gpu_model_runner.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 copy
import os
import queue
import time
from concurrent.futures import Future
from threading import Thread
from typing import Any, Dict, List, Optional, cast

import numpy as np
import paddle
from paddle import nn
from paddleformers.utils.log import logger

from fastdeploy.config import FDConfig
from fastdeploy.engine.pooling_params import PoolingParams
from fastdeploy.engine.request import ImagePosition, Request, RequestType
from fastdeploy.model_executor.graph_optimization.utils import (
    profile_run_guard,
    sot_warmup_guard,
)
from fastdeploy.model_executor.guided_decoding import (
    LogitsProcessorBase,
    get_guided_backend,
)
from fastdeploy.model_executor.layers.attention import get_attention_backend
from fastdeploy.model_executor.layers.attention.append_attn_backend import (
    allocate_launch_related_buffer,
)
from fastdeploy.model_executor.layers.attention.base_attention_backend import (
    AttentionBackend,
)
from fastdeploy.model_executor.layers.moe.routing_indices_cache import (
    RoutingReplayManager,
)
from fastdeploy.model_executor.layers.rotary_embedding import get_rope_3d
from fastdeploy.model_executor.layers.sample.meta_data import SamplingMetadata
from fastdeploy.model_executor.layers.sample.sampler import Sampler, SpeculativeSampler
from fastdeploy.model_executor.model_loader import get_model_loader
from fastdeploy.platforms import current_platform
from fastdeploy.spec_decode import SpecMethod
from fastdeploy.worker.input_batch import InputBatch, reorder_split_prefill_and_decode

if current_platform.is_iluvatar():
    from fastdeploy.model_executor.ops.iluvatar import (
        recover_decode_task,
        set_data_ipc,
        set_value_by_flags_and_idx,
    )

    share_external_data = None
elif current_platform.is_dcu():
    from fastdeploy.model_executor.ops.gpu import set_value_by_flags_and_idx

    recover_decode_task = None
    share_external_data = None
else:
    from fastdeploy.model_executor.ops.gpu import (
        recover_decode_task,
        set_value_by_flags_and_idx,
        share_external_data,
        speculate_schedule_cache,
        set_data_ipc,
        unset_data_ipc,
    )

import zmq

from fastdeploy import envs
from fastdeploy.engine.tasks import PoolingTask
from fastdeploy.input.ernie4_5_vl_processor import DataProcessor
from fastdeploy.inter_communicator import IPCSignal, ZmqIpcClient
from fastdeploy.logger.deterministic_logger import DeterministicLogger
from fastdeploy.model_executor.forward_meta import ForwardMeta
from fastdeploy.model_executor.layers.pool.metadata import PoolingMetadata
from fastdeploy.model_executor.models.ernie4_5_vl.modeling_resampler import ScatterOp
from fastdeploy.model_executor.models.interfaces_base import FdModelForPooling
from fastdeploy.model_executor.pre_and_post_process import (
    async_set_value,
    post_process,
    pre_process,
    rebuild_padding,
    save_output_normal,
)
from fastdeploy.output.pooler import PoolerOutput
from fastdeploy.worker.model_runner_base import (
    DistributedOut,
    DistributedStatus,
    ModelRunnerBase,
)
from fastdeploy.worker.output import LogprobsTensors, ModelOutputData, ModelRunnerOutput


class GPUModelRunner(ModelRunnerBase):
    def __init__(
        self,
        fd_config: FDConfig,
        device: str,  # logic device
        device_id: int,  # physical device id
        rank: int,
        local_rank: int,
    ):
        super().__init__(fd_config=fd_config, device=device)
        self.MAX_INFER_SEED = 9223372036854775806
        self.enable_mm = self.model_config.enable_mm
        self.rank = rank
        self.local_rank = local_rank
        self.device_id = device_id
        self.spec_method = self.fd_config.speculative_config.method
        self.speculative_decoding = self.spec_method is not None
        self.enable_logprob = fd_config.model_config.enable_logprob
        self.enable_early_stop = self.fd_config.early_stop_config.enable_early_stop
        self.is_pooling_model = self.fd_config.model_config.runner_type == "pooling"
        self.ori_vocab_size = self.fd_config.model_config.ori_vocab_size
        self.max_logprobs = None
        if self.enable_logprob:
            self.max_logprobs = (
                self.ori_vocab_size
                if fd_config.model_config.max_logprobs == -1
                else fd_config.model_config.max_logprobs
            )
        self.temp_scaled_logprobs = True
        self.top_p_normalized_logprobs = True
        self.prompt_logprobs_reqs: dict[str, Request] = {}
        self.in_progress_prompt_logprobs: dict[str, LogprobsTensors] = {}
        self.forward_batch_reqs_list: list[Request] = [None for _ in range(self.scheduler_config.max_num_seqs)]
        self.cache_kvs_map: dict = {}
        self.exist_prefill_flag = False

        if self.speculative_decoding:
            self._real_output_token_num_host = paddle.empty([1], dtype="int32").pin_memory()
            self.output_token_num_event = paddle.device.cuda.Event()

        # VL model config:
        if self.enable_mm:
            if "ernie" in self.fd_config.model_config.model_type:
                self._init_image_preprocess()

            self.amp_black = [
                "reduce_sum",
                "c_softmax_with_cross_entropy",
                "elementwise_div",
                "sin",
                "cos",
                "sort",
                "multinomial",
            ]
            self.amp_white = [
                "lookup_table",
                "lookup_table_v2",
                "flash_attn",
                "matmul",
                "matmul_v2",
                "fused_gemm_epilogue",
            ]

            if self.cache_config.max_encoder_cache > 0:
                self.encoder_cache: dict[str, paddle.Tensor] = {}
            else:
                self.encoder_cache = None

        #  Sampler
        if not self.speculative_decoding:
            self.sampler = Sampler(fd_config)
        else:
            self.sampler = SpeculativeSampler(fd_config)

        self.guided_backend = None
        if self.fd_config.structured_outputs_config.guided_decoding_backend != "off":
            self.guided_backend = get_guided_backend(fd_config=self.fd_config)
            self.sampler.set_reasoning_parser(self.guided_backend.get_reasoning_parser())

        # Lazy initialize kv cache after model loading
        # self.kv_caches: list[paddle.Tensor] = []

        # CUDA Graph
        self.use_cudagraph = self.graph_opt_config.use_cudagraph
        self.cudagraph_capture_sizes = list(reversed(self.graph_opt_config.cudagraph_capture_sizes))
        self.cudagraph_capture_sizes_prefill = list(reversed(self.graph_opt_config.cudagraph_capture_sizes_prefill))
        self.sot_warmup_sizes = self.graph_opt_config.sot_warmup_sizes
        self.cudagraph_only_prefill = self.graph_opt_config.cudagraph_only_prefill

        # Initialize input batch
        self.share_inputs = InputBatch(self.fd_config)
        self.share_inputs.init_share_inputs()
        self.increment_value = (
            4 if not self.speculative_decoding else (self.speculative_config.num_speculative_tokens + 1) * 4
        )
        self.infer_seed_increment = paddle.full(
            shape=[self.scheduler_config.max_num_seqs, 1], fill_value=self.increment_value, dtype="int64", device="cpu"
        )

        self.restore_chunked_prefill_request = dict()

        # Initialize deterministic logger (only when deterministic debugging is enabled)
        self.deterministic_logger = (
            DeterministicLogger(self.share_inputs)
            if envs.FD_DETERMINISTIC_MODE and envs.FD_DETERMINISTIC_LOG_MODE
            else None
        )

        # Initialize attention Backend
        # NOTE(gonshaotian): Currently, all attention layers share one attention backend instance.
        # In the future, we will expand it as a list.
        self.attn_backends: list[AttentionBackend] = []
        # self.attn_metadatas: list[AttentionMetadata] = []
        self._initialize_attn_backend()

        # Forward meta store the global meta information of the forward
        self.forward_meta: ForwardMeta = None

        # Postprocess Env params
        os.environ["INFERENCE_MSG_QUEUE_ID"] = str(self.parallel_config.local_engine_worker_queue_port)
        logger.info(f"queue id is {str(self.parallel_config.local_engine_worker_queue_port)}")

        # Rollout routing replay config
        self.routing_replay_manager = None

        self.zmq_client = None
        self.async_output_queue = None
        if envs.FD_USE_GET_SAVE_OUTPUT_V1:
            port = self.fd_config.parallel_config.local_engine_worker_queue_port
            logger.info(f"zmq client get_save_output_rank{local_rank}_{port}")
            self.zmq_client = ZmqIpcClient(name=f"get_save_output_rank{local_rank}_{port}", mode=zmq.PUSH)
            self.zmq_client.connect()
            self.zmq_client.socket.SNDTIMEO = 3000
            self.async_output_queue: queue.Queue = queue.Queue()
            self.async_output_copy_thread = Thread(
                target=self._async_output_busy_loop,
                daemon=True,
                name="WorkerAsyncOutputCopy",
            )
            self.async_output_copy_thread.start()

        self.enable_entropy = self.model_config.enable_entropy

        # init signal
        cache_ready_signal_data = np.zeros(shape=[self.parallel_config.tensor_parallel_size], dtype=np.int32)
        self.cache_ready_signal = IPCSignal(
            name="cache_ready_signal",
            array=cache_ready_signal_data,
            dtype=np.int32,
            suffix=self.parallel_config.local_engine_worker_queue_port,
            create=False,
        )

        # for overlap
        self._cached_model_output_data = None
        self._cached_sampler_output = None
        self._cached_post_process_event = None
        # Cached token count for next batch prediction in overlap scheduling.
        # Used to avoid synchronization overhead when preparing inputs for the next batch.
        self._cached_launch_token_num = -1
        self.enable_overlap_schedule = fd_config.scheduler_config.enable_overlap_schedule and (
            not self.speculative_decoding
        )
        if self.enable_overlap_schedule:
            logger.info("Using overlap schedule")
        self.current_launch_token_num = 0

    def _async_output_busy_loop(self):
        """Entrypoint for the thread which handles outputs asynchronously."""
        while True:
            try:
                output = self.async_output_queue.get()
                self.zmq_client.send_pyobj(output)
            except Exception as e:
                logger.exception("Exception in async output loop: %s", e)

    def exist_prefill(self):
        """
        check whether prefill stage exist
        """
        return self.exist_prefill_flag

    def exist_decode(self):
        """
        check whether decode stage exist
        """
        seq_lens_decoder = self.share_inputs["seq_lens_decoder"]
        stop_flags = self.share_inputs["stop_flags"].squeeze(1)
        return ((seq_lens_decoder > 0) & ~stop_flags).any().cpu().numpy().item()

    def _resolve_current_launch_token_num(
        self, cached_token_num: int, token_num_event, is_dummy_or_profile_run: bool
    ) -> int:
        """
        Resolve token count for current batch.

        In overlap mode, uses cached value from previous batch prediction to avoid GPU-CPU sync.
        Falls back to fresh computation in certain conditions:
        - dummy/profile runs need accurate counts
        - non-overlap mode doesn't support caching
        - prefill stage changes batch composition
        - invalid cached value
        """
        if (
            is_dummy_or_profile_run
            or (not self.enable_overlap_schedule)
            or self.exist_prefill()
            or cached_token_num <= 0
        ):
            token_num_event.synchronize()
            return self.share_inputs["seq_lens_this_time_cpu"].numpy().sum().item()
        return cached_token_num

    def _predict_next_launch_token_num(self) -> int:
        """
        Predict token count for next batch.

        In overlap scheduling, while current batch executes model forward,
        the scheduler may have prepared decode requests for next batch.
        This prediction allows next batch to skip synchronization.

