"""
# Copyright (c) 2025  PaddlePaddle Authors. All Rights Reserved.
#
# 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 argparse
import json
import os
import time
from typing import Tuple

import numpy as np
import paddle
import paddle.distributed as dist
from paddle.distributed import fleet

from fastdeploy import envs
from fastdeploy.config import (
    CacheConfig,
    DecodingConfig,
    DeviceConfig,
    EarlyStopConfig,
    ErnieArchitectures,
    FDConfig,
    GraphOptimizationConfig,
    LoadConfig,
    ModelConfig,
    ParallelConfig,
    PlasAttentionConfig,
    SpeculativeConfig,
)
from fastdeploy.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
from fastdeploy.inter_communicator import EngineWorkerQueue as TaskQueue
from fastdeploy.inter_communicator import IPCSignal
from fastdeploy.model_executor.layers.quantization import parse_quant_config
from fastdeploy.platforms import current_platform
from fastdeploy.scheduler import SchedulerConfig
from fastdeploy.utils import get_logger, optional_type
from fastdeploy.worker.worker_base import WorkerBase

logger = get_logger("worker_process", "worker_process.log")


def get_worker(fd_config: FDConfig, local_rank: int, rank: int) -> WorkerBase:
    """
    get worker of different device
    """
    if fd_config.model_config.enable_logprob and not current_platform.is_cuda():
        raise NotImplementedError("Only CUDA platform supports logprob.")
    if current_platform.is_dcu():
        from fastdeploy.worker.dcu_worker import DcuWorker

        return DcuWorker(fd_config=fd_config, local_rank=local_rank, rank=rank)
    if current_platform.is_cuda():
        from fastdeploy.worker.gpu_worker import GpuWorker

        return GpuWorker(fd_config=fd_config, local_rank=local_rank, rank=rank)
    if current_platform.is_xpu():
        from fastdeploy.worker.xpu_worker import XpuWorker

        return XpuWorker(fd_config=fd_config, local_rank=local_rank, rank=rank)
    if current_platform.is_iluvatar():
        from fastdeploy.worker.iluvatar_worker import IluvatarWorker

        return IluvatarWorker(fd_config=fd_config, local_rank=local_rank, rank=rank)
    if current_platform.is_gcu():
        from fastdeploy.worker.gcu_worker import GcuWorker

        return GcuWorker(fd_config=fd_config, local_rank=local_rank, rank=rank)
    if current_platform.is_maca():
        from fastdeploy.worker.metax_worker import MetaxWorker

        return MetaxWorker(fd_config=fd_config, local_rank=local_rank, rank=rank)
    if current_platform.is_intel_hpu():
        from fastdeploy.worker.hpu_worker import HpuWorker

        return HpuWorker(fd_config=fd_config, local_rank=local_rank, rank=rank)


def init_distributed_environment(seed: int = 20) -> Tuple[int, int]:
    """Initialize Paddle Fleet and get rank of worker"""
    # Global rank
    ranks = dist.get_world_size()
    dist_strategy = fleet.DistributedStrategy()
    if ranks > 0:
        dist_strategy.hybrid_configs = {
            "dp_degree": 1,
            "mp_degree": ranks,
            "pp_degree": 1,
            "sharding_degree": 1,
        }

        # Set control in tensor parallel
        dist_strategy.tensor_parallel_configs = {"tensor_init_seed": seed}
        fleet.init(is_collective=True, strategy=dist_strategy)

        # Local rank
        local_rank = fleet.worker_index()
    else:
        local_rank = 0
    return ranks, local_rank


def update_fd_config_for_mm(fd_config: FDConfig) -> None:
    architectures = fd_config.model_config.architectures
    if fd_config.model_config.enable_mm and ErnieArchitectures.contains_ernie_arch(architectures):
        tokenizer = Ernie4_5Tokenizer.from_pretrained(
            fd_config.model_config.model,
            model_max_length=fd_config.parallel_config.max_model_len,
            padding_side="right",
            use_fast=False,
        )
        tokenizer.ignored_index = -100
        if tokenizer.pad_token is None:
            tokenizer.pad_token = tokenizer.unk_token

