# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# 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 asyncio
import heapq
import importlib
import logging
import os
import socket
import threading
from abc import ABC, abstractmethod
from contextlib import asynccontextmanager
from typing import Any, Callable, Dict, List, Tuple, Type
from uuid import uuid4

import aiohttp
import fastapi
import ray
import uvicorn
from cachetools import LRUCache
from omegaconf import DictConfig
from openai import AsyncOpenAI
from openai.types.chat.chat_completion import ChatCompletion
from starlette.requests import Request

from verl.protocol import DataProto
from verl.single_controller.ray.base import RayWorkerGroup
from verl.utils import hf_tokenizer
from verl.utils.fs import copy_to_local

logger = logging.getLogger(__file__)


def _get_free_port():
    with socket.socket() as sock:
        sock.bind(("", 0))
        return sock.getsockname()[1]


class AsyncServerBase(ABC):
    """Base class for AsyncServer."""

    def __init__(self):
        self.address = ray._private.services.get_node_ip_address()
        self.port = None
        self.server_ready = asyncio.Event()
        asyncio.create_task(self._start_fastapi_server())

    async def _start_fastapi_server(self):
        @asynccontextmanager
        async def lifespan(app: fastapi.FastAPI):
            print("FastAPI startup")
            self.server_ready.set()
            yield

            # There's no way to gracefully restart uvicorn server if port is already in use,
            # so we exit the process directly and let AsyncLLMServerManager restart it.
            print("FastAPI shutdown, maybe address already in use, exit process immediately.")
            os._exit(-1)

        app = fastapi.FastAPI(lifespan=lifespan)
        app.router.add_api_route("/v1/chat/completions", self.chat_completion, methods=["POST"])

        self.port = _get_free_port()
        config = uvicorn.Config(app, host=["::", "0.0.0.0"], port=self.port, log_level="warning")
        server = uvicorn.Server(config)
        await server.serve()

    async def get_server_address(self) -> Tuple[str, int]:
        """Get FastAPI server address."""
        await self.server_ready.wait()
        return f"{self.address}:{self.port}"

    @abstractmethod
    async def chat_completion(self, raw_request: Request):
        """OpenAI chat completion API.

        API reference: https://platform.openai.com/docs/api-reference/chat/create
        """
        raise NotImplementedError

    @abstractmethod
    async def init_engine(self):
        """Init async LLM engine."""
        raise NotImplementedError

    @abstractmethod
    async def wake_up(self):
        """Wake up engine to load model weights and build kv cache."""
        raise NotImplementedError

    @abstractmethod
    async def sleep(self):
        """Sleep engine to offload model weights and discard kv cache."""
        raise NotImplementedError


class ChatCompletionScheduler:
    def __init__(
        self,
        config: DictConfig,
        model_path: str,
        server_addresses: List[str],
        max_cache_size: int = 10000,
    ):
        """
        Args:
            config: DictConfig, rollout config.
            model_path: str, model path.
            server_addresses: List[str], server addresses.
            max_cache_size: int, max cache size of request_id to address mapping.
        """
        self.config = config
        self.model_name = "/".join(model_path.split("/")[-2:])
        local_path = copy_to_local(model_path)
        self.tokenizer = hf_tokenizer(local_path, trust_remote_code=True)

        # Least requests load balancing
        self.weighted_addresses = [[0, address] for address in server_addresses]
        heapq.heapify(self.weighted_addresses)

        # LRU cache to map request_id to address
        self.request_id_to_address = LRUCache(maxsize=max_cache_size)

    async def submit_chat_completions(
        self,
        callback: Callable[[ChatCompletion, Dict[str, Any], Exception], None],
        callback_additional_info: Dict[str, Any],
        **chat_complete_request,
    ):
        """
        Submit a chat completion request to the server with the least number of requests.