        Returns -1 if prediction is not applicable (non-overlap or prefill exists).
        """
        if self.exist_prefill():
            return -1
        return (
            self.share_inputs["seq_lens_this_time_cpu"].numpy().sum().item()
            + self.share_inputs["is_block_step_cpu"].numpy().sum().item()
        )

    def only_prefill(self):
        """
        check whether prefill only
        """
        if_only_prefill = True
        decode_exists = None
        if self.fd_config.parallel_config.use_ep and self.fd_config.scheduler_config.splitwise_role == "mixed":
            only_prefill_batch_list = []
            decode_exists = self.exist_decode()
            paddle.distributed.all_gather_object(only_prefill_batch_list, not decode_exists)
            if_only_prefill = all(only_prefill_batch_list)

        if_only_prefill = if_only_prefill and not (decode_exists if decode_exists is not None else self.exist_decode())

        return if_only_prefill

    def collect_distributed_status(self):
        """
        Collect distributed status
        """
        dist_status_list = []
        dist_status_obj = DistributedStatus()
        dist_out = DistributedOut()

        prefill_exists = None
        if_only_decode = True
        # mix ep in single node
        if self.fd_config.parallel_config.use_ep and self.fd_config.scheduler_config.splitwise_role == "mixed":
            prefill_exists = self.exist_prefill()
            dist_status_obj.only_decode = not prefill_exists

        # whether chunked moe
        if self.fd_config.parallel_config.enable_chunked_moe:
            chunk_size = self.fd_config.parallel_config.chunked_moe_size
            token_num = self.share_inputs["ids_remove_padding"].shape[0]

            if token_num > chunk_size:
                self.forward_meta.moe_num_chunk = (token_num + chunk_size - 1) // chunk_size
            else:
                self.forward_meta.moe_num_chunk = 1

            dist_status_obj.moe_num_chunk = self.forward_meta.moe_num_chunk

        # only ep need to collect and sync distributed status
        if self.fd_config.parallel_config.use_ep and self.fd_config.scheduler_config.splitwise_role == "mixed":
            # call once to gather all status
            paddle.distributed.all_gather_object(dist_status_list, dist_status_obj)

            # Update Batch type for cuda graph for if_only_decode
            if_only_decode = all(dist_status.only_decode for dist_status in dist_status_list)

        if_only_decode = if_only_decode and not (
            prefill_exists if prefill_exists is not None else self.exist_prefill()
        )

        max_moe_num_chunk = None
        if self.fd_config.parallel_config.enable_chunked_moe:
            max_moe_num_chunk = max(dist_status.moe_num_chunk for dist_status in dist_status_list)

        dist_out = DistributedOut(
            if_only_decode=if_only_decode,
            max_moe_num_chunk=max_moe_num_chunk,
        )

        return dist_out

    def only_decode(self):
        """
        check whether decode only
        """
        # Update Batch type for cuda graph for if_only_decode
        if_only_decode = True
        prefill_exists = None
        # mix ep in single node
        if self.fd_config.parallel_config.use_ep and self.fd_config.scheduler_config.splitwise_role == "mixed":
            only_decode_batch_list = []
            prefill_exists = self.exist_prefill()
            paddle.distributed.all_gather_object(only_decode_batch_list, not prefill_exists)
            if_only_decode = all(only_decode_batch_list)

        if_only_decode = if_only_decode and not (
            prefill_exists if prefill_exists is not None else self.exist_prefill()
        )

        return if_only_decode

    def _init_speculative_proposer(self):
        """
        Init speculative proposer
        """
        if self.spec_method is None:
            self.proposer = None
            return
        # MTP-specific: swap seq_lens_this_time to the buffer tensor
        if self.spec_method == SpecMethod.MTP:
            self.share_inputs["seq_lens_this_time"] = self.share_inputs["seq_lens_this_time_buffer"]
        self.proposer = self.spec_method.create_proposer(
            self.fd_config,
            main_model=self.get_model(),
            local_rank=self.local_rank,
            device_id=self.device_id,
            share_inputs=self.share_inputs,
        )

    def _init_logits_processor(self, request) -> tuple[Future[LogitsProcessorBase],]:
        """
        init logits processor for guided decoding
        """
        assert self.guided_backend is not None, (
            "guided_backend is None, use " "--guided-decoding-backend to specify the backend at server startup."
        )

        if request.guided_json is not None:
            schemata_key = ("json", request.guided_json)
        elif request.guided_regex is not None:
            schemata_key = ("regex", request.guided_regex)
        elif request.guided_grammar is not None:
            schemata_key = ("grammar", request.guided_grammar)
        elif request.structural_tag is not None:
            schemata_key = ("structural_tag", request.structural_tag)

        return (
            self.guided_backend.get_logits_processor(
                schemata_key=schemata_key,
                enable_thinking=False,  # TODO cfg
            ),
            schemata_key,
        )

    def _process_mm_features(self, request_list: List[Request]):
        """
        Process and cache vision features from model
            - add image_features, extract and cache vision features from model
            - add rope_emb, rotate position embeddings
        """
        if not self.enable_mm:
            return
        self.share_inputs["image_features_list"] = [-1] * self.scheduler_config.max_num_seqs
        img_index = 0
        req_idx_img_index_map = {}
        multi_vision_inputs = {
            "images_lst": [],
            "grid_thw_lst": [],
            "vit_position_ids_lst": [],
            "cu_seqlens": [0],
            "encoder_cache_info": [],
            "feature_position_list": [],
            "grid_thw_lst_batches": [],
            "feature_position_list_batches": [],
        }
        rope_3d_position_ids = {
            "position_ids_idx": [],
            "position_ids_lst": [],
            "position_ids_offset": [0],
            "max_tokens_lst": [],
        }

        for request in request_list:
            if request.task_type.value != RequestType.PREFILL.value:
                continue

            if self.encoder_cache is not None:
                evict_mm_hashes = request.get("evict_mm_hashes", None)
                if evict_mm_hashes:
                    for mm_hash in evict_mm_hashes:
                        self.encoder_cache.pop(mm_hash, None)

            position_ids = request.multimodal_inputs["position_ids"]
            idx = self.share_inputs.get_index_by_batch_id(request.idx)
            rope_3d_position_ids["position_ids_idx"].append(idx)
            req_idx_img_index_map[idx] = -1
            rope_3d_position_ids["position_ids_lst"].append(position_ids)
            rope_3d_position_ids["position_ids_offset"].append(
                position_ids.shape[0] + rope_3d_position_ids["position_ids_offset"][-1]
            )

            if self.is_pooling_model:
                rope_3d_position_ids["max_tokens_lst"].append(0)
            else:
                rope_3d_position_ids["max_tokens_lst"].append(request.get("max_tokens", 2048))

            if request.with_image:
                req_idx_img_index_map[idx] = img_index
                img_index = img_index + 1
                inputs = request.multimodal_inputs
                if self.encoder_cache is not None:
                    if envs.FD_ENABLE_MAX_PREFILL:
                        if "vit_seqlen" in inputs:
                            vit_seqlen_list = inputs["vit_seqlen"][request.num_image_start : request.num_image_end]
                        if "vit_position_ids" in inputs:
                            vit_position_ids_list = inputs["vit_position_ids"][
                                request.num_image_start : request.num_image_end
                            ]
                    grid_thw_list = inputs["grid_thw"][request.num_image_start : request.num_image_end]
                    mm_hashes_list = inputs["mm_hashes"][request.num_image_start : request.num_image_end]
                    feature_positions = self._get_feature_positions(
                        mm_positions=inputs["mm_positions"][request.num_image_start : request.num_image_end],
                        prefill_start_index=request.prefill_start_index,
                        prefill_end_index=request.prefill_end_index,
                    )
                    image_start_idx = request.num_image_start

                    logger.debug(
                        f"request {request.request_id} start process encoder info, image_start_idx: {image_start_idx} "
                        f"grid_thw_list: {grid_thw_list}, feature_positions: {feature_positions}, mm_hashes_list: {mm_hashes_list}"
                    )
                    encoder_cache_info_per_req = []
                    grid_thw_lst_per_req = []
                    for i, mm_hash in enumerate(mm_hashes_list):
                        image_offset = np.prod(grid_thw_list[i])
                        logger.debug(
                            f"run idx {i} with mm_hash {mm_hash} image_offset: {image_offset} grid_thw: {grid_thw_list[i]}"
                        )
                        if mm_hash in self.encoder_cache:
                            encoder_cache_info_per_req.append((mm_hash, feature_positions[i], True))
                            continue

                        encoder_cache_info_per_req.append((mm_hash, feature_positions[i], False))
                        if envs.FD_ENABLE_MAX_PREFILL:
                            multi_vision_inputs["images_lst"].append(
                                inputs["images"][image_start_idx : image_start_idx + image_offset].to(self.device)
                            )
                            multi_vision_inputs["grid_thw_lst"].append(paddle.to_tensor(grid_thw_list[i]))
                            grid_thw_lst_per_req.append(paddle.to_tensor(grid_thw_list[i], dtype=paddle.int64))
                            multi_vision_inputs["cu_seqlens"].append(vit_seqlen_list[i])
                            multi_vision_inputs["vit_position_ids_lst"].append(vit_position_ids_list[i])
                        else:
                            multi_vision_inputs["images_lst"].append(
                                paddle.to_tensor(
                                    inputs["images"][image_start_idx : image_start_idx + image_offset],
                                    dtype="uint8" if "ernie" in self.model_config.model_type else "bfloat16",
                                )
                            )
                            multi_vision_inputs["grid_thw_lst"].append(
                                paddle.to_tensor(grid_thw_list[i], dtype=paddle.int64)
                            )
                            grid_thw_lst_per_req.append(paddle.to_tensor(grid_thw_list[i], dtype=paddle.int64))
                        image_start_idx += image_offset
                    multi_vision_inputs["grid_thw_lst_batches"].append(grid_thw_lst_per_req)
                    multi_vision_inputs["encoder_cache_info"].append(encoder_cache_info_per_req)
                else:
                    if envs.FD_ENABLE_MAX_PREFILL:
                        multi_vision_inputs["images_lst"].append(
                            inputs["images"][request.image_start : request.image_end].to(self.device)
                        )
                        multi_vision_inputs["grid_thw_lst"].extend(
                            paddle.to_tensor(inputs["grid_thw"][request.num_image_start : request.num_image_end])
                        )
                        multi_vision_inputs["grid_thw_lst_batches"].append(
                            paddle.to_tensor(inputs["grid_thw"][request.num_image_start : request.num_image_end])
                        )
                        multi_vision_inputs["cu_seqlens"].extend(
                            inputs["vit_seqlen"][request.num_image_start : request.num_image_end]
                        )
                        multi_vision_inputs["vit_position_ids_lst"].extend(
                            inputs["vit_position_ids"][request.num_image_start : request.num_image_end]
                        )
                    else:
                        multi_vision_inputs["images_lst"].append(
                            paddle.to_tensor(
                                inputs["images"][request.image_start : request.image_end],
                                dtype="uint8" if "ernie" in self.model_config.model_type else "bfloat16",
                            )
                        )
                        multi_vision_inputs["grid_thw_lst"].extend(
                            paddle.to_tensor(
                                inputs["grid_thw"][request.num_image_start : request.num_image_end],
                                dtype=paddle.int64,
                            )
                        )
                        multi_vision_inputs["grid_thw_lst_batches"].append(
                            paddle.to_tensor(
                                inputs["grid_thw"][request.num_image_start : request.num_image_end],
                                dtype=paddle.int64,
                            )
                        )
                    multi_vision_inputs["feature_position_list"].extend(
                        self._get_feature_positions(
                            mm_positions=inputs["mm_positions"][request.num_image_start : request.num_image_end],
                            prefill_start_index=request.prefill_start_index,
                            prefill_end_index=request.prefill_end_index,
                        )
                    )
                    multi_vision_inputs["feature_position_list_batches"].append(
                        self._get_feature_positions(
                            mm_positions=inputs["mm_positions"][request.num_image_start : request.num_image_end],
                            prefill_start_index=request.prefill_start_index,
                            prefill_end_index=request.prefill_end_index,
                        )
                    )
        if self.encoder_cache is not None:
            if len(multi_vision_inputs["images_lst"]) > 0 or len(multi_vision_inputs["encoder_cache_info"]) > 0:
                image_features_output = None
                if len(multi_vision_inputs["images_lst"]) > 0:
                    image_features_output = self.extract_vision_features(multi_vision_inputs)

                logger.debug(f"encoder_cache_info: {multi_vision_inputs['encoder_cache_info']}")
                feature_idx = 0
                image_features_list = []
                for index, encoder_cache_info in enumerate(multi_vision_inputs["encoder_cache_info"]):
                    merge_image_features, thw_idx = [], 0
                    for mm_hash, feature_position, use_cache in encoder_cache_info:
                        if use_cache:
                            assert mm_hash in self.encoder_cache, f"{mm_hash} not in encoder cache"
                            mm_feature = self.encoder_cache[mm_hash].cuda()
                        else:
                            assert (
                                image_features_output is not None
                            ), f"image_features_output is None, images_lst length: {len(multi_vision_inputs['images_lst'])}"
                            grid_thw = multi_vision_inputs["grid_thw_lst_batches"][index][thw_idx]
                            mm_token_lenght = inputs["mm_num_token_func"](grid_thw=grid_thw)
                            mm_feature = image_features_output[feature_idx : feature_idx + mm_token_lenght]

                            # add feature to encoder cache
                            self.encoder_cache[mm_hash] = mm_feature.detach().cpu()
                            feature_idx += mm_token_lenght
                            thw_idx += 1

                        feature_start = feature_position.offset
                        feature_end = feature_position.offset + feature_position.length
                        merge_image_features.append(mm_feature[feature_start:feature_end])
                    image_features_list.append(paddle.concat(merge_image_features, axis=0))
                for idx, index in req_idx_img_index_map.items():
                    if index != -1:
                        self.share_inputs["image_features_list"][idx] = image_features_list[index]
        elif len(multi_vision_inputs["images_lst"]) > 0:
            image_features_output = self.extract_vision_features(multi_vision_inputs)
            image_features_list = []
            feature_idx = 0
            for index, feature_position_item in enumerate(multi_vision_inputs["feature_position_list_batches"]):
                grid_thw_lst = multi_vision_inputs["grid_thw_lst_batches"][index]
                assert len(feature_position_item) == len(grid_thw_lst), f"{feature_position_item} != {grid_thw_lst}"
                merge_image_features, thw_idx = [], 0
                for feature_position in feature_position_item:
                    grid_thw = grid_thw_lst[thw_idx]
                    mm_token_lenght = inputs["mm_num_token_func"](grid_thw=grid_thw)
                    mm_feature = image_features_output[feature_idx : feature_idx + mm_token_lenght]

                    feature_start = feature_position.offset
                    feature_end = feature_position.offset + feature_position.length
                    merge_image_features.append(mm_feature[feature_start:feature_end])
                    feature_idx += mm_token_lenght
                    thw_idx += 1
                image_features_list.append(paddle.concat(merge_image_features, axis=0))
            for idx, index in req_idx_img_index_map.items():
                if index != -1:
                    self.share_inputs["image_features_list"][idx] = image_features_list[index]

        if len(rope_3d_position_ids["position_ids_idx"]) > 0:
            packed_position_ids = paddle.to_tensor(
                np.concatenate(rope_3d_position_ids["position_ids_lst"]), dtype="int64"
            )
            rope_3d_lst = self.prepare_rope3d(
                packed_position_ids,
                rope_3d_position_ids["max_tokens_lst"],
                rope_3d_position_ids["position_ids_offset"],
            )
            for i, idx in enumerate(rope_3d_position_ids["position_ids_idx"]):
                self.share_inputs["rope_emb"][idx : idx + 1, :] = rope_3d_lst[i]

    def _get_feature_positions(
        self, mm_positions: List[ImagePosition], prefill_start_index: int, prefill_end_index: int
    ):
        """
        Filter and adjust ImagePosition objects that fall within the specified prefill range.