        fd_config.model_config.tensor_parallel_degree = fd_config.parallel_config.tensor_parallel_size
        fd_config.model_config.tensor_parallel_rank = fd_config.parallel_config.tensor_parallel_rank
        vision_config = fd_config.model_config.vision_config
        vision_config.dtype = fd_config.model_config.dtype
        # vision_config.tensor_parallel_degree = fd_config.parallel_config.tensor_parallel_size
        # vision_config.tensor_parallel_rank = fd_config.parallel_config.tensor_parallel_rank
        fd_config.model_config.im_patch_id = tokenizer.get_vocab()["<|IMAGE_PLACEHOLDER|>"]
        fd_config.model_config.think_end_id = tokenizer.get_vocab()["</think>"]
        fd_config.model_config.sequence_parallel = fd_config.parallel_config.sequence_parallel


class PaddleDisWorkerProc:
    """
    Paddle Distributed wrapper for fastdeploy.worker.Worker,
        for handling single-node multi-GPU tensor parallel.
    The wrapper internally executes an event loop that continuously executes requests
        in the task queue. Control flow is transmitted by IPC.
    """

    def __init__(self, fd_config: FDConfig, ranks: int = 1, local_rank: int = 0) -> None:
        """
        Initialize a distributed worker and task queue for single-node multi-GPU setup.
        Args:
            fd_config (FDConfig): Arguments related to inference, containing
                attributes such as weight_dtype, act_dtype, mp_size, hidden_size, head_dim,
                num_attention_heads, and ffn_hidden_size.
        """
        self.ranks = ranks
        self.local_rank = local_rank
        self.fd_config = fd_config
        self.parallel_config = fd_config.parallel_config
        self.cache_config = fd_config.cache_config
        self.scheduler_config = fd_config.scheduler_config

        # TODO(gongshaotian): Use worker factory to get worker
        self.worker = get_worker(fd_config=fd_config, local_rank=self.local_rank, rank=self.ranks)

        self.max_chips_per_node = 16 if current_platform.is_iluvatar() else 8

    def init_health_status(self) -> None:
        """
        Initialize the health status of the worker.
        Worker Status:
            worker_ready_signal:
            worker_healthy_live_signal:
            exist_task_signal:
            exist_swapped_task_signal:
            model_weights_status:
        """
        self.max_chips_per_node = 16 if current_platform.is_iluvatar() else 8
        if self.parallel_config.data_parallel_size > 1 and not envs.FD_ENABLE_MULTI_API_SERVER:
            launched_expert_service_signal_data = np.zeros(
                shape=[min(self.parallel_config.data_parallel_size, self.max_chips_per_node)], dtype=np.int32
            )
            self.launched_expert_service_signal = IPCSignal(
                name="launched_expert_service_signal",
                array=launched_expert_service_signal_data,
                dtype=np.int32,
                suffix=self.parallel_config.engine_pid,
                create=False,
            )
            while self.launched_expert_service_signal.value[self.local_rank % self.max_chips_per_node] == 0:
                pass

        # init worker_ready_signal
        array_size = min(
            self.max_chips_per_node,
            self.parallel_config.tensor_parallel_size * self.parallel_config.data_parallel_size,
        )

        workers_ready = np.zeros(shape=[array_size], dtype=np.int32)
        self.worker_ready_signal = IPCSignal(
            name="worker_ready_signal",
            array=workers_ready,
            dtype=np.int32,
            suffix=self.parallel_config.engine_pid,
            create=False,
        )
        self.worker_ready_signal.value[self.local_rank % self.max_chips_per_node] = 1
        # init worker_healthy_live_signal
        workers_alive = np.zeros(shape=[min(array_size, self.parallel_config.tensor_parallel_size)], dtype=np.int32)
        self.worker_healthy_live_signal = IPCSignal(
            name="worker_healthy_live_signal",
            array=workers_alive,
            dtype=np.int32,
            suffix=self.parallel_config.engine_worker_queue_port,
            create=False,
        )
        local_rank = self.local_rank % self.parallel_config.tensor_parallel_size
        self.worker_healthy_live_signal.value[local_rank % self.max_chips_per_node] = int(time.time())