        Args:
            callback: Callable[[ChatCompletion, Dict[str, Any], Exception], None], async callback function
                to handle the response. The callback function should have the following signature:

                ```python
                async def callback(completions: ChatCompletion, info: Dict[str, Any], exception: Exception):
                    ...
                ```
                - completions: chat completion response from server.
                - info: user provided `callback_additional_info`.
                - exception: exception raise from OpenAI client if request failed, otherwise None.

                **CAUTION**: the callback function must be async and non-blocking, if you have any blocking operation,
                please move to seperate thread or process pool to avoid blocking the event loop.

            callback_additional_info: Dict[str, Any], additional info to pass to the callback function.

            **chat_complete_request: dict, request parameters same as OpenAI AsyncCompletions.create.
                OpenAI API reference: https://platform.openai.com/docs/api-reference/chat/create
        """
        if "extra_headers" not in chat_complete_request:
            chat_complete_request["extra_headers"] = {}

        extra_headers = chat_complete_request["extra_headers"]
        request_id = extra_headers.get("x-request-id", None)
        if request_id:
            if request_id.startswith("chatcmpl-"):
                request_id = request_id[len("chatcmpl-") :]
                extra_headers["x-request-id"] = request_id

            address = self.request_id_to_address[request_id]
        else:
            address = self.weighted_addresses[0][1]
            self.weighted_addresses[0][0] += 1
            heapq.heapreplace(self.weighted_addresses, self.weighted_addresses[0])

            request_id = uuid4().hex
            self.request_id_to_address[request_id] = address
            chat_complete_request["extra_headers"]["x-request-id"] = request_id

        completions, exception = None, None
        try:
            # TODO: OpenAI client uses httpx, seems to have performance issue in high concurrency requests.
            completions = await self._chat_completions_openai(address, **chat_complete_request)
        except Exception as e:
            # Let user handle the exception
            exception = e

        await callback(completions, callback_additional_info, exception)

    async def _chat_completions_openai(self, address: str, **chat_complete_request) -> ChatCompletion:
        client = AsyncOpenAI(
            base_url=f"http://{address}/v1",
            api_key="token-abc123",
        )
        return await client.chat.completions.create(**chat_complete_request)

    async def _chat_completions_aiohttp(self, address: str, **chat_complete_request) -> ChatCompletion:
        try:
            session = aiohttp.ClientSession()
            async with session.post(
                url=f"http://{address}/v1/chat/completions",
                headers={"Authorization": "Bearer token-abc123"},
                json=chat_complete_request,
            ) as resp:
                data = await resp.json()
                return ChatCompletion(**data)
        finally:
            await session.close()

    async def generate_sequences(self, prompts: DataProto, **sampling_params) -> DataProto:
        raise NotImplementedError


class AsyncLLMServerManager:
    """AsyncLLMServerManager manage a group of vllm instances, i.e AsyncvLLMServer."""

    def __init__(self, config: DictConfig, worker_group: RayWorkerGroup):
        """Initialize AsyncLLMServerManager.

        Args:
            config: DictConfig, actor_rollout_ref config.
            worker_group: RayWorkerGroup, worker group of AsyncActorRolloutRefWorker.
        """
        self.config = config
        self.worker_group = worker_group

        self.rollout_tp_size = self.config.rollout.tensor_model_parallel_size
        self.rollout_dp_size = self.worker_group.world_size // self.rollout_tp_size

        register_center = ray.get_actor(f"{self.worker_group.name_prefix}_register_center")
        workers_info = ray.get(register_center.get_worker_info.remote())
        assert len(workers_info) == self.worker_group.world_size

        self.async_llm_servers = [None] * self.rollout_dp_size
        self.server_addresses = [None] * self.rollout_dp_size

        server_class = async_server_class(
            rollout_backend=self.config.rollout.name,
        )

