FastDeploy/fastdeploy/worker/worker_process.py

"""
# 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 time
from typing import List

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

from fastdeploy.config import (DecodingConfig, DeviceConfig, FDConfig,
                               GraphOptimizationConfig, LoadConfig,
                               ModelConfig, ParallelConfig, SpeculativeConfig)
from fastdeploy.inter_communicator import EngineWorkerQueue as TaskQueue
from fastdeploy.inter_communicator import IPCSignal
from fastdeploy.model_executor.layers.quantization import \
    get_quantization_config
from fastdeploy.platforms import current_platform
from fastdeploy.utils import get_logger, none_or_str
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)

def init_distributed_environment(seed: int = 20) -> List[int]:
    """ Initialize Paddle Fleet and get rank of worker """
    # Global rank
    ranks = dist.get_world_size()
    dist_strategy = fleet.DistributedStrategy()

    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()

    return ranks, local_rank

class PaddleDisWorkerProc():
    """
    Paddle Distrubuted wrapper for fastdeploy.worker.Worker,
        for handling single-node multi-GPU tensor parallel.
    The wrapper internally executea 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

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

        # Initialize task queue
        task_address = (self.parallel_config.pod_ip,
                        self.parallel_config.engine_worker_queue_port)

        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.expert_parallel_rank)

    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:
        """
        # init worker_ready_signal
        max_chips_per_node = 16 if current_platform.is_iluvatar() else 8
        array_size = min(
            max_chips_per_node, self.parallel_config.tensor_parallel_size *
            self.parallel_config.expert_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 %
                                       max_chips_per_node] = 1

        # init worker_healthy_live_signal
        workers_alive = np.zeros(shape=[self.ranks], 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_pid,
            create=False)
        self.worker_healthy_live_signal.value[self.local_rank % 8] = 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_pid,
            create=False)

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

        # init exist_swapped_task_signal
        workers_swapped_task = np.zeros(
            shape=[self.parallel_config.expert_parallel_size],
            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_pid,
            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_pid,
            create=False)

    def event_loop_ep(self) -> None:
        """
        Tmp loop function for ep utill DP is supported
        """
        while True:
            self.worker_healthy_live_signal.value[self.local_rank] = int(
                time.time())

            if self.fd_config.parallel_config.tensor_parallel_rank == 0 and self.task_queue.num_tasks(
            ) > 0:
                tasks, read_finish = self.task_queue.get_tasks()

                req_dicts = []
                for req_dict, bsz in tasks:
                    num_running_requests = int(bsz)
                    req_dicts.extend(req_dict)
                logger.info(f"Rank: {self.local_rank}, num_running_requests: {num_running_requests}, " \
                            f"num_insert_requests: {len(req_dicts)}")
                # Process prefill inputs
                self.worker.preprocess_new_task(req_dicts)

            # 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()

    def event_loop_normal(self) -> None:
        """ Main event loop for Paddle Distrubuted 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)
        mp_num_per_node = self.parallel_config.tensor_parallel_size // self.nnode
        req_ids = []
        while True:
            if self.local_rank == 0:
                if self.model_weights_status.value[0] != 0:
                    self.exist_task_signal.value[0] = 2
                else:
                    self.exist_task_signal.value[0] = 0

            if self.parallel_config.tensor_parallel_size > 1:
                # Synchronize before updating weights
                paddle.distributed.barrier()

            self.insert_step = False
            self.worker_healthy_live_signal.value[self.local_rank] = int(
                time.time())

            # The first worker detects whether there are tasks in the task queue
            if self.local_rank %  mp_num_per_node == 0:
                if self.task_queue.num_tasks() > 0:
                    if self.nnode > 1:
                        self.task_queue.read_finish_flag.set(1)
                    else:
                        self.exist_task_signal.value[
                            self.fd_config.parallel_config.
                            expert_parallel_rank] = 1

            if self.parallel_config.tensor_parallel_size > 1:
                # Synchronize the signal for other workers
                # TODO(@wufeisheng): Split TP group and EP group
                paddle.distributed.barrier()

            if self.fd_config.load_config.dynamic_load_weight:
                if self.exist_task_signal.value[0] == 2:
                    from fastdeploy.rl.dynamic_weight_manager import \
                        DynamicWeightManager
                    DynamicWeightManager.check_model_weights_status(
                        self.model_weights_status, self.worker.model_runner,
                        self.parallel_config.engine_pid)

            if self.exist_task_signal.value[
                    self.fd_config.parallel_config.expert_parallel_rank] == 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[self.fd_config.parallel_config
                                                 .expert_parallel_rank] = 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)

            if not self.worker.model_runner.not_need_stop():
                if self.ranks > 1:
                    paddle.distributed.barrier()

                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)

            self.exist_prefill_task_signal.value[
                0] = self.worker.prefill_finished()


    def determine_num_available_blocks(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} --------"
            )
            logger.info(
                f"------- num_blocks_local:{num_blocks_local} --------")

            logger.info(
                f"self.fd_config.parallel_config.do_profile:{self.fd_config.parallel_config.do_profile}"
            )