        # init model_weights_status
        workers_model_weights = np.zeros(shape=[1], dtype=np.int32)
        self.model_weights_status = IPCSignal(
            name="model_weights_status",
            array=workers_model_weights,
            dtype=np.int32,
            suffix=self.parallel_config.engine_worker_queue_port,
            create=False,
        )

        # init exist_task_signal
        workers_exist_task = np.zeros([1], dtype=np.int32)
        self.exist_task_signal = IPCSignal(
            name="exist_task_signal",
            array=workers_exist_task,
            dtype=np.int32,
            suffix=self.parallel_config.engine_worker_queue_port,
            create=False,
        )

        # init exist_swapped_task_signal
        workers_swapped_task = np.zeros(shape=[1], dtype=np.int32)
        self.exist_swapped_task_signal = IPCSignal(
            name="exist_swapped_task_signal",
            array=workers_swapped_task,
            dtype=np.int32,
            suffix=self.parallel_config.engine_worker_queue_port,
            create=False,
        )

        # init exist_prefill_task_signal
        exist_prefill_task_signal_data = np.zeros([1], dtype=np.int32)
        self.exist_prefill_task_signal = IPCSignal(
            name="exist_prefill_task_signal",
            array=exist_prefill_task_signal_data,
            dtype=np.int32,
            suffix=self.parallel_config.engine_worker_queue_port,
            create=False,
        )

    def _broadcast_model_weights_signal(self, src: int, group) -> int:
        model_weights_signal_tensor = paddle.full(shape=[1], fill_value=self.model_weights_signal[0], dtype="int32")
        paddle.distributed.broadcast(model_weights_signal_tensor, src=src, group=group)
        return model_weights_signal_tensor.item()

    def _tp_barrier_wait(self):
        if current_platform.is_xpu():
            self.task_queue.worker_process_tp_barrier.wait()
        else:
            paddle.distributed.barrier(self.parallel_config.tp_group)

    def event_loop_normal(self) -> None:
        """Main event loop for Paddle Distributed Workers.
        TODO(gongshaotian): support remote calling of functions that control worker.
        """

        # Currently, only support single node
        self.nnode = int((self.parallel_config.tensor_parallel_size + 7) // 8)
        req_ids = []
        num_running_requests = 0
        local_rank = self.local_rank % self.parallel_config.tensor_parallel_size
        self.model_weights_signal = np.zeros([1], dtype=np.int32)
        while True:
            if local_rank == 0:
                if self.model_weights_status.value[0] != 0:
                    self.model_weights_signal[0] = int(self.model_weights_status.value[0])
                if self.fd_config.load_config.dynamic_load_weight and self.parallel_config.enable_expert_parallel:
                    self.model_weights_signal[0] = self._broadcast_model_weights_signal(
                        src=0, group=self.parallel_config.ep_group
                    )
            if self.fd_config.load_config.dynamic_load_weight and self.parallel_config.tensor_parallel_size > 1:
                self.model_weights_signal[0] = self._broadcast_model_weights_signal(
                    src=0, group=self.parallel_config.tp_group
                )

            self.insert_step = False
            req_dicts = None
            local_rank = self.local_rank % self.parallel_config.tensor_parallel_size
            self.worker_healthy_live_signal.value[local_rank % self.max_chips_per_node] = int(time.time())

            # The first worker detects whether there are tasks in the task queue
            if local_rank == 0:
                if self.task_queue.num_tasks() > 0:
                    # VL only support 1 batch to prefill
                    if envs.ENABLE_V1_KVCACHE_SCHEDULER or not (
                        self.fd_config.model_config.enable_mm and self.worker.exist_prefill()
                    ):
                        if self.nnode > 1 and self.parallel_config.tensor_parallel_size > self.max_chips_per_node:
                            self.task_queue.read_finish_flag.set(1)
                        else:
                            self.exist_task_signal.value[0] = 1

            if self.parallel_config.tensor_parallel_size > 1:
                # Synchronize the signal for other workers
                self._tp_barrier_wait()