        # Start all server instances, restart if address already in use.
        unready_dp_ranks = set(range(self.rollout_dp_size))
        while len(unready_dp_ranks) > 0:
            workers = {
                rollout_dp_rank: server_class.options(
                    # make sure AsyncvLLMServer colocates with its corresponding workers
                    scheduling_strategy=ray.util.scheduling_strategies.NodeAffinitySchedulingStrategy(
                        node_id=workers_info[rollout_dp_rank * self.rollout_tp_size],
                        soft=False,
                    ),
                    name=f"async_llm_worker_{rollout_dp_rank}",
                ).remote(config, self.rollout_dp_size, rollout_dp_rank, self.worker_group.name_prefix)
                for rollout_dp_rank in unready_dp_ranks
            }

            for rollout_dp_rank, worker in workers.items():
                try:
                    address = ray.get(worker.get_server_address.remote())
                    self.server_addresses[rollout_dp_rank] = address
                    self.async_llm_servers[rollout_dp_rank] = worker
                    unready_dp_ranks.remove(rollout_dp_rank)
                except Exception:
                    ray.kill(worker)
                    print(f"worker {rollout_dp_rank} failed, maybe address already in use, restarting...")

        # All server instances are ready, init AsyncLLM engine.
        ray.get([worker.init_engine.remote() for worker in self.async_llm_servers])

        # Init user provided chat scheduler in sperate thread.
        self.chat_scheduler: ChatCompletionScheduler = None
        self.chat_scheduler_loop = None
        self.chat_scheduler_ready = threading.Event()
        self.chat_scheduler_thread = threading.Thread(target=self._init_chat_scheduler, daemon=True)
        self.chat_scheduler_thread.start()
        self.chat_scheduler_ready.wait()

    def _init_chat_scheduler(self):
        self.chat_scheduler_loop = asyncio.new_event_loop()
        asyncio.set_event_loop(self.chat_scheduler_loop)

        module_path, class_name = self.config.rollout.chat_scheduler.rsplit(".", 1)
        module = importlib.import_module(module_path)
        scheduler_cls = getattr(module, class_name)
        self.chat_scheduler = scheduler_cls(
            config=self.config.rollout,
            model_path=self.config.model.path,
            server_addresses=self.server_addresses,
        )

        self.chat_scheduler_ready.set()
        self.chat_scheduler_loop.run_forever()

    def wake_up(self):
        """Wake up all vllm instances."""
        ray.get([worker.wake_up.remote() for worker in self.async_llm_servers])

    def sleep(self):
        """Sleep all vllm instances."""
        ray.get([worker.sleep.remote() for worker in self.async_llm_servers])

    def submit_chat_completions(
        self,
        callback: Callable[[ChatCompletion, Dict[str, Any], Exception], None],
        callback_additional_info: Dict[str, Any],
        **chat_complete_request,
    ):
        """Submit a chat completion request to chat scheduler and wait until it is done.
        To submit multiple requests in parallel, please use `generate_sequences` instead.

        Args: same as ChatCompletionScheduler.submit_chat_completions.
        """
        assert self.chat_scheduler is not None, "chat scheduler is not initialized."
        future = asyncio.run_coroutine_threadsafe(
            self.chat_scheduler.submit_chat_completions(
                callback=callback,
                callback_additional_info=callback_additional_info,
                **chat_complete_request,
            ),
            self.chat_scheduler_loop,
        )
        future.result()

    def generate_sequences(self, prompts: DataProto, **sampling_params) -> DataProto:
        """Generate multiple sequences in parallel via chat scheduler."""
        assert self.chat_scheduler is not None, "chat scheduler is not initialized."

        future = asyncio.run_coroutine_threadsafe(self.chat_scheduler.generate_sequences(prompts, **sampling_params), self.chat_scheduler_loop)
        return future.result()


def async_server_class(rollout_backend: str) -> Type[AsyncServerBase]:
    """Get async server class.

    Args:
        rollout_backend: str, rollout backend, should be "vllm" or "sglang".

    Returns:
        Type[AsyncServerBase]: async server class.
    """
    if rollout_backend == "vllm":
        from verl.workers.rollout.vllm_rollout.vllm_async_server import AsyncvLLMServer

        return AsyncvLLMServer
    elif rollout_backend == "sglang":
        raise NotImplementedError
    else:
        raise NotImplementedError