        Args:
            mm_positions: List of ImagePosition objects to filter
            prefill_start_index: Start index of the prefill range
            prefill_end_index: End index of the prefill range

        Returns:
            List of ImagePosition objects that are within or intersect with the prefill range
        """
        feature_positions = []
        for position in mm_positions:
            position_start = position.offset
            position_end = position.offset + position.length
            if position_end <= prefill_start_index or position_start >= prefill_end_index:
                continue
            elif position_start >= prefill_start_index and position_end <= prefill_end_index:
                new_position = copy.deepcopy(position)
                new_position.offset = 0
                feature_positions.append(new_position)
            else:
                new_position = copy.deepcopy(position)
                # Adjust offset if it starts before prefill_start_index
                if position_start < prefill_start_index:
                    new_position.offset = prefill_start_index - position_start
                    new_position.length = min(position_end, prefill_end_index) - prefill_start_index
                # Adjust length if it extends beyond prefill_end_index
                elif position_end > prefill_end_index:
                    new_position.offset = 0
                    new_position.length = prefill_end_index - position_start
                feature_positions.append(new_position)

        logger.debug(
            f"get feature_positions, original positions: {mm_positions}, filtered positions: {feature_positions}"
        )
        return feature_positions

    def insert_tasks_v1(self, req_dicts: List[Request], num_running_requests: int = None):
        """
        Process scheduler output tasks, used when ENABLE_V1_KVCACHE_SCHEDULER=1
        req_dict: A list of Request dict
        num_running_requests: batch_size
        """
        req_len = len(req_dicts)

        batch_pooling_params = []
        self.share_inputs["num_running_requests"] = num_running_requests
        self.share_inputs["running_requests_ids"] = range(num_running_requests)
        for i in range(req_len):
            request = req_dicts[i]
            idx = self.share_inputs.get_index_by_batch_id(request.idx)
            self.share_inputs["req_ids"][idx] = str(request.request_id)

            if hasattr(request, "pooling_params") and request.pooling_params is not None:
                batch_pooling_params.append(request.pooling_params)

            logits_info = None
            prefill_tokens = []
            if request.task_type.value == RequestType.PREFILL.value:  # prefill task
                self.share_inputs["preempted_idx"][idx : idx + 1, :] = 0
                self.share_inputs["req_ids"][idx] = str(request.request_id)
                # guided decoding
                if (
                    request.guided_json is not None
                    or request.guided_regex is not None
                    or request.structural_tag is not None
                    or request.guided_grammar is not None
                ):
                    logits_info, schemata_key = self._init_logits_processor(request)
                    request.schemata_key = schemata_key

                    if (
                        self.scheduler_config.splitwise_role == "decode"
                        and hasattr(request, "prefill_end_index")
                        and hasattr(request, "prompt_token_ids")
                        and request.prefill_end_index > len(request.prompt_token_ids)
                        and hasattr(request, "output_token_ids")
                    ):
                        prefill_tokens.extend(request.output_token_ids)

                prefill_start_index = request.prefill_start_index
                prefill_end_index = request.prefill_end_index
                length = prefill_end_index - prefill_start_index
                if not self.is_pooling_model:
                    if request.get("enable_thinking") is not None:
                        enable_thinking = bool(request.get("enable_thinking"))
                        logger.debug(f"request {request.request_id} with {enable_thinking=} at idx {idx}")
                        self.share_inputs["enable_thinking"][idx : idx + 1, :] = enable_thinking
                        if enable_thinking:
                            self.share_inputs["limit_think_status"][idx : idx + 1, :] = 0
                            if request.get("reasoning_max_tokens") is not None:
                                # Enable thinking
                                self.share_inputs["max_think_lens"][idx : idx + 1, :] = request.get(
                                    "reasoning_max_tokens"
                                )
                            else:
                                self.share_inputs["max_think_lens"][idx : idx + 1, :] = -1
                            if request.get("response_max_tokens") is not None:
                                # Enable thinking
                                self.share_inputs["max_reply_lens"][idx : idx + 1, :] = request.get(
                                    "response_max_tokens"
                                )
                            else:
                                self.share_inputs["max_reply_lens"][idx : idx + 1, :] = -1
                        else:
                            # Disable thinking
                            self.share_inputs["max_think_lens"][idx : idx + 1, :] = -1
                            self.share_inputs["max_reply_lens"][idx : idx + 1, :] = -1
                            self.share_inputs["limit_think_status"][idx : idx + 1, :] = 0

                if isinstance(request.prompt_token_ids, np.ndarray):
                    prompt_token_ids = request.prompt_token_ids.tolist()
                else:
                    prompt_token_ids = request.prompt_token_ids
                input_ids = prompt_token_ids + request.output_token_ids
                prompt_len = len(prompt_token_ids)
                self.share_inputs["token_ids_all"][idx : idx + 1, :prompt_len] = np.array(
                    prompt_token_ids, dtype="int64"
                )
                self.share_inputs["token_ids_all"][idx : idx + 1, prompt_len:] = -1

                # Log complete input_ids for input determinism verification
                # Note: Only current request info is logged here; batch info is logged during forward
                if self.deterministic_logger is not None:
                    self.deterministic_logger.log_prefill_input(
                        request.request_id, idx, prefill_start_index, prefill_end_index, input_ids
                    )

                logger.debug(
                    f"Handle prefill request {request} at idx {idx}, "
                    f"{prefill_start_index=}, {prefill_end_index=}, "
                    f"need_prefilled_token_num={len(input_ids)}"
                    f"prompt_len={prompt_len}"
                )
                self.share_inputs["input_ids"][idx : idx + 1, :length] = np.array(
                    input_ids[prefill_start_index:prefill_end_index]
                )
                encoder_block_num = len(request.block_tables)
                self.share_inputs["encoder_block_lens"][idx : idx + 1] = encoder_block_num
                self.share_inputs["block_tables"][idx : idx + 1, :] = -1
                self.share_inputs["block_tables"][idx : idx + 1, :encoder_block_num] = np.array(
                    request.block_tables, dtype="int32"
                )
                self.share_inputs["stop_flags"][idx : idx + 1] = False
                self.share_inputs["seq_lens_decoder"][idx : idx + 1] = prefill_start_index
                self.share_inputs["seq_lens_this_time_buffer"][idx : idx + 1] = length
                self.share_inputs["seq_lens_encoder"][idx : idx + 1] = length
                self.exist_prefill_flag = True
                self.share_inputs["step_seq_lens_decoder"][idx : idx + 1] = 0
                self.share_inputs["prompt_lens"][idx : idx + 1] = len(input_ids)
                self.share_inputs["is_block_step"][idx : idx + 1] = False
                self.share_inputs["is_chunk_step"][idx : idx + 1] = prefill_end_index < len(input_ids)
                self.share_inputs["step_idx"][idx : idx + 1] = (
                    len(request.output_token_ids) if prefill_end_index >= len(input_ids) else 0
                )
                # pooling model request.sampling_params is None
                if request.sampling_params is not None and request.sampling_params.prompt_logprobs is not None:
                    self.prompt_logprobs_reqs[request.request_id] = request
                self.forward_batch_reqs_list[idx] = request

                if self.speculative_decoding and self.spec_method == SpecMethod.SUFFIX and self.proposer is not None:
                    if isinstance(request.prompt_token_ids, np.ndarray):
                        prompt_token_ids = request.prompt_token_ids.tolist()
                    else:
                        prompt_token_ids = request.prompt_token_ids
                    self.proposer.start_request(idx, request.request_id, prompt_token_ids)

                # Routing Replay
                if self.fd_config.routing_replay_config.enable_routing_replay:
                    # 1.prefix task(need regist) 2. chunkend task(not need regist)
                    self.routing_replay_manager.register_request(batch_id=idx, request_id=request.request_id)

                if (
                    self.fd_config.scheduler_config.splitwise_role == "decode"
                ):  # In PD, we continue to decode after P generate first token
                    self.share_inputs["seq_lens_encoder"][idx : idx + 1] = 0
                    self.exist_prefill_flag = False
                    self._cached_launch_token_num = -1
                    if self.speculative_decoding:
                        # D speculate decode, seq_lens_this_time = length + 1
                        self.share_inputs["seq_lens_this_time"][idx : idx + 1] = length + 1
                        self.share_inputs["draft_tokens"][idx : idx + 1, 0 : length + 1] = paddle.to_tensor(
                            request.draft_token_ids[0 : length + 1],
                            dtype="int64",
                        )
            elif request.task_type.value == RequestType.DECODE.value:  # decode task
                logger.debug(f"Handle decode request {request} at idx {idx}")
                encoder_block_num = len(request.block_tables)
                self.share_inputs["encoder_block_lens"][idx : idx + 1] = encoder_block_num
                self.share_inputs["block_tables"][idx : idx + 1, :] = -1
                if current_platform.is_cuda():
                    async_set_value(
                        self.share_inputs["block_tables"][idx : idx + 1, :encoder_block_num], request.block_tables
                    )
                else:
                    self.share_inputs["block_tables"][idx : idx + 1, :encoder_block_num] = np.array(
                        request.block_tables, dtype="int32"
                    )
                self.share_inputs["preempted_idx"][idx : idx + 1, :] = 0
                continue
            else:  # preempted task
                logger.info(f"Handle preempted request {request} at idx {idx}")
                self.share_inputs["preempted_idx"][idx : idx + 1, :] = 1
                self.share_inputs["block_tables"][idx : idx + 1, :] = -1
                self.share_inputs["stop_flags"][idx : idx + 1] = True
                self.share_inputs["seq_lens_this_time_buffer"][idx : idx + 1] = 0
                self.share_inputs["seq_lens_decoder"][idx : idx + 1] = 0
                self.share_inputs["seq_lens_encoder"][idx : idx + 1] = 0
                self.share_inputs["is_block_step"][idx : idx + 1] = False
                self.prompt_logprobs_reqs.pop(request.request_id, None)
                self.in_progress_prompt_logprobs.pop(request.request_id, None)
                self.forward_batch_reqs_list[idx] = None