            if self.fd_config.load_config.dynamic_load_weight:
                if self.parallel_config.enable_expert_parallel:
                    paddle.distributed.barrier(self.parallel_config.ep_group)
                else:
                    paddle.distributed.barrier(self.parallel_config.tp_group)
                if self.model_weights_signal[0] != 0:
                    logger.info(
                        f"Rank: {self.local_rank} to update or clear parameters, signal is {self.model_weights_signal[0]}, [-1:clear, 1:update]"
                    )
                    from fastdeploy.rl.dynamic_weight_manager import (
                        DynamicWeightManager,
                    )

                    self.model_weights_status.value[0] = self.model_weights_signal[0]
                    DynamicWeightManager.check_model_weights_status(
                        self.model_weights_status,
                        # model_weights_signal
                        self.worker.model_runner,
                        self.parallel_config.engine_worker_queue_port,
                    )
                    self.model_weights_signal[0] = 0
                    logger.info(f"Rank: {self.local_rank} has updated or cleared parameters.")

            if self.exist_task_signal.value[0] == 1 or self.task_queue.read_finish_flag.get() == 1:
                logger.info(f"Rank: {self.local_rank} Detected new requests.")
                self.insert_step = True

                tasks, read_finish = self.task_queue.get_tasks()
                if read_finish:
                    # Ensure that every worker get the task
                    self.exist_task_signal.value[0] = 0
                    self.task_queue.read_finish_flag.set(0)

                req_dicts = []
                for req_dict, bsz in tasks:
                    num_running_requests = int(bsz)
                    req_dicts.extend(req_dict)

                req_ids = [req.request_id for req in req_dicts]
                logger.info(
                    f"Rank: {self.local_rank}, num_running_requests: {num_running_requests}, "
                    f"num_insert_requests: {len(req_dicts)}, req_ids: {req_ids}"
                )

                # Process prefill inputs
                self.worker.preprocess_new_task(req_dicts, num_running_requests)

            if (not self.parallel_config.use_ep) and (not self.worker.model_runner.not_need_stop()):
                if self.ranks > 1:
                    self._tp_barrier_wait()

                time.sleep(0.001)
                continue

            # Execute model to generate token. The generated token will be written to the buffer.
            # These generated tokens can be obtained through get_output op.
            self.worker.execute_model(req_dicts, num_running_requests)
            self.exist_prefill_task_signal.value[0] = self.worker.exist_prefill()

    def initialize_kv_cache(self) -> None:
        """Profiles the peak memory usage of the model to determine how many
        KV blocks may be allocated without OOMs.

        The engine will first conduct a profiling of the existing memory usage.
        Then, it calculate the maximum possible number of GPU and CPU blocks
        that can be allocated with the remaining free memory.

        .. tip::
            You may limit the usage of GPU memory
            by adjusting the `gpu_memory_utilization` parameter.
        """
        if self.fd_config.parallel_config.do_profile:
            # 1. Get available memory(bytes)
            available_kv_cache_memory = self.worker.determine_available_memory()
            logger.info(f"------- available_kv_cache_memory:{available_kv_cache_memory / 1024**3} GB --------")

            # 2. Calculate the appropriate number of blocks
            model_block_memory_used = self.worker.cal_theortical_kvcache()
            num_blocks_local = int(available_kv_cache_memory // model_block_memory_used)
            # NOTE(liuzichang): Too many block will lead to illegal memory access
            # We will develop dynamic limits in future.
            if num_blocks_local > 40000:
                logger.info(f"------- Reset num_blocks_local {num_blocks_local} to 40000")
                num_blocks_local = min(40000, num_blocks_local)
            logger.info(f"------- model_block_memory_used:{model_block_memory_used / 1024**3} GB --------")
            logger.info(f"------- num_blocks_local:{num_blocks_local} --------")

            if num_blocks_local <= 0:
                raise ValueError(
                    "The total number of blocks cannot be less than zero."
                    "Please increase gpu_memory_utilization"
                    "Or decrease max_num_batched_tokens(max model length) "
                )