            # 3. Send IPCSignal
            get_profile_block_num = np.zeros(shape=[self.ranks],
                                             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[
                self.local_rank] = num_blocks_local

            # Wait all worker send the signal
            while np.any(self.get_profile_block_num_signal.value <= 0):
                time.sleep(0.01)
            num_blocks_global = self.get_profile_block_num_signal.value.min(
            ).item()
            self.get_profile_block_num_signal.value[
                self.local_rank] = num_blocks_global
        else:
            num_blocks_global = self.fd_config.parallel_config.total_block_num
        # NOTE(liuzichang): Too big num_blocks_global will lead to error 700
        # 4. Updata share inputs
        self.worker.reinitialize_kv_cache(num_gpu_blocks=num_blocks_global)

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

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


def parse_args():
    """
    Parse args from command line
    """
    parser = argparse.ArgumentParser("FastDeploy LLM Inference")
    parser.add_argument("-m",
                        "--model_name_or_path",
                        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=int, 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_method",
        default=None,
        type=none_or_str,
        choices=[
            None,
            "ngram",
            "mtp",
        ],
    )
    parser.add_argument(
        "--speculative_max_draft_token_num",
        default=1,
        type=int,
    )
    parser.add_argument(
        "--speculative_model_name_or_path",
        default="",
        type=str,
    )
    parser.add_argument(
        "--speculative_model_quantization",
        default="WINT8",
        type=str,
    )
    parser.add_argument(
        "--speculative_benchmark_mode",
        default=False,
        type=bool,
    )
    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("--enable-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("--enable_expert_parallell",
                        action='store_true',
                        help="enable expert parallell")
    parser.add_argument("--ori_vocab_size", type=int, default=None)

    parser.add_argument("--quantization",
                        type=str,
                        default="None",
                        help="Quantization name for the model, currentlly 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_optimiaztion_config",
                        type=json.loads,
                        default=None,
                        help=" Configation of Graph optimization backend. "
    )
    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'],
        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_mm",
                        type=str,
                        default="false",
                        help="Whether to use vl")
    parser.add_argument("--enable_logprob",
                        action='store_true',
                        help="Enable output of token-level log probabilities.")

    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
    """
    paddle.set_default_dtype(args.dtype)
    model_config = ModelConfig(vars(args))
    device_config = DeviceConfig(vars(args))
    decoding_config = DecodingConfig(vars(args))
    speculative_config = SpeculativeConfig(vars(args))
    parallel_config = ParallelConfig(vars(args))
    load_config = LoadConfig(vars(args))

    graph_opt_config = GraphOptimizationConfig(
        use_cudagraph=args.graph_optimiaztion_config["use_cudagraph"],
        graph_opt_level=args.graph_optimiaztion_config["graph_opt_level"],
        cudagraph_capture_sizes=args.graph_optimiaztion_config["cudagraph_capture_sizes"]
    )

    # 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}"
    )

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

    quantization_config = model_config.quantization_config
    if not model_config.is_quantized:
        if quantization_config is not None:
            if "kv_cache_quant_type" not in quantization_config:
                model_config.is_quantized = True

    quant_config_name = None
    if quantization_config is not None and quantization_config.get(
            "quantization", None) is None:
        raise ValueError(
            "quantization_config should have a key named 'quantization' for specify quant config."
        )

    if quantization_config is not None:
        quant_config_name = quantization_config["quantization"]
    elif args.quantization != "None":
        quantization_config = {}
        quant_config_name = args.quantization
        quantization_config["quantization"] = quant_config_name
        # Special handling for Ernie models
        is_ernie = "Ernie4_5_ForCausalLM" in model_config.architectures or \
                    "Ernie4_5_MoeForCausalLM" in model_config.architectures or \
                    "Ernie4_5_VLMoeForConditionalGeneration" in model_config.architectures
        if quant_config_name == "wint4" and is_ernie:
            quantization_config["dense_quant_type"] = "wint8"
            quantization_config["moe_quant_type"] = "wint4"
            quantization_config["quantization"] = "mix_quant"
            quant_config_name = "mix_quant"
    else:
        quant_config_name = None

    if quant_config_name is None:
        quant_config = None
    else:
        quant_cls = get_quantization_config(quant_config_name)
        quant_config = quant_cls.from_config(quantization_config)

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

    # Set VL tag
    model_config.enable_mm = getattr(args, 'enable_mm', 'false').lower() == 'true'
    logger.info(f"- Dynamic load weight: {load_config.dynamic_load_weight}")
    logger.info(f"- Load strategy: {load_config.load_strategy}")

    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)

    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
    worker_proc = PaddleDisWorkerProc(fd_config, ranks, local_rank)

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

    # Load model
    worker_proc.load_model()
    logger.info("determine_num_available_blocks")
    worker_proc.determine_num_available_blocks()

    # Trigger CUDAGraph capture
    worker_proc.worker.graph_optimize_and_warm_up_model()

    # Initialize health status
    worker_proc.init_health_status()

    # Start event loop
    if fd_config.parallel_config.use_ep:
        # TODO(wufeisheng): Delete this branch
        worker_proc.event_loop_ep()
    else:
        worker_proc.event_loop_normal()


if __name__ == "__main__":
    run_worker_proc()