                # Routing Replay
                if self.fd_config.routing_replay_config.enable_routing_replay:
                    self.routing_replay_manager.clear_request(batch_id=idx)

                continue

            assert len(request.eos_token_ids) == self.model_config.eos_tokens_lens
            self.share_inputs["eos_token_id"][:] = np.array(request.eos_token_ids, dtype="int64").reshape(-1, 1)

            self.share_inputs["top_p"][idx : idx + 1] = request.get("top_p", 0.7)
            self.share_inputs["top_k"][idx : idx + 1] = request.get("top_k", 0)
            self.share_inputs["top_k_list"][idx] = request.get("top_k", 0)
            self.share_inputs["min_p"][idx : idx + 1] = request.get("min_p", 0.0)
            self.share_inputs["min_p_list"][idx] = request.get("min_p", 0.0)
            self.share_inputs["temperature"][idx : idx + 1] = request.get("temperature", 0.95)
            self.share_inputs["penalty_score"][idx : idx + 1] = request.get("repetition_penalty", 1.0)
            self.share_inputs["frequency_score"][idx : idx + 1] = request.get("frequency_penalty", 0.0)
            self.share_inputs["presence_score"][idx : idx + 1] = request.get("presence_penalty", 0.0)
            self.share_inputs["temp_scaled_logprobs"][idx : idx + 1] = request.get("temp_scaled_logprobs", False)
            self.share_inputs["top_p_normalized_logprobs"][idx : idx + 1] = request.get(
                "top_p_normalized_logprobs", False
            )

            self.share_inputs["min_dec_len"][idx : idx + 1] = request.get("min_tokens", 1)
            self.share_inputs["max_dec_len"][idx : idx + 1] = request.get(
                "max_tokens", self.model_config.max_model_len
            )

            if request.get("seed") is not None:
                self.share_inputs["infer_seed"][idx : idx + 1] = request.get("seed")

            if request.get("bad_words_token_ids") is not None and len(request.get("bad_words_token_ids")) > 0:
                bad_words_len = len(request.get("bad_words_token_ids"))
                self.share_inputs["bad_tokens_len"][idx] = bad_words_len
                self.share_inputs["bad_tokens"][idx : idx + 1, :bad_words_len] = np.array(
                    request.get("bad_words_token_ids"), dtype="int64"
                )
            else:
                self.share_inputs["bad_tokens_len"][idx] = 1
                self.share_inputs["bad_tokens"][idx : idx + 1, :] = np.array([-1], dtype="int64")

            if request.get("stop_token_ids") is not None and request.get("stop_seqs_len") is not None:
                stop_seqs_num = len(request.get("stop_seqs_len"))
                for i in range(stop_seqs_num, self.model_config.max_stop_seqs_num):
                    request.sampling_params.stop_seqs_len.append(0)
                self.share_inputs["stop_seqs_len"][idx : idx + 1, :] = np.array(
                    request.sampling_params.stop_seqs_len, dtype="int32"
                )
                self.share_inputs["stop_seqs"][
                    idx : idx + 1, :stop_seqs_num, : len(request.get("stop_token_ids")[0])
                ] = np.array(request.get("stop_token_ids"), dtype="int64")
            else:
                self.share_inputs["stop_seqs_len"][idx : idx + 1, :] = 0

            self.pooling_params = batch_pooling_params
            # For logits processors
            self.share_inputs["logits_processors_args"][idx] = request.get("logits_processors_args") or {}

            self.sampler.apply_logits_processor(idx, logits_info, prefill_tokens)

        self._process_mm_features(req_dicts)

        self.share_inputs["seq_lens_this_time"] = self.share_inputs["seq_lens_this_time_buffer"][:num_running_requests]
        if self.spec_method == SpecMethod.MTP:
            self.proposer.insert_tasks_v1(req_dicts, num_running_requests, self.share_inputs.index_to_batch_id)

    def insert_prefill_inputs(self, req_dicts: List[Request], num_running_requests: int):
        raise NotImplementedError("GPUs only support KVCACHE SCHEDULER V1 in versions 2.6 and above.")

    def get_input_length_list(
        self, num_tokens: int, batch_size: int, expected_decode_len: int, capture_prefill: bool = False
    ):
        """
        Generates some list for _dummy_prefill_inputs, when capture pure prefill or mtp,
        the list should be carefully constructed.

        This function addresses a specific problem: in the pure prefill stage, variable
        input lengths (e.g., `prompt[160, 0]` vs. `prompt[80, 80]`) can lead to different
        CUDA Grid dimensions for kernels like `split_q_block`. This prevents CUDA Graph
        reuse.

        The `split_q_block` kernel calculates the total number of blocks, which directly
        determines the `griddim.x` launch parameter for the `multi_query_append_attention_kernel`.
        The blocks for a single sequence are determined by the formula:
        `num_blocks = ceil((sequence_length * group_size) / block_shape_q)`

        Due to the `ceil` (ceiling) function, distributing a total number of tokens across
        a batch of shorter sequences will result in a larger total block count. For example,
        with a `group_size` of 5 and `block_shape_q` of 64:
        - A single sequence of 160 tokens requires `ceil((160 * 5) / 64) = 13` blocks.
        - Two sequences of 80 tokens each require `ceil((80 * 5) / 64) * 2 = 7 * 2 = 14` blocks.

        To ensure graph replayability, this function creates a "dummy" list of sequence
        lengths that's designed to produce the theoretical maximum `encoder_num_blocks_x_cpu`
        for the given `num_tokens` and `batch_size`. This strategy ensures the captured
        CUDA Graph has the largest possible grid dimensions. At runtime, if the actual number
        of blocks is less than or equal to this maximum, the kernel can safely execute by
        using an early-exit mechanism.

        Args:
            num_tokens (int): The total number of tokens across all sequences.
            batch_size (int): The number of sequences (requests) in the batch.

        Returns:
            List[int]: A list of integers representing the sequence length for each request.
                    This list is crafted to maximize the total number of blocks.
        """
        # NOTE(gongshaotian): The maximum decoding length is equal to the expected decoded tokens plus the eos token
        max_dec_len = expected_decode_len + 1
        input_length = min(
            num_tokens // (1 if capture_prefill else batch_size),
            self.model_config.max_model_len - max_dec_len,
        )

        # NOTE(wanglongzhi): When the full length is too large, DeepEP's buffer size will not be enough to cause the result to appear nan.
        # TODO(wanglongzhi): Figure out the accurate buffer size of DeepEP.
        if self.fd_config.parallel_config.enable_expert_parallel:
            input_length = min(input_length, 32)

        block_num = (
            input_length + self.cache_config.block_size - 1
        ) // self.cache_config.block_size + self.cache_config.enc_dec_block_num

        input_length_list = [input_length] * batch_size

        if capture_prefill:
            if num_tokens < batch_size:
                input_length_list = [1] * num_tokens
            else:
                input_length_list = [1] * (batch_size - 1)
                input_length_list.append(num_tokens - batch_size + 1)

        len_of_input_length_list = len(input_length_list)
        max_dec_len_list = [max_dec_len] * len_of_input_length_list

        return input_length_list, max_dec_len_list, block_num

    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not self.is_pooling_model:
            return []

        supported_tasks = list(model.pooler.get_supported_tasks())

        if self.cache_config.enable_chunked_prefill and "encode" in supported_tasks:
            supported_tasks.remove("encode")

            logger.debug(
                "Chunked prefill is not supported with "
                "encode task which using ALL pooling. "
                "Please turn off chunked prefill by export=FD_DISABLE_CHUNKED_PREFILL=1 before using it."
            )

        # score not support
        return supported_tasks

    def _dummy_prefill_inputs(self, input_length_list: List[int], max_dec_len_list: List[int], block_num: int):
        """Set dummy prefill inputs to share_inputs"""
        batch_size = len(input_length_list)
        for i in range(batch_size):
            idx = i
            input_length = input_length_list[i]
            max_dec_len = max_dec_len_list[i]
            self.share_inputs["input_ids"][idx : idx + 1, :input_length] = np.array([5] * input_length)
            self.share_inputs["token_ids_all"][idx : idx + 1, :input_length] = np.array([5] * input_length)
            self.share_inputs["eos_token_id"][:] = np.array(
                [2] * self.model_config.eos_tokens_lens, dtype="int64"
            ).reshape(-1, 1)
            self.share_inputs["seq_lens_this_time_buffer"][idx : idx + 1] = input_length
            self.share_inputs["step_seq_lens_encoder"][idx : idx + 1] = input_length
            self.share_inputs["seq_lens_encoder"][idx : idx + 1] = input_length
            self.exist_prefill_flag = True
            self.share_inputs["seq_lens_decoder"][idx : idx + 1] = 0
            self.share_inputs["prompt_lens"][idx : idx + 1] = 0
            self.share_inputs["step_idx"][idx : idx + 1] = 0
            self.share_inputs["max_dec_len"][idx : idx + 1] = max_dec_len
            self.share_inputs["min_dec_len"][idx : idx + 1] = max_dec_len
            self.share_inputs["stop_flags"][idx : idx + 1] = False
            self.share_inputs["temperature"][idx : idx + 1] = 1

            self.share_inputs["encoder_block_lens"][idx : idx + 1] = block_num
            self.share_inputs["block_tables"][idx : idx + 1, :block_num] = np.arange(
                idx * block_num, (idx + 1) * block_num, 1
            )
        self.share_inputs["seq_lens_this_time"] = self.share_inputs["seq_lens_this_time_buffer"]

    def _prepare_inputs(self, cached_token_num=-1, is_dummy_or_profile_run=False) -> None:
        """Prepare the model inputs"""
        if self.enable_mm and self.share_inputs["image_features_list"] is not None:
            tensor_feats = [t for t in self.share_inputs["image_features_list"] if isinstance(t, paddle.Tensor)]
            if tensor_feats:
                self.share_inputs["image_features"] = paddle.concat(tensor_feats, axis=0)
        recover_decode_task(
            self.share_inputs["stop_flags"],
            self.share_inputs["seq_lens_this_time"],
            self.share_inputs["seq_lens_encoder"],
            self.share_inputs["seq_lens_decoder"],
            self.share_inputs["step_seq_lens_decoder"],
            self.share_inputs["block_tables"],
            self.share_inputs["is_block_step"],
            self.share_inputs["draft_tokens"] if self.speculative_decoding else None,
            self.share_inputs["step_draft_tokens"] if self.speculative_decoding else None,
            self.share_inputs["step_seq_lens_this_time"] if self.speculative_decoding else None,
            self.cache_config.block_size,
            self.speculative_config.num_speculative_tokens if self.speculative_decoding else 0,
        )
        logprobs_reqs = [
            req
            for req in self.forward_batch_reqs_list
            if req is not None and req.sampling_params is not None and req.sampling_params.logprobs is not None
        ]
        if len(logprobs_reqs):
            self.max_logprobs = (
                max(
                    [
                        self.ori_vocab_size if req.sampling_params.logprobs < 0 else req.sampling_params.logprobs
                        for req in logprobs_reqs
                    ]
                )
                if not self.speculative_decoding
                else 20
            )
            self.temp_scaled_logprobs = any(req.sampling_params.temp_scaled_logprobs for req in logprobs_reqs)
            self.top_p_normalized_logprobs = any(
                req.sampling_params.top_p_normalized_logprobs for req in logprobs_reqs
            )
        elif self.enable_logprob:
            self.max_logprobs = None if not self.speculative_decoding else 0

        # Remove padding
        self.share_inputs["seq_lens_this_time_cpu"].copy_(self.share_inputs["seq_lens_this_time"], False)
        self.share_inputs["is_block_step_cpu"].copy_(self.share_inputs["is_block_step"], False)
        token_num_event = paddle.device.cuda.create_event()
        token_num_event.record()
        token_num = self._resolve_current_launch_token_num(cached_token_num, token_num_event, is_dummy_or_profile_run)
        (
            ids_remove_padding,
            batch_id_per_token,
            cu_seqlens_q,
            cu_seqlens_k,
            cu_seqlens_q_output,
            batch_id_per_token_output,
            real_output_token_num,
        ) = pre_process(
            token_num,
            self.share_inputs["input_ids"],
            self.share_inputs["seq_lens_this_time"],
            self.speculative_decoding,
            (self.share_inputs["draft_tokens"] if self.speculative_decoding else None),
            self.share_inputs["seq_lens_encoder"],
            self.share_inputs["seq_lens_decoder"],
        )
        self.share_inputs["ids_remove_padding"].copy_(ids_remove_padding, False)
        # NOTE: (changwenbin) Initialized to max_num_seq '-1' before copying, marking illegal positions
        self.share_inputs["batch_id_per_token"][:] = -1
        self.share_inputs["batch_id_per_token"].copy_(batch_id_per_token, False)
        self.share_inputs["cu_seqlens_q"].copy_(cu_seqlens_q, False)
        self.share_inputs["cu_seqlens_k"].copy_(cu_seqlens_k, False)

        # For speculative decoding
        if self.speculative_decoding:
            self.share_inputs["cu_seqlens_q_output"].copy_(cu_seqlens_q_output, False)
            self.share_inputs["batch_id_per_token_output"].copy_(batch_id_per_token_output, False)

            self._real_output_token_num_host.copy_(real_output_token_num, False)
            self.output_token_num_event.record()

        # Initialize forward meta data
        self.initialize_forward_meta(is_dummy_or_profile_run=is_dummy_or_profile_run)

        # Get sampling metadata
        self.sampling_metadata = SamplingMetadata(
            temperature=self.share_inputs["temperature"],
            top_p=self.share_inputs["top_p"],
            top_k=self.share_inputs["top_k"],
            top_k_list=self.share_inputs["top_k_list"],
            min_p=self.share_inputs["min_p"],
            min_p_list=self.share_inputs["min_p_list"],
            seed=self.share_inputs["infer_seed"],
            step_idx=self.share_inputs["step_idx"],
            token_ids_all=self.share_inputs["token_ids_all"],
            prompt_lens=self.share_inputs["prompt_lens"],
            frequency_penalties=self.share_inputs["frequency_score"],
            presence_penalties=self.share_inputs["presence_score"],
            repetition_penalties=self.share_inputs["penalty_score"],
            min_dec_lens=self.share_inputs["min_dec_len"],
            bad_words_token_ids=self.share_inputs["bad_tokens"],
            bad_words_token_len=self.share_inputs["bad_tokens_len"],
            eos_token_ids=self.share_inputs["eos_token_id"],
            max_num_logprobs=self.max_logprobs,
            enable_early_stop=self.enable_early_stop,
            stop_flags=self.share_inputs["stop_flags"],
            temp_scaled_logprobs_flag=self.temp_scaled_logprobs,
            top_p_normalized_logprobs_flag=self.top_p_normalized_logprobs,
            temp_scaled_logprobs=self.share_inputs["temp_scaled_logprobs"],
            top_p_normalized_logprobs=self.share_inputs["top_p_normalized_logprobs"],
            logits_processors=self.share_inputs["logits_processors"],
            share_inputs=self.share_inputs,
        )
        return token_num, token_num_event

    def _process_reorder(self) -> None:
        if self.attn_backends and getattr(self.attn_backends[0], "enable_ids_reorder", False):
            self.share_inputs.enable_pd_reorder = True
            self.share_inputs.condense()
            reorder_split_prefill_and_decode(input_batch=self.share_inputs)
            if self.speculative_decoding:
                if self.spec_method == SpecMethod.MTP:
                    self.proposer.reorder_inputs(self.share_inputs.index_to_batch_id)

    def load_model(self) -> None:
        """load or download model"""
        logger.info(f"Starting to load model {self.model_config.architectures[0]}")
        # 1. Load original model
        model_loader = get_model_loader(load_config=self.fd_config.load_config)
        self.model = model_loader.load_model(fd_config=self.fd_config)