            if self.ranks > 1:
                num_blocks_local = paddle.full(shape=[1], fill_value=num_blocks_local, dtype="int32")
                dist.all_reduce(num_blocks_local, op=dist.ReduceOp.MIN)
                num_blocks_local = num_blocks_local.item()

            if self.local_rank % self.max_chips_per_node == 0:
                # 3. Send IPCSignal
                get_profile_block_num = np.zeros(shape=[1], dtype=np.int32)
                self.get_profile_block_num_signal = IPCSignal(
                    name="get_profile_block_num",
                    array=get_profile_block_num,
                    dtype=np.int32,
                    suffix=self.parallel_config.engine_pid,
                    create=False,
                )
                self.get_profile_block_num_signal.value[0] = num_blocks_local
        else:
            num_blocks_local = self.fd_config.parallel_config.total_block_num
        logger.info(f"------- num_blocks_global: {num_blocks_local} --------")
        # wait engine launch cache_manager
        if self.cache_config.enable_prefix_caching or self.scheduler_config.splitwise_role != "mixed":
            launched_cache_manager_signal_data = np.zeros([1], dtype=np.int32)
            self.launched_cache_manager_signal = IPCSignal(
                name="launched_cache_manager_signal",
                array=launched_cache_manager_signal_data,
                dtype=np.int32,
                suffix=self.parallel_config.engine_pid,
                create=False,
            )
            while np.any(self.launched_cache_manager_signal.value[0] <= 0):
                time.sleep(0.01)
        # 4. init kv_cache with accurate num_blocks
        self.worker.initialize_cache(num_gpu_blocks=num_blocks_local)

    def graph_optimize_and_warm_up_model(self) -> None:
        self.worker.graph_optimize_and_warm_up_model()
        # reset cache_messager prefilled_step signal
        if self.scheduler_config.splitwise_role == "prefill":
            dp_rank_id = (
                self.local_rank
                + self.parallel_config.local_data_parallel_id * self.parallel_config.tensor_parallel_size
            )
            gpu_id = self.worker.model_runner.device_id
            prefilled_step_name = f"splitwise_complete_prefilled_step_{dp_rank_id}"
            prefilled_step_idx_data = np.zeros(shape=[1], dtype=np.int32)
            step_shm_value = IPCSignal(
                name=prefilled_step_name,
                array=prefilled_step_idx_data,
                dtype=np.int32,
                suffix=gpu_id,
                create=False,
            )
            step_shm_value.value[0] = -1

    def init_device(self) -> None:
        """Initialize device and Construct model runner"""
        self.worker.init_device()

    def start_task_queue_service(self):
        # Initialize task queue
        task_address = (
            self.parallel_config.pod_ip,
            self.parallel_config.engine_worker_queue_port,
        )
        logger.info(f"connect task queue address {task_address}")
        self.task_queue = TaskQueue(
            address=task_address,
            is_server=False,
            num_client=self.parallel_config.tensor_parallel_size,
            client_id=self.parallel_config.tensor_parallel_rank,
            local_data_parallel_id=self.parallel_config.data_parallel_rank,
        )

    def load_model(self) -> None:
        """Load weights and create model"""

        self.worker.load_model()
        loaded_model_signal_data = np.zeros(shape=[1], dtype=np.int32)
        self.loaded_model_signal = IPCSignal(
            name="loaded_model_signal",
            array=loaded_model_signal_data,
            dtype=np.int32,
            suffix=self.parallel_config.engine_pid,
            create=False,
        )
        if self.ranks > 1:
            paddle.distributed.barrier()
        self.loaded_model_signal.value[0] = 1


