart/form-data
        "stream_include_usage":
        True,
        "stream_continuous_usage_stats":
        True,
    }
    if request_func_input.extra_body:
        payload.update(request_func_input.extra_body)
    headers = {
        "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
    }
    if request_func_input.request_id:
        headers["x-request-id"] = request_func_input.request_id

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

    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:
        form = aiohttp.FormData()
        form.add_field("file", f, content_type="audio/wav")
        for key, value in payload.items():
            form.add_field(key, str(value))

        output = RequestFuncOutput()
        output.prompt_len = request_func_input.prompt_len

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

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

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

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

                                    # Decoding phase
                                    else:
                                        output.itl.append(
                                            timestamp - most_recent_timestamp)

                                    generated_text += content or ""
                                elif usage := data.get("usage"):
                                    output.output_tokens = usage.get(
                                        "completion_tokens")

                                most_recent_timestamp = timestamp

                    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_embeddings(
    request_func_input: RequestFuncInput,
    session: aiohttp.ClientSession,
    pbar: Optional[tqdm] = None,
):
    api_url = request_func_input.api_url
    assert api_url.endswith(
        "embeddings"
    ), "OpenAI Embeddings API URL must end with 'embeddings'."

    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
    }

    payload = {
        "model": request_func_input.model,
        "input": request_func_input.prompt,
    }

    output = RequestFuncOutput()
    st = time.perf_counter()
    try:
        async with session.post(
            url=api_url,
            headers=headers,
            json=payload
        ) as response:
            if response.status == 200:
                output.latency = time.perf_counter() - st
                data = await response.json()
                output.success = True
                output.generated_text = ""
                output.prompt_len = data.get(
                    "usage", {}).get(
                    "prompt_tokens", 0)
            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


# TODO: Add more request functions for different API protocols.
ASYNC_REQUEST_FUNCS = {
    "vllm": async_request_openai_completions,
    "openai": async_request_openai_completions,
    "openai-chat": async_request_openai_chat_completions,
    "openai-audio": async_request_openai_audio,
    "openai-embeddings": async_request_openai_embeddings,
}

OPENAI_COMPATIBLE_BACKENDS = [
    k for k, v in ASYNC_REQUEST_FUNCS.items()
    if v in (async_request_openai_completions,
             async_request_openai_chat_completions)
]