        # 2. Load lora model

        # 3. Load drafter model(for speculative decoding)

        # 4. Init proposer for speculative method
        self._init_speculative_proposer()

        # Load RL dynamic model
        if self.fd_config.load_config.dynamic_load_weight:
            from fastdeploy.rl.dynamic_weight_manager import DynamicWeightManager

            if self.spec_method == SpecMethod.MTP:
                self.dynamic_weight_manager = DynamicWeightManager(
                    self.fd_config, [self.model, self.proposer.model], self.local_rank
                )
            else:
                self.dynamic_weight_manager = DynamicWeightManager(self.fd_config, self.model, self.local_rank)

    def get_model(self) -> nn.Layer:
        """Get current model"""
        return self.model

    def initialize_forward_meta(self, is_dummy_or_profile_run=False):
        """
        Initialize forward meta, attention meta data and update some config.
        """
        # Initialize forward meta
        routing_replay_table = None
        if self.routing_replay_manager is not None:
            routing_replay_table = self.routing_replay_manager.get_routing_table()
        self.forward_meta = ForwardMeta(
            ids_remove_padding=self.share_inputs["ids_remove_padding"],
            rotary_embs=self.share_inputs["rope_emb"],
            attn_backend=self.attn_backends[0],
            decoder_batch_ids=self.share_inputs["decoder_batch_ids"],
            decoder_tile_ids_per_batch=self.share_inputs["decoder_tile_ids_per_batch"],
            decoder_num_blocks_cpu=self.share_inputs["decoder_num_blocks_cpu"],
            # NOTE: (changwenbin) MLA kernel only needs decoder_num_blocks_device in place of GPU tensor,
            # adapted to cudagraph.
            decoder_num_blocks_device=self.share_inputs["decoder_num_blocks_device"],
            decoder_chunk_size_device=self.share_inputs["decoder_chunk_size_device"],
            max_len_tensor_cpu=self.share_inputs["max_len_tensor_cpu"],
            seq_lens_encoder=self.share_inputs["seq_lens_encoder"],
            seq_lens_decoder=self.share_inputs["seq_lens_decoder"],
            seq_lens_this_time=self.share_inputs["seq_lens_this_time"],
            batch_id_per_token=self.share_inputs["batch_id_per_token"],
            cu_seqlens_q=self.share_inputs["cu_seqlens_q"],
            cu_seqlens_k=self.share_inputs["cu_seqlens_k"],
            block_tables=self.share_inputs["block_tables"],
            caches=self.share_inputs["caches"],
            encoder_batch_ids=self.share_inputs["encoder_batch_ids"],
            encoder_tile_ids_per_batch=self.share_inputs["encoder_tile_ids_per_batch"],
            encoder_num_blocks_x_cpu=self.share_inputs["encoder_num_blocks_x_cpu"],
            kv_batch_ids=self.share_inputs["kv_batch_ids"],
            kv_tile_ids_per_batch=self.share_inputs["kv_tile_ids_per_batch"],
            kv_num_blocks_x_cpu=self.share_inputs["kv_num_blocks_x_cpu"],
            routing_replay_table=routing_replay_table,
        )

        dist_status = self.collect_distributed_status()

        if_only_decode = dist_status.if_only_decode
        if self.fd_config.parallel_config.enable_chunked_moe:
            self.forward_meta.max_moe_num_chunk = dist_status.max_moe_num_chunk

        only_decode_use_cudagraph = self.use_cudagraph and if_only_decode

        # Update config about moe for better performance
        # TODO(wanglongzhi):Modifying the config at runtime is not appropriate; it needs to be moved to forward_meta. It will be used in MoEMethodBase.apply()
        if self.fd_config.parallel_config.use_ep and self.fd_config.scheduler_config.splitwise_role == "mixed":
            self.fd_config.model_config.moe_phase.phase = "decode" if if_only_decode else "prefill"
            if self.speculative_decoding:
                self.proposer.fd_config.model_config.moe_phase.phase = "decode" if if_only_decode else "prefill"

        # Update Batch type for cuda graph for only_prefill_batch
        only_prefill_use_cudagraph = self.use_cudagraph and self.cudagraph_only_prefill and self.only_prefill()

        # When support capture both prefill-only and decode-only, this will use [only_prefill_use_cudagraph or only_decode_use_cudagraph]
        self.forward_meta.step_use_cudagraph = (
            only_prefill_use_cudagraph
            if self.cudagraph_only_prefill
            else only_decode_use_cudagraph and self.forward_meta.ids_remove_padding.shape[0] > 0
        )

        # Use static graph splitting to isolate incompatible operators from the CUDA Graph. This splits the graph into subgraphs, allowing Prefill, Decode, and Mixed Batches to run compatible parts via CUDA Graph.
        if (
            hasattr(self, "graph_opt_config")
            and self.use_cudagraph
            and self.graph_opt_config.graph_opt_level > 0
            and not self.graph_opt_config.full_cuda_graph
        ):
            self.forward_meta.step_use_cudagraph = True

        # Set forward_meta.is_dummy_or_profile_run to True to skip init_kv_signal_per_query for attention backends
        self.forward_meta.is_dummy_or_profile_run = is_dummy_or_profile_run

        # Initialzie attention meta data
        for attn_backend in self.attn_backends:
            attn_backend.init_attention_metadata(self.forward_meta)

        # for zero size
        self.forward_meta.is_zero_size = self.forward_meta.ids_remove_padding.shape[0] == 0
        self.forward_meta.exist_prefill = self.exist_prefill()

    def initialize_kv_cache(self, profile: bool = False) -> None:
        """
        Initialize kv cache
        """
        # cache_kvs = {}
        max_block_num = self.num_gpu_blocks

        # Get kv cache dtype
        cache_type = self.model_config.dtype
        kv_cache_quant_type = None

        # NOTE:(changwenbin) Determine whether it is Multi-Head Latent Attention,
        # To rationalize the allocation of kvcache.
        from fastdeploy import envs

        self.mla_cache = envs.FD_ATTENTION_BACKEND == "MLA_ATTN"
        self.dsa_cache = envs.FD_ATTENTION_BACKEND == "DSA_ATTN"

        if (
            self.quant_config
            and hasattr(self.quant_config, "kv_cache_quant_type")
            and self.quant_config.kv_cache_quant_type is not None
        ):
            cache_type = "uint8"
            kv_cache_quant_type = self.quant_config.kv_cache_quant_type
        # Get kv cache shape
        if self.dsa_cache:
            # Determine dsa cache quant type
            kv_cache_quant_type = "uint8"
            cache_type = "uint8"

            # NOTE(changwenbin) Get dsa cache shape.
            key_cache_shape, value_cache_shape, indexer_cache_shape = self.attn_backends[0].get_kv_cache_shape(
                max_num_blocks=max_block_num, kv_cache_quant_type=kv_cache_quant_type
            )
        else:
            key_cache_shape, value_cache_shape = self.attn_backends[0].get_kv_cache_shape(
                max_num_blocks=max_block_num, kv_cache_quant_type=kv_cache_quant_type
            )
            indexer_cache_shape = []
        if kv_cache_quant_type == "block_wise_fp8":
            kv_cache_scale_shape = [key_cache_shape[0], key_cache_shape[1], key_cache_shape[2]]
        local_rank = self.local_rank % self.parallel_config.tensor_parallel_size

        # Check if gpu runner needs to create kv cache
        # 1. During profiling, it creates its own kv cache.
        # 2. If no need to profile, create kv cache if cache managers do not exist.
        create_cache_tensor = profile or not (
            self.fd_config.cache_config.num_cpu_blocks > 0
            or self.fd_config.cache_config.kvcache_storage_backend
            or self.fd_config.scheduler_config.splitwise_role != "mixed"
        )

        cache_ready_signal = self.cache_ready_signal
        if not create_cache_tensor:
            logger.info(f"Waiting for cache managers to create kv cache.. {cache_ready_signal.value}")
            while cache_ready_signal.value[local_rank] != 1:
                time.sleep(1)
            logger.info(f"OK! Stop waiting. {cache_ready_signal.value}")

        logger.info(f"Initializing kv cache for all layers. {cache_ready_signal.value}")
        cache_kvs_list = []

        for i in range(self.model_config.num_hidden_layers):
            # init key cache
            key_cache_name = f"key_caches_{i}_rank{local_rank}.device{self.device_id}"
            key_cache_scales_name = f"key_cache_scales_{i}_rank{local_rank}.device{self.device_id}"
            if value_cache_shape:
                val_cache_name = f"value_caches_{i}_rank{local_rank}.device{self.device_id}"
                value_cache_scales_name = f"value_cache_scales_{i}_rank{local_rank}.device{self.device_id}"
            elif indexer_cache_shape:
                indexer_cache_name = f"indexer_caches_{i}_rank{local_rank}.device{self.device_id}"
            if create_cache_tensor:
                logger.info(
                    f"..creating kv cache for layer {i}: key:{key_cache_shape}, value:{value_cache_shape}, indexer:{indexer_cache_shape}"
                )
                key_cache = paddle.full(shape=key_cache_shape, fill_value=0, dtype=cache_type)
                set_data_ipc(key_cache, key_cache_name)
                self.cache_kvs_map[key_cache_name] = key_cache
                if value_cache_shape:
                    val_cache = paddle.full(shape=value_cache_shape, fill_value=0, dtype=cache_type)
                    set_data_ipc(val_cache, val_cache_name)
                    self.cache_kvs_map[val_cache_name] = val_cache
                    cache_kvs_list.extend([key_cache, val_cache])
                elif indexer_cache_shape:
                    indexer_cache = paddle.full(shape=indexer_cache_shape, fill_value=0, dtype=cache_type)
                    set_data_ipc(indexer_cache, indexer_cache_name)
                    self.cache_kvs_map[indexer_cache_name] = indexer_cache
                    cache_kvs_list.extend([key_cache, indexer_cache])
                else:
                    cache_kvs_list.extend([key_cache])
                if kv_cache_quant_type == "block_wise_fp8":
                    key_cache_scales = paddle.full(
                        shape=kv_cache_scale_shape, fill_value=0, dtype=paddle.get_default_dtype()
                    )
                    set_data_ipc(key_cache_scales, key_cache_scales_name)
                    self.cache_kvs_map[key_cache_scales_name] = key_cache_scales
                    if value_cache_shape:
                        val_cache_scales = paddle.full(
                            shape=kv_cache_scale_shape, fill_value=0, dtype=paddle.get_default_dtype()
                        )
                        set_data_ipc(val_cache_scales, value_cache_scales_name)
                        self.cache_kvs_map[value_cache_scales_name] = val_cache_scales
                        cache_kvs_list.extend([key_cache_scales, val_cache_scales])
                    else:
                        cache_kvs_list.extend([key_cache_scales])
            else:
                logger.info(
                    f"..attaching kv cache for layer {i}: key:{key_cache_shape}, value:{value_cache_shape}, indexer:{indexer_cache_shape}"
                )
                key_cache = paddle.empty(shape=[], dtype=cache_type)
                key_cache = share_external_data(key_cache, key_cache_name, key_cache_shape)
                self.cache_kvs_map[key_cache_name] = key_cache
                if kv_cache_quant_type == "block_wise_fp8":
                    key_cache_scales = paddle.empty(shape=[], dtype=paddle.get_default_dtype())
                    key_cache_scales = share_external_data(
                        key_cache_scales, key_cache_scales_name, kv_cache_scale_shape
                    )
                    self.cache_kvs_map[key_cache_scales_name] = key_cache_scales
                if value_cache_shape:
                    val_cache = paddle.empty(shape=[], dtype=cache_type)
                    val_cache = share_external_data(val_cache, val_cache_name, value_cache_shape)
                    self.cache_kvs_map[val_cache_name] = val_cache
                    cache_kvs_list.extend([key_cache, val_cache])
                    if kv_cache_quant_type == "block_wise_fp8":
                        val_cache_scales = paddle.empty(shape=[], dtype=paddle.get_default_dtype())
                        val_cache_scales = share_external_data(
                            val_cache_scales, value_cache_scales_name, kv_cache_scale_shape
                        )
                        self.cache_kvs_map[value_cache_scales_name] = val_cache_scales
                        cache_kvs_list.extend([key_cache_scales, val_cache_scales])
                elif indexer_cache_shape:
                    indexer_cache = paddle.empty(shape=[], dtype=cache_type)
                    indexer_cache = share_external_data(indexer_cache, indexer_cache_name, indexer_cache_shape)
                    self.cache_kvs_map[indexer_cache_name] = indexer_cache
                    cache_kvs_list.extend([key_cache, indexer_cache])
                else:
                    cache_kvs_list.extend([key_cache])
                    if kv_cache_quant_type == "block_wise_fp8":
                        cache_kvs_list.extend([key_cache_scales])

        self.share_inputs["caches"] = cache_kvs_list

        if not profile and create_cache_tensor:
            cache_ready_signal.value[local_rank] = 1
            logger.info(f"✅ kv cache is ready! {cache_ready_signal.value}")

        paddle.device.cuda.empty_cache()
        logger.info("kv cache is initialized!")