def parse_args():
    """
    Parse args from command line
    """
    parser = argparse.ArgumentParser("FastDeploy LLM Inference")
    parser.add_argument(
        "-m",
        "--model",
        type=str,
        default="./output",
        help="model dir",
    )
    parser.add_argument("-mbs", "--max_num_seqs", type=int, default=34, help="max batch size")
    parser.add_argument("--total_block_num", type=int, default=2000)
    parser.add_argument("--block_size", type=int, default=64)
    parser.add_argument("--pod_ip", type=str, default="127.0.0.1")
    parser.add_argument("--engine_worker_queue_port", type=str, default="9923")
    parser.add_argument("--max_model_len", type=int, default=3072, help="max model len")
    parser.add_argument("--device_ids", type=str, default="0", help="cuda visible devices")
    parser.add_argument("--dtype", type=str, default="bfloat16", help="input dtype")
    parser.add_argument("--enc_dec_block_num", type=int, default=1, help="encoder's decoder num")
    parser.add_argument(
        "--kv_cache_ratio",
        type=float,
        default=0.7,
        help="kv cache ratio for input",
    )
    parser.add_argument("--first_token_id", type=int, default=1, help="first token id")
    parser.add_argument(
        "--gpu_memory_utilization",
        type=float,
        default=0.9,
        help="gpu memory utilization",
    )
    parser.add_argument("--engine_pid", type=int, default=None, help="Process ID of engine")
    parser.add_argument("--do_profile", action="store_true", help="do profile or not")
    parser.add_argument("--pad_token_id", type=int, default=-1, help="pad token id")
    parser.add_argument("--eos_tokens_lens", type=int, default=2, help="eos token lens")
    parser.add_argument(
        "--enable_chunked_prefill",
        action="store_true",
        help="enable chunked prefill",
    )
    parser.add_argument(
        "--speculative_config",
        type=json.loads,
        default=None,
        help="Configuration of SpeculativeConfig.",
    )
    parser.add_argument(
        "--max_num_batched_tokens",
        type=int,
        default=2048,
        help="max num batched tokens",
    )

    parser.add_argument(
        "--enable_prefix_caching",
        action="store_true",
        help="enable prefix cache",
    )
    parser.add_argument(
        "--disable_custom_all_reduce",
        action="store_true",
        help="enable custom all-reduce",
    )
    parser.add_argument("--splitwise_role", type=str, default="mixed", help="splitwise role")
    parser.add_argument(
        "--tensor_parallel_size",
        type=int,
        default=1,
        help="tensor parallel size",
    )
    parser.add_argument(
        "--expert_parallel_size",
        type=int,
        default=1,
        help="expert parallel size",
    )
    parser.add_argument(
        "--data_parallel_size",
        type=int,
        default=1,
        help="data parallel size",
    )
    parser.add_argument(
        "--enable_expert_parallel",
        action="store_true",
        help="enable expert parallel",
    )
    parser.add_argument("--ori_vocab_size", type=int, default=None)
    parser.add_argument("--think_end_id", type=int, default=-1)

    parser.add_argument(
        "--quantization",
        type=json.loads,
        default=None,
        help="Quantization name for the model, currently support "
        "'wint4', 'wint8',"
        "default is None. The priority of this configuration "
        "is lower than that of the config file. "
        "More complex quantization methods need to be configured via the config file.",
    )
    parser.add_argument(
        "--graph_optimization_config",
        type=json.loads,
        default=None,
        help="Configuration of Graph optimization backend.",
    )
    parser.add_argument(
        "--plas_attention_config",
        type=json.loads,
        default=None,
        help="Configation of plas attention.",
    )
    parser.add_argument(
        "--guided_decoding_backend",
        type=str,
        default="off",
        help="guided decoding backend",
    )
    parser.add_argument(
        "--disable_any_whitespace",
        action="store_false",
        help="Disable any whitespace for guided decoding.",
    )
    parser.add_argument(
        "--dynamic_load_weight",
        action="store_true",
        help="Enable dynamic weight loading strategy",
    )
    parser.add_argument(
        "--load_strategy",
        type=str,
        choices=["ipc", "ipc_snapshot", "meta", "normal"],
        default="ipc_snapshot",
        help="Weight loading method when dynamic loading is enabled: "
        "'ipc': real-time IPC streaming with automatic resharding, "
        "'ipc_snapshot': load from disk snapshot of IPC weights.",
    )
    parser.add_argument(
        "--enable_logprob",
        action="store_true",
        help="Enable output of token-level log probabilities.",
    )
    parser.add_argument(
        "--reasoning_parser",
        type=str,
        default=None,
        help="Flag specifies the reasoning parser to use for extracting reasoning content from the model output",
    )
    parser.add_argument(
        "--early_stop_config",
        type=json.loads,
        default=None,
        help="Configuration of early stop.",
    )

    parser.add_argument(
        "--load_choices",
        type=str,
        default="default",
        help="The format of the model weights to load. default/new_loader.",
    )

    parser.add_argument(
        "--ips",
        type=str,
        default=None,
        help="The ips of multinode deployment.",
    )

    parser.add_argument(
        "--lm_head_fp32",
        action="store_true",
        help="Flag to specify dtype of lm_head as FP32",
    )

    parser.add_argument(
        "--cache-transfer-protocol",
        type=str,
        default="ipc",
        help="support protocol list, comma separated, default is ipc",
    )
    parser.add_argument(
        "--runner",
        type=str,
        default="auto",
        help="The type of model runner to use.Each FD instance only supports one model runner.even if the same model can be used for multiple types.",
    )

    parser.add_argument(
        "--convert",
        type=str,
        default="auto",
        help="Convert the model using adapters. The most common use case is to adapt a text generation model to be used for pooling tasks.",
    )

    parser.add_argument(
        "--override-pooler-config",
        type=optional_type(json.loads),
        default=None,
        help="Override configuration for the pooler.",
    )

    args = parser.parse_args()
    return args


def initialize_fd_config(args, ranks: int = 1, local_rank: int = 0) -> FDConfig:
    """Initialize FDConfig from either RolloutModelConfig or argparse.Namespace

    Args:
        config: Configuration object containing all parameters (either RolloutModelConfig or argparse.Namespace)

    Returns:
        FDConfig: Initialized FastDeploy configuration object
    """
    # RL rollout
    paddle.set_default_dtype(args.dtype)
    model_config = ModelConfig(vars(args))
    device_config = DeviceConfig(vars(args))
    decoding_config = DecodingConfig(vars(args))
    speculative_config = SpeculativeConfig(args.speculative_config)
    parallel_config = ParallelConfig(vars(args))
    cache_config = CacheConfig(vars(args))
    scheduler_config = SchedulerConfig(vars(args))
    parallel_config.tensor_parallel_rank = local_rank % parallel_config.tensor_parallel_size
    parallel_config.data_parallel_rank = local_rank // parallel_config.tensor_parallel_size
    # config for EP
    if parallel_config.expert_parallel_size > 1:
        expert_parallel_rank = int(local_rank % parallel_config.expert_parallel_size)
        if isinstance(model_config.moe_num_experts, list):
            num_experts = model_config.moe_num_experts[0]
        else:
            num_experts = model_config.moe_num_experts

        num_experts_per_rank = num_experts // parallel_config.expert_parallel_size
        num_experts_start_offset = expert_parallel_rank * num_experts_per_rank
        max_chips_per_node = 16 if current_platform.is_iluvatar() else 8
        parallel_config.local_data_parallel_id = expert_parallel_rank % max_chips_per_node

        parallel_config.expert_parallel_rank = expert_parallel_rank
        parallel_config.num_experts_per_rank = num_experts_per_rank
        parallel_config.num_experts_start_offset = num_experts_start_offset

    if args.load_strategy != "meta":
        parallel_config.engine_worker_queue_port = parallel_config.engine_worker_queue_port[
            parallel_config.local_data_parallel_id
        ]
    parallel_config.set_communicate_group()

    load_config = LoadConfig(vars(args))

    graph_opt_config = GraphOptimizationConfig(args.graph_optimization_config)

    plas_attention_config = PlasAttentionConfig(args.plas_attention_config)

    early_stop_config = EarlyStopConfig(args.early_stop_config)