    def _initialize_attn_backend(self) -> None:
        """
        Initialize attention backends
        """
        assert len(self.attn_backends) == 0

        num_heads = self.model_config.num_attention_heads // self.parallel_config.tensor_parallel_size
        self.model_config.kv_num_heads = max(
            1,
            int(self.model_config.num_key_value_heads) // self.parallel_config.tensor_parallel_size,
        )
        head_dim = self.model_config.head_dim

        encoder_block_shape_q = 64
        decoder_block_shape_q = 16

        # Deterministic mode: use deterministic_split_kv_size to ensure batch-invariant attention
        if envs.FD_DETERMINISTIC_MODE:
            decoder_block_shape_q = envs.FD_DETERMINISTIC_SPLIT_KV_SIZE

        res_buffer = allocate_launch_related_buffer(
            max_batch_size=self.scheduler_config.max_num_seqs,
            max_model_len=self.model_config.max_model_len,
            encoder_block_shape_q=encoder_block_shape_q,
            decoder_block_shape_q=decoder_block_shape_q,
            decoder_step_token_num=self.speculative_config.num_speculative_tokens + 1,
            num_heads=num_heads,
            kv_num_heads=self.model_config.kv_num_heads,
            block_size=self.fd_config.cache_config.block_size,
        )
        self.share_inputs.update(res_buffer)

        # Get the attention backend
        attn_cls = get_attention_backend()
        attn_backend = attn_cls(
            self.fd_config,
            kv_num_heads=self.model_config.kv_num_heads,
            num_heads=num_heads,
            head_dim=head_dim,
            encoder_block_shape_q=encoder_block_shape_q,
            decoder_block_shape_q=decoder_block_shape_q,
        )

        self.attn_backends.append(attn_backend)

    def _dummy_pooler_run_task(
        self,
        hidden_states: paddle.Tensor,
        task: PoolingTask,
    ) -> PoolerOutput:
        num_tokens = hidden_states.shape[0]
        max_num_seqs = self.scheduler_config.max_num_seqs
        num_reqs = min(num_tokens, max_num_seqs)
        min_tokens_per_req = num_tokens // num_reqs
        num_scheduled_tokens_list = [min_tokens_per_req] * num_reqs
        num_scheduled_tokens_list[-1] += num_tokens % num_reqs
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs

        req_num_tokens = num_tokens // num_reqs
        dummy_prompt_lens = paddle.to_tensor(num_scheduled_tokens_list, dtype="int64", place=paddle.CPUPlace())
        dummy_token_ids = paddle.zeros([num_reqs, req_num_tokens], dtype="int64", device=hidden_states.place)
        model = cast(FdModelForPooling, self.get_model())
        dummy_pooling_params = PoolingParams(task=task)
        to_update = model.pooler.get_pooling_updates(task)
        to_update.apply(dummy_pooling_params)

        dummy_metadata = PoolingMetadata(
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
        dummy_metadata.build_pooling_cursor(num_scheduled_tokens_list, device=hidden_states.place)

        try:
            return model.pooler(hidden_states=hidden_states, pooling_metadata=dummy_metadata)
        except RuntimeError as e:
            if "out of memory" in str(e):
                raise RuntimeError(
                    "CUDA out of memory occurred when warming up pooler "
                    f"({task=}) with {num_reqs} dummy requests. Please try "
                    "lowering `max_num_seqs` or `gpu_memory_utilization` when "
                    "initializing the engine."
                ) from e
            else:
                raise e

    def _dummy_pooler_run(
        self,
        hidden_states: paddle.Tensor,
        model_output: paddle.Tensor,
    ) -> PoolerOutput:
        output_size = dict[PoolingTask, float]()
        for task in self.get_supported_pooling_tasks():

            output = self._dummy_pooler_run_task(hidden_states, task)
            output_size[task] = sum(o.numel() * o.element_size() if hasattr(o, "numel") else 0 for o in output)
            del output

        max_task = max(output_size.items(), key=lambda x: x[1])[0]
        pooler_output = self._dummy_pooler_run_task(hidden_states, max_task)

        model_output_data = ModelOutputData(
            next_tokens=self.share_inputs["next_tokens"],
            stop_flags=self.share_inputs["stop_flags"],
            step_idx=self.share_inputs["step_idx"],
            max_dec_len=self.share_inputs["max_dec_len"],
            seq_lens_this_time=self.share_inputs["seq_lens_this_time"],
            eos_token_id=self.share_inputs["eos_token_id"],
            not_need_stop=self.share_inputs["not_need_stop"],
            not_need_stop_device=self.share_inputs["not_need_stop_device"],
            input_ids=self.share_inputs["input_ids"],
            seq_lens_encoder=self.share_inputs["seq_lens_encoder"],
            seq_lens_decoder=self.share_inputs["seq_lens_decoder"],
            is_block_step=self.share_inputs["is_block_step"],
            full_hidden_states=model_output,
            msg_queue_id=self.parallel_config.msg_queue_id,
            mp_rank=self.parallel_config.tensor_parallel_rank,
            use_ep=self.parallel_config.use_ep,
            draft_tokens=(self.share_inputs["draft_tokens"] if self.speculative_decoding else None),
            actual_draft_token_num=(
                self.share_inputs["actual_draft_token_num"] if self.speculative_decoding else None
            ),
            accept_tokens=(self.share_inputs["accept_tokens"] if self.speculative_decoding else None),
            accept_num=(self.share_inputs["accept_num"] if self.speculative_decoding else None),
            token_ids_all=self.share_inputs["token_ids_all"],
            stop_token_ids=self.share_inputs["stop_seqs"],
            stop_seqs_len=self.share_inputs["stop_seqs_len"],
            min_tokens=self.share_inputs["min_dec_len"],
            prompt_lens=self.share_inputs["prompt_lens"],
            index_to_batch_id=self.share_inputs["index_to_batch_id"],
            enable_pd_reorder=getattr(self.share_inputs, "enable_pd_reorder", False),
        )

        post_process(
            sampler_or_pooler_output=pooler_output,
            model_output=model_output_data,
            share_inputs=self.share_inputs,
            sampling_metadata=self.sampling_metadata,
            block_size=self.cache_config.block_size,
            speculative_decoding=self.speculative_decoding,
            skip_save_output=True,
            async_output_queue=self.async_output_queue,
            think_end_id=self.model_config.think_end_id,
            splitwise_role_is_decode=self.scheduler_config.splitwise_role == "decode",
        )
        self.exist_prefill_flag = False
        return pooler_output

    def _dummy_sampler_run(
        self,
        hidden_states: paddle.Tensor,
        model_output: paddle.Tensor,
        batch_size: int,
        accept_all_drafts=False,
        reject_all_drafts=False,
    ) -> paddle.Tensor:
        logits = self.model.compute_logits(hidden_states)

        if not self.speculative_decoding:
            set_value_by_flags_and_idx(
                self.share_inputs["token_ids_all"],
                self.share_inputs["input_ids"],
                self.share_inputs["seq_lens_this_time"],
                self.share_inputs["seq_lens_encoder"],
                self.share_inputs["seq_lens_decoder"],
                self.share_inputs["prompt_lens"],
                self.share_inputs["step_idx"],
                self.share_inputs["stop_flags"],
            )
            sampler_output = self.sampler(logits, self.sampling_metadata)
            if self.parallel_config.tensor_parallel_size > 1:
                paddle.distributed.broadcast(
                    sampler_output.sampled_token_ids,
                    self.parallel_config.data_parallel_rank * self.parallel_config.tensor_parallel_size,
                    group=self.parallel_config.tp_group,
                )
        else:
            sampler_output = self.sampler(
                logits,
                self.sampling_metadata,
                self.model_config.max_model_len,
                self.share_inputs,
                int(self._real_output_token_num_host),
                self.increment_value,
                accept_all_drafts,
                reject_all_drafts,
            )
            if self.parallel_config.tensor_parallel_size > 1:
                paddle.distributed.broadcast(
                    self.share_inputs["accept_tokens"],
                    self.parallel_config.data_parallel_rank * self.parallel_config.tensor_parallel_size,
                    group=self.parallel_config.tp_group,
                )
                paddle.distributed.broadcast(
                    self.share_inputs["accept_num"],
                    self.parallel_config.data_parallel_rank * self.parallel_config.tensor_parallel_size,
                    group=self.parallel_config.tp_group,
                )
                paddle.distributed.broadcast(
                    self.share_inputs["step_idx"],
                    self.parallel_config.data_parallel_rank * self.parallel_config.tensor_parallel_size,
                    group=self.parallel_config.tp_group,
                )
                paddle.distributed.broadcast(
                    self.share_inputs["stop_flags"],
                    self.parallel_config.data_parallel_rank * self.parallel_config.tensor_parallel_size,
                    group=self.parallel_config.tp_group,
                )
        # 5. post process
        model_output_data = ModelOutputData(
            next_tokens=self.share_inputs["next_tokens"],
            stop_flags=self.share_inputs["stop_flags"],
            step_idx=self.share_inputs["step_idx"],
            max_dec_len=self.share_inputs["max_dec_len"],
            seq_lens_this_time=self.share_inputs["seq_lens_this_time"],
            eos_token_id=self.share_inputs["eos_token_id"],
            not_need_stop=self.share_inputs["not_need_stop"],
            not_need_stop_device=self.share_inputs["not_need_stop_device"],
            input_ids=self.share_inputs["input_ids"],
            seq_lens_encoder=self.share_inputs["seq_lens_encoder"],
            seq_lens_decoder=self.share_inputs["seq_lens_decoder"],
            is_block_step=self.share_inputs["is_block_step"],
            full_hidden_states=model_output,
            msg_queue_id=self.parallel_config.msg_queue_id,
            mp_rank=self.parallel_config.tensor_parallel_rank,
            use_ep=self.parallel_config.use_ep,
            draft_tokens=(self.share_inputs["draft_tokens"] if self.speculative_decoding else None),
            actual_draft_token_num=(
                self.share_inputs["actual_draft_token_num"] if self.speculative_decoding else None
            ),
            accept_tokens=(self.share_inputs["accept_tokens"] if self.speculative_decoding else None),
            accept_num=(self.share_inputs["accept_num"] if self.speculative_decoding else None),
            token_ids_all=self.share_inputs["token_ids_all"],
            stop_token_ids=self.share_inputs["stop_seqs"],
            stop_seqs_len=self.share_inputs["stop_seqs_len"],
            min_tokens=self.share_inputs["min_dec_len"],
            prompt_lens=self.share_inputs["prompt_lens"],
            mask_rollback=self.share_inputs["mask_rollback"],
            index_to_batch_id=self.share_inputs["index_to_batch_id"],
            enable_pd_reorder=getattr(self.share_inputs, "enable_pd_reorder", False),
        )

        post_process(
            sampler_or_pooler_output=sampler_output,
            model_output=model_output_data,
            share_inputs=self.share_inputs,
            sampling_metadata=self.sampling_metadata,
            block_size=self.cache_config.block_size,
            speculative_decoding=self.speculative_decoding,
            skip_save_output=True,
            async_output_queue=self.async_output_queue,
            think_end_id=self.model_config.think_end_id,
            splitwise_role_is_decode=self.scheduler_config.splitwise_role == "decode",
            enable_entropy=self.enable_entropy and self.parallel_config.tensor_parallel_rank == 0,
            is_naive_mode=(self.speculative_decoding and self.proposer is None),
            prefill_one_step_stop=self.parallel_config.prefill_one_step_stop,
        )
        self.exist_prefill_flag = False
        if self.speculative_decoding:
            if self.spec_method == SpecMethod.MTP:
                self.proposer.run(
                    full_hidden_states=model_output,
                    step_use_cudagraph=self.forward_meta.step_use_cudagraph,
                    is_dummy_run=True,
                )
            elif self.spec_method == SpecMethod.NAIVE:
                pass
            else:
                self.proposer.prepare_dummy_speculative_drafts(share_inputs=self.share_inputs, batch_size=batch_size)
        return sampler_output

    def _dummy_run(
        self,
        num_tokens: int,
        batch_size: int,
        expected_decode_len: int = 1,
        in_capturing: bool = False,
        capture_prefill: bool = False,
        accept_all_drafts: bool = False,
        reject_all_drafts: bool = False,
    ) -> paddle.Tensor:
        """
        Use dummy inputs to run before formal execution.
        Args:
            num_tokens:
            expected_decode_len: Expected number of tokens generated
            in_capturing: Is cuda graph in capturing state
            capture_prefill: Capture pure prefill for cuda graph
            accept_all_drafts: Target model will accept all draft tokens
            reject_all_drafts: Target model will reject all draft tokens
        """
        input_length_list, max_dec_len_list, block_num = self.get_input_length_list(
            num_tokens=num_tokens,
            batch_size=batch_size,
            expected_decode_len=expected_decode_len,
            capture_prefill=capture_prefill,
        )
        self._dummy_prefill_inputs(
            input_length_list=input_length_list,
            max_dec_len_list=max_dec_len_list,
            block_num=block_num,
        )
        if self.spec_method == SpecMethod.MTP:
            self.proposer.dummy_prefill_inputs(
                num_tokens=num_tokens,
                batch_size=batch_size,
                expected_decode_len=expected_decode_len,
            )

        while True:
            # 1. Initialize forward meta and attention meta data
            self._prepare_inputs(is_dummy_or_profile_run=True)
            # 2. Padding inputs for cuda graph
            self.forward_meta.step_use_cudagraph = in_capturing and self.forward_meta.step_use_cudagraph
            self.padding_cudagraph_inputs()

            if self.enable_mm:
                model_output = self.model(
                    self.forward_meta.ids_remove_padding,
                    self.share_inputs["image_features"],
                    self.forward_meta,
                )
            else:
                # fallback paddleformers use cuda graph need kwargs
                model_output = self.model(
                    ids_remove_padding=self.forward_meta.ids_remove_padding,
                    forward_meta=self.forward_meta,
                )
            if self.use_cudagraph:
                model_output = model_output[: self.real_token_num]

            if self.is_pooling_model:
                self._dummy_pooler_run(model_output, model_output)
                break
            else:
                if self.speculative_decoding:
                    self.output_token_num_event.synchronize()
                    real_num = int(self._real_output_token_num_host)
                    real_batch_id_per_token_output = self.share_inputs["batch_id_per_token_output"][:real_num]
                else:
                    real_batch_id_per_token_output = None
                hidden_states = rebuild_padding(
                    model_output,
                    self.share_inputs["cu_seqlens_q"],
                    self.share_inputs["seq_lens_this_time"],
                    self.share_inputs["seq_lens_decoder"],
                    self.share_inputs["seq_lens_encoder"],
                    real_batch_id_per_token_output,
                    (self.share_inputs["cu_seqlens_q_output"] if self.speculative_decoding else None),
                )
                self._dummy_sampler_run(hidden_states, model_output, batch_size, accept_all_drafts, reject_all_drafts)

            # 7. Updata 'infer_seed' and step_cuda()
            if not self.speculative_decoding:
                self.share_inputs["infer_seed"].add_(self.infer_seed_increment)
                self.share_inputs["infer_seed"][:] %= self.MAX_INFER_SEED
            if int((self.share_inputs["seq_lens_this_time"] > 0).sum()) == 0:
                break

            if capture_prefill and self.graph_opt_config.graph_opt_level > 0:
                # only need to capture prefill
                break

    @sot_warmup_guard(True)
    def capture_model(self) -> None:
        """
        Trigger CUDA Graph capture for all shapes in cuda graph capture list
        """
        if not self.use_cudagraph:
            logger.info("Skipping CUDA graph capture. Please check GraphOptimizationConfig")
            return
        time_before_capture = time.perf_counter()
        expected_decode_len = 1
        capture_sizes = self.cudagraph_capture_sizes.copy()
        try:
            if self.fd_config.graph_opt_config.cudagraph_only_prefill:
                for num_tokens in sorted(capture_sizes, reverse=True):
                    self._dummy_run(
                        num_tokens=num_tokens,
                        batch_size=self.scheduler_config.max_num_seqs,
                        in_capturing=True,
                        expected_decode_len=expected_decode_len,
                        capture_prefill=True,
                    )
                    logger.info(
                        f"Warm up the model with the num_tokens:{num_tokens}, expected_decode_len:{expected_decode_len}"
                    )
            elif self.speculative_decoding and self.spec_method == SpecMethod.MTP:
                # Capture Target Model without bsz 1
                for capture_size in sorted(capture_sizes, reverse=True):
                    expected_decode_len = self.speculative_config.num_speculative_tokens * 2 + 1
                    self._dummy_run(
                        num_tokens=self.fd_config.get_max_chunk_tokens(),
                        batch_size=int(capture_size / (self.speculative_config.num_speculative_tokens + 1)),
                        in_capturing=True,
                        expected_decode_len=expected_decode_len,
                        accept_all_drafts=True,
                    )
                    logger.info(
                        f"Warm up the model with the num_tokens:{capture_size}, expected_decode_len:{expected_decode_len}"
                    )
            else:
                for batch_size in sorted(capture_sizes, reverse=True):
                    self._dummy_run(
                        num_tokens=self.fd_config.get_max_chunk_tokens(),
                        batch_size=batch_size,
                        in_capturing=True,
                        expected_decode_len=expected_decode_len,
                    )
                    logger.info(
                        f"Warm up the model with the batch size:{batch_size}, num tokens:{expected_decode_len}"
                    )
        except RuntimeError as e:
            if "out of memory" in str(e):
                raise RuntimeError(
                    "CUDA out of memory occurred when warming up CUDAGraph "
                    f"with the capture sizes {capture_sizes}. Please try "
                    "lowering `max_num_seqs` or `gpu_memory_utilization` when "
                    "initializing the engine."
                ) from e
            if "CUDA error(700)" in str(e):
                raise RuntimeError(
                    "CUDA error(700), an illegal memory access was encountered, "
                    "when warming up CUDAGraph. Please try to set the startup parameter: "
                    "--graph-optimization-config '{\"use_cudagraph\": false}' to close CUDAGraph"
                ) from e
            else:
                raise e

        time_after_capture = time.perf_counter()
        logger.info(f"Cuda Graph capturing took {time_after_capture - time_before_capture} seconds")

    @sot_warmup_guard(True)
    def capture_model_prefill_and_mixed(self) -> None:
        """
        Trigger CUDA Graph capture for prefill/mixed phase in static split graph mode.
        """
        if not self.use_cudagraph:
            logger.info("Skipping CUDA graph capture. Please check GraphOptimizationConfig")
            return
        time_before_capture = time.perf_counter()
        capture_sizes = self.cudagraph_capture_sizes_prefill.copy()
        for capture_size in sorted(capture_sizes, reverse=True):
            self._dummy_run(
                num_tokens=capture_size,
                batch_size=1,
                in_capturing=True,
                expected_decode_len=1,
                capture_prefill=True,
            )
            logger.info(f"Warm up the model (prefill/mixed) with num_tokens:{capture_size}")
        time_after_capture = time.perf_counter()
        logger.info(
            f"Cuda Graph capturing (Prefill and Mixed) took {time_after_capture - time_before_capture} seconds"
        )

    def vision_encoder_compile(self):
        if self.graph_opt_config.graph_opt_level == 0:
            return
        # Currently only PaddleOCR-VL model is supported for vision encoder layer
        if self.model_config.model_type != "paddleocr_vl":
            return

        # Compile for paddleocr_vl vision encoder layer
        def apply_compile(fn):
            backend = "CINN" if self.graph_opt_config.graph_opt_level >= 2 else None
            return paddle.jit.to_static(
                fn,
                full_graph=False,
                backend=backend,
            )

        from fastdeploy.model_executor.models.paddleocr_vl.siglip import SiglipEncoder

        SiglipEncoder._run_encoder_layer = apply_compile(SiglipEncoder._run_encoder_layer)

        # Warmup for paddleocr_vl vision encoder layer
        logger.info(f"Warmup for {self.model_config.model_type} compile...")
        self._dummy_run_extract_vision_features()

    @sot_warmup_guard(True)
    def sot_warmup(self) -> None:
        start_time = time.perf_counter()
        for batch_size in self.sot_warmup_sizes:
            self._dummy_run(
                num_tokens=self.fd_config.get_max_chunk_tokens(),
                batch_size=batch_size,
            )
            logger.info(f"SOT warmup the model with the batch size:{batch_size}")
        logger.info(f"SOT warmup took {time.perf_counter() - start_time} seconds")

    def _get_p_done_idxs_gd(self, model_forward_batch: Optional[List[Request]], num_running_requests: int):
        """
        Get indices for guided decoding.
        When Prefill is done, async compiled logits_processor must be joined.
        """
        if self.guided_backend is None:
            return []

        prefill_done_idxs = []
        for idx in range(0, num_running_requests):
            batch_id = self.share_inputs.get_index_by_batch_id(idx)
            if self.share_inputs["step_idx"][batch_id] == 0:
                prefill_done_idxs.append(batch_id)

        if model_forward_batch is None:
            return prefill_done_idxs

        for task in model_forward_batch:
            if task.task_type.value != RequestType.PREFILL.value:
                continue
            # in chunk prefill
            if self.cache_config.enable_chunked_prefill:
                if hasattr(task, "prefill_end_index") and hasattr(task, "prompt_token_ids"):
                    task_idx = self.share_inputs.get_index_by_batch_id(task.idx)
                    if len(task.prompt_token_ids) > task.prefill_end_index and task_idx in prefill_done_idxs:
                        prefill_done_idxs.remove(task_idx)

        return prefill_done_idxs

    def execute_model(
        self,
        model_forward_batch: Optional[List[Request]] = None,
        num_running_requests: int = None,
    ) -> None:
        """
        The Entrance of model execute.
        Args:
            model_forward_batch: 'Request' contains information related to prompt and is an abstract
            class at the server level, which is too granular for ModelRunner.
            We plan to replace it with 'ModelForwardBatch'.
            intermediate_tensors:
            num_running_requests: batch_size
        """
        if not self.enable_overlap_schedule:
            self.execute_model_normal(model_forward_batch, num_running_requests)
        else:
            self.execute_model_overlap(model_forward_batch, num_running_requests)

    def execute_model_normal(
        self,
        model_forward_batch: Optional[List[Request]] = None,
        num_running_requests: int = None,
    ) -> None:
        model_output, p_done_idxs, _ = self._preprocess_and_execute_model(model_forward_batch, num_running_requests)
        if model_output is None:
            return

        model_output_data, sampler_output, post_process_event = self._postprocess(
            model_output, p_done_idxs, model_forward_batch, num_running_requests
        )
        if model_output_data is not None and not self.speculative_decoding:
            # synchronizes the async DtoH copies of sampled_token_ids.
            post_process_event.synchronize()
            self._save_model_output(model_output_data, sampler_output)

    def execute_model_overlap(
        self,
        model_forward_batch: Optional[List[Request]] = None,
        num_running_requests: int = None,
    ) -> None:
        # preprocess and execute model (current batch)
        model_output, p_done_idxs, token_num_event = self._preprocess_and_execute_model(
            model_forward_batch, num_running_requests, self._cached_launch_token_num
        )

        # save output (last batch)
        if self._cached_model_output_data is not None:
            # synchronizes the async DtoH copies of sampled_token_ids.
            self._cached_post_process_event.synchronize()
            self._save_model_output(
                self._cached_model_output_data,
                self._cached_sampler_output,
            )

        # postprocess (current batch)
        # synchronizes the async DtoH copies of seq_lens_this_time_cpu and is_block_step_cpu,
        # ensuring that the token count for the current batch is ready to be computed and reused in the subsequent batch.
        token_num_event.synchronize()
        next_launch_token_num = self._predict_next_launch_token_num()
        if self.share_inputs["seq_lens_this_time_cpu"].numpy().sum().item() > 0 and model_output is not None:
            model_output_data, sampler_output, post_process_event = self._postprocess(
                model_output, p_done_idxs, model_forward_batch, num_running_requests
            )
            self._cached_model_output_data = model_output_data
            self._cached_sampler_output = sampler_output
            self._cached_post_process_event = post_process_event
        else:
            self._cached_model_output_data = None
            self._cached_sampler_output = None
            self._cached_post_process_event = None
        self._cached_launch_token_num = next_launch_token_num

    def _preprocess_and_execute_model(
        self,
        model_forward_batch: Optional[List[Request]] = None,
        num_running_requests: int = None,
        cached_token_num: int = -1,
    ) -> None:
        if self.deterministic_logger is not None:
            self.deterministic_logger.log_batch_start(model_forward_batch)

        # Reorder inputs to split prefill and decode tokens
        self._process_reorder()

        # 1. Prepare inputs of model and sampler.
        current_launch_token_num, token_num_event = self._prepare_inputs(cached_token_num)
        self.current_launch_token_num = current_launch_token_num

        # NOTE(sunxin):
        # If current_launch_token_num is 0, it means the current worker is in an idle state,
        # and no further processing is required in TP mode.
        # However, in EP (Expert Parallelism) mode, there is data on other runner,
        # the current runner is required to execute part of the model.
        if current_launch_token_num == 0 and not self.parallel_config.use_ep:
            return None, None, token_num_event

        p_done_idxs = self._get_p_done_idxs_gd(model_forward_batch, num_running_requests)
        self.sampler.pre_process(p_done_idxs)
        if self.fd_config.routing_replay_config.enable_routing_replay:
            self.routing_replay_manager.pending_update_positions = self.routing_replay_manager.get_token_positions(
                seq_lens_decoder=self.share_inputs["seq_lens_decoder"],
                seq_lens_this_time=self.share_inputs["seq_lens_this_time_buffer"],
            )