    # Note(tangbinhan): used for load_checkpoint
    model_config.pretrained_config.tensor_parallel_rank = parallel_config.tensor_parallel_rank
    model_config.pretrained_config.tensor_parallel_degree = parallel_config.tensor_parallel_size
    model_config.pretrained_config.is_mtp = False
    model_config.pretrained_config.head_dim = model_config.head_dim

    logger.info(f"parallel_config.use_ep {parallel_config.use_ep}")
    logger.info(f"parallel_config.tensor_parallel_size {parallel_config.tensor_parallel_size}")
    logger.info(f"parallel_config.tensor_parallel_rank {parallel_config.tensor_parallel_rank}")
    logger.info(f"parallel_config.engine_worker_queue_port {parallel_config.engine_worker_queue_port}")

    if getattr(model_config, "num_hidden_layers", None) is None:
        raise ValueError("num_hidden_layers is None")

    quant_config = parse_quant_config(
        args,
        model_config,
        is_ernie=ErnieArchitectures.contains_ernie_arch(model_config.architectures),
        is_v1_loader=load_config.load_choices == "default_v1",
    )

    # Log quantization info
    logger.info("===========quantization_config==============")
    if quant_config is not None:
        if model_config.is_quantized:
            logger.info("Model Status: Offline Quantized (pre-quantized weights loaded)")
        else:
            logger.info("Model Status: Original (will apply online quantization)")

        logger.info(f"{model_config.quantization_config}")
    else:
        logger.info("No quantization config found and use original weight and act dtype.")

    logger.info(f"- Dynamic load weight: {load_config.dynamic_load_weight}")
    logger.info(f"- Load strategy: {load_config.load_strategy}")

    if (
        args.speculative_config is not None
        and ("method" in args.speculative_config)
        and (args.speculative_config["method"] is not None)
    ):
        logger.info("Set ENABLE_V1_KVCACHE_SCHEDULER to 0 due to not support speculative decoding now.")
        envs.ENABLE_V1_KVCACHE_SCHEDULER = 0
    if args.splitwise_role != "mixed" and args.cache_transfer_protocol != "rdma":
        envs.ENABLE_V1_KVCACHE_SCHEDULER = 0
    if not current_platform.is_cuda() and not current_platform.is_xpu():
        logger.info("Set ENABLE_V1_KVCACHE_SCHEDULER to 0 due to not supported.")
        envs.ENABLE_V1_KVCACHE_SCHEDULER = 0
    if parallel_config.guided_decoding_backend != "off":
        logger.info("Set ENABLE_V1_KVCACHE_SCHEDULER to 0 due to not supported guided_decoding.")
        envs.ENABLE_V1_KVCACHE_SCHEDULER = 0

    if envs.ENABLE_V1_KVCACHE_SCHEDULER and args.splitwise_role == "prefill":
        os.environ["PREFILL_NODE_ONE_STEP_STOP_V1"] = "1"

    fd_config = FDConfig(
        model_config=model_config,
        parallel_config=parallel_config,
        speculative_config=speculative_config,
        device_config=device_config,
        load_config=load_config,
        decoding_config=decoding_config,
        quant_config=quant_config,
        graph_opt_config=graph_opt_config,
        early_stop_config=early_stop_config,
        cache_config=cache_config,
        scheduler_config=scheduler_config,
        ips=args.ips,
        plas_attention_config=plas_attention_config,
    )
    update_fd_config_for_mm(fd_config)

    return fd_config


def run_worker_proc() -> None:
    """
    start worker process
    """
    # Get args form Engine
    args = parse_args()

    ranks, local_rank = init_distributed_environment()

    # Get fd_config
    fd_config = initialize_fd_config(args, ranks, local_rank)

    # Create worker process
    if current_platform.is_iluvatar():
        from fastdeploy.worker.iluvatar_worker import IluvatarPaddleDisWorkerProc

        worker_proc = IluvatarPaddleDisWorkerProc(fd_config, ranks, local_rank)
    else:
        worker_proc = PaddleDisWorkerProc(fd_config, ranks, local_rank)

    # Initialize device and create model runner
    worker_proc.init_device()

    # Load model
    worker_proc.load_model()
    # Initialize KV Cache
    worker_proc.initialize_kv_cache()

    # Trigger CUDAGraph capture
    worker_proc.graph_optimize_and_warm_up_model()

    # Initialize health status
    worker_proc.init_health_status()

    worker_proc.start_task_queue_service()

    # Start event loop
    worker_proc.event_loop_normal()


if __name__ == "__main__":
    run_worker_proc()