        # 1.1 Update state of logits processor
        for proc in self.sampling_metadata.logits_processors:
            proc.update_state(self.share_inputs)

        # 2. Padding inputs for cuda graph
        self.padding_cudagraph_inputs()
        # 3. Execute model
        if self.enable_mm:
            model_output = self.model(
                self.forward_meta.ids_remove_padding,
                self.share_inputs["image_features"],
                self.forward_meta,
            )
        else:
            model_output = self.model(
                ids_remove_padding=self.forward_meta.ids_remove_padding,
                forward_meta=self.forward_meta,
            )
        if self.use_cudagraph:
            model_output = model_output[: self.real_token_num]
        return model_output, p_done_idxs, token_num_event

    def _postprocess(
        self,
        model_output: paddle.Tensor,
        p_done_idxs: List[int],
        model_forward_batch: Optional[List[Request]] = None,
        num_running_requests: int = None,
    ) -> None:

        if self.speculative_decoding:
            self.output_token_num_event.synchronize()
            real_num = int(self._real_output_token_num_host)
            real_batch_id_per_token_output = self.share_inputs["batch_id_per_token_output"][:real_num]

        prompt_logprobs_list = self._get_prompt_logprobs_list(model_output)
        if self.is_pooling_model:
            pooler_output = self._pool(model_output, num_running_requests)

            model_output_data = ModelOutputData(
                next_tokens=self.share_inputs["next_tokens"],
                stop_flags=self.share_inputs["stop_flags"],
                step_idx=self.share_inputs["step_idx"],
                max_dec_len=self.share_inputs["max_dec_len"],
                seq_lens_this_time=self.share_inputs["seq_lens_this_time"],
                eos_token_id=self.share_inputs["eos_token_id"],
                not_need_stop=self.share_inputs["not_need_stop"],
                not_need_stop_device=self.share_inputs["not_need_stop_device"],
                input_ids=self.share_inputs["input_ids"],
                seq_lens_encoder=self.share_inputs["seq_lens_encoder"],
                seq_lens_decoder=self.share_inputs["seq_lens_decoder"],
                is_block_step=self.share_inputs["is_block_step"],
                full_hidden_states=model_output,
                msg_queue_id=self.parallel_config.msg_queue_id,
                mp_rank=self.parallel_config.tensor_parallel_rank,
                use_ep=self.parallel_config.use_ep,
                draft_tokens=(self.share_inputs["draft_tokens"] if self.speculative_decoding else None),
                actual_draft_token_num=(
                    self.share_inputs["actual_draft_token_num"] if self.speculative_decoding else None
                ),
                accept_tokens=(self.share_inputs["accept_tokens"] if self.speculative_decoding else None),
                accept_num=(self.share_inputs["accept_num"] if self.speculative_decoding else None),
                token_ids_all=self.share_inputs["token_ids_all"],
                stop_token_ids=self.share_inputs["stop_seqs"],
                stop_seqs_len=self.share_inputs["stop_seqs_len"],
                min_tokens=self.share_inputs["min_dec_len"],
                prompt_lens=self.share_inputs["prompt_lens"],
                index_to_batch_id=self.share_inputs["index_to_batch_id"],
                enable_pd_reorder=getattr(self.share_inputs, "enable_pd_reorder", False),
            )

            post_process(
                sampler_or_pooler_output=pooler_output,
                model_output=model_output_data,
                share_inputs=self.share_inputs,
                sampling_metadata=self.sampling_metadata,
                block_size=self.cache_config.block_size,
                save_each_rank=self.parallel_config.use_ep,
                speculative_decoding=self.speculative_decoding,
                skip_save_output=False,
                async_output_queue=self.async_output_queue,
                enable_entropy=self.enable_entropy and self.parallel_config.tensor_parallel_rank == 0,
                routing_replay_manager=self.routing_replay_manager,
            )

            return None, None, None
        else:
            hidden_states = rebuild_padding(
                model_output,
                self.share_inputs["cu_seqlens_q"],
                self.share_inputs["seq_lens_this_time"],
                self.share_inputs["seq_lens_decoder"],
                self.share_inputs["seq_lens_encoder"],
                (real_batch_id_per_token_output if self.speculative_decoding else None),
                (self.share_inputs["cu_seqlens_q_output"] if self.speculative_decoding else None),
            )

            # 4. Compute logits, Sample
            if self.deterministic_logger is not None:
                # Log MD5 of hidden_states (model output)
                self.deterministic_logger.log_tensor_md5s(
                    {"hidden_states": hidden_states},
                    forward_batch_reqs_list=self.forward_batch_reqs_list,
                    stage="hidden_states",
                )

            logits = self.model.compute_logits(hidden_states)

            if self.deterministic_logger is not None:
                # Log MD5 of logits (before sampling)
                self.deterministic_logger.log_tensor_md5s(
                    {"logits": logits}, forward_batch_reqs_list=self.forward_batch_reqs_list, stage="logits"
                )

            if not self.speculative_decoding:
                set_value_by_flags_and_idx(
                    self.share_inputs["token_ids_all"],
                    self.share_inputs["input_ids"],
                    self.share_inputs["seq_lens_this_time"],
                    self.share_inputs["seq_lens_encoder"],
                    self.share_inputs["seq_lens_decoder"],
                    self.share_inputs["prompt_lens"],
                    self.share_inputs["step_idx"],
                    self.share_inputs["stop_flags"],
                )
                sampler_output = self.sampler(
                    logits,
                    self.sampling_metadata,
                    p_done_idxs,
                )

                if self.deterministic_logger is not None:
                    # Log MD5 of sampling results
                    self.deterministic_logger.log_tensor_md5s(
                        {"sampled_token_ids": sampler_output.sampled_token_ids},
                        forward_batch_reqs_list=self.forward_batch_reqs_list,
                        stage="sampled_tokens",
                    )

                if (
                    self.enable_logprob
                    and not envs.FD_USE_GET_SAVE_OUTPUT_V1
                    and sampler_output.logprobs_tensors is None
                ):
                    sampler_output.logprobs_tensors = LogprobsTensors(
                        logprob_token_ids=sampler_output.sampled_token_ids,
                        logprobs=paddle.empty_like(sampler_output.sampled_token_ids, device="cpu", dtype="float32"),
                        selected_token_ranks=paddle.empty(
                            [sampler_output.sampled_token_ids.shape[0]], device="cpu", dtype="int64"
                        ),
                    )
                if self.parallel_config.tensor_parallel_size > 1:
                    paddle.distributed.broadcast(
                        sampler_output.sampled_token_ids,
                        self.parallel_config.data_parallel_rank * self.parallel_config.tensor_parallel_size,
                        group=self.parallel_config.tp_group,
                    )
            else:
                sampler_output = self.sampler(
                    logits,
                    self.sampling_metadata,
                    self.model_config.max_model_len,
                    self.share_inputs,
                    int(self._real_output_token_num_host),
                    self.increment_value,
                )
                if self.parallel_config.tensor_parallel_size > 1:
                    paddle.distributed.broadcast(
                        self.share_inputs["accept_tokens"],
                        self.parallel_config.data_parallel_rank * self.parallel_config.tensor_parallel_size,
                        group=self.parallel_config.tp_group,
                    )
                    paddle.distributed.broadcast(
                        self.share_inputs["accept_num"],
                        self.parallel_config.data_parallel_rank * self.parallel_config.tensor_parallel_size,
                        group=self.parallel_config.tp_group,
                    )
                    paddle.distributed.broadcast(
                        self.share_inputs["step_idx"],
                        self.parallel_config.data_parallel_rank * self.parallel_config.tensor_parallel_size,
                        group=self.parallel_config.tp_group,
                    )
                    paddle.distributed.broadcast(
                        self.share_inputs["stop_flags"],
                        self.parallel_config.data_parallel_rank * self.parallel_config.tensor_parallel_size,
                        group=self.parallel_config.tp_group,
                    )
            # 5. Post Process
            model_output_data = ModelOutputData(
                next_tokens=self.share_inputs["next_tokens"],
                stop_flags=self.share_inputs["stop_flags"],
                step_idx=self.share_inputs["step_idx"],
                max_dec_len=self.share_inputs["max_dec_len"],
                seq_lens_this_time=self.share_inputs["seq_lens_this_time"],
                eos_token_id=self.share_inputs["eos_token_id"],
                not_need_stop=self.share_inputs["not_need_stop"],
                not_need_stop_device=self.share_inputs["not_need_stop_device"],
                input_ids=self.share_inputs["input_ids"],
                seq_lens_encoder=self.share_inputs["seq_lens_encoder"],
                seq_lens_decoder=self.share_inputs["seq_lens_decoder"],
                is_block_step=self.share_inputs["is_block_step"],
                full_hidden_states=model_output,
                msg_queue_id=self.parallel_config.msg_queue_id,
                mp_rank=self.parallel_config.tensor_parallel_rank,
                use_ep=self.parallel_config.use_ep,
                draft_tokens=(self.share_inputs["draft_tokens"] if self.speculative_decoding else None),
                actual_draft_token_num=(
                    self.share_inputs["actual_draft_token_num"] if self.speculative_decoding else None
                ),
                token_ids_all=self.share_inputs["token_ids_all"],
                accept_tokens=(self.share_inputs["accept_tokens"] if self.speculative_decoding else None),
                accept_num=(self.share_inputs["accept_num"] if self.speculative_decoding else None),
                stop_token_ids=self.share_inputs["stop_seqs"],
                stop_seqs_len=self.share_inputs["stop_seqs_len"],
                min_tokens=self.share_inputs["min_dec_len"],
                prompt_lens=self.share_inputs["prompt_lens"],
                mask_rollback=self.share_inputs["mask_rollback"],
                prompt_logprobs_list=prompt_logprobs_list,
                index_to_batch_id=self.share_inputs["index_to_batch_id"],
                enable_pd_reorder=getattr(self.share_inputs, "enable_pd_reorder", False),
            )

            if self.spec_method == SpecMethod.MTP and self.scheduler_config.splitwise_role == "prefill":
                skip_save_output = True
            else:
                skip_save_output = False

            post_process(
                sampler_or_pooler_output=sampler_output,
                model_output=model_output_data,
                share_inputs=self.share_inputs,
                sampling_metadata=self.sampling_metadata,
                block_size=self.cache_config.block_size,
                save_each_rank=self.parallel_config.use_ep,
                speculative_decoding=self.speculative_decoding,
                skip_save_output=skip_save_output,
                async_output_queue=self.async_output_queue,
                think_end_id=self.model_config.think_end_id,
                splitwise_role_is_decode=self.scheduler_config.splitwise_role == "decode",
                enable_entropy=self.enable_entropy and self.parallel_config.tensor_parallel_rank == 0,
                is_naive_mode=(self.speculative_decoding and self.proposer is None),
                prefill_one_step_stop=self.parallel_config.prefill_one_step_stop,
                routing_replay_manager=self.routing_replay_manager,
            )

            if self.guided_backend is not None and sampler_output is not None:
                self.sampler.post_process(sampler_output.sampled_token_ids)

            # 6. Speculative decode -- proposer run (method="naive" has proposer=None, skip)
            # For naive mode: seq_lens_this_time is already reset to 1 inside
            # unified_update_model_status kernel. For MTP/Ngram, the proposer
            # will overwrite it with (draft_count + 1) below.

            if self.speculative_decoding and self.proposer is not None:
                if self.spec_method == SpecMethod.MTP:
                    self.proposer.run(
                        full_hidden_states=model_output, step_use_cudagraph=self.forward_meta.step_use_cudagraph
                    )
                elif self.spec_method == SpecMethod.NAIVE:
                    pass
                else:
                    self.proposer.run(share_inputs=self.share_inputs)

            # 7. Update 'infer_seed' and step_cuda()
            if not self.speculative_decoding:
                self.share_inputs["infer_seed"].add_(self.infer_seed_increment)
                self.share_inputs["infer_seed"][:] %= self.MAX_INFER_SEED
            if self.speculative_decoding:
                speculate_schedule_cache(
                    self.share_inputs["draft_tokens"],
                    self.share_inputs["block_tables"],
                    self.share_inputs["stop_flags"],
                    self.share_inputs["prompt_lens"],
                    self.share_inputs["seq_lens_this_time"],
                    self.share_inputs["seq_lens_encoder"],
                    self.share_inputs["seq_lens_decoder"],
                    self.share_inputs["step_seq_lens_decoder"],
                    self.share_inputs["step_draft_tokens"],
                    self.share_inputs["step_seq_lens_this_time"],
                    self.share_inputs["accept_num"],
                    self.share_inputs["accept_tokens"],
                    self.share_inputs["is_block_step"],
                    self.share_inputs["not_need_stop"],
                    self.cache_config.block_size,
                    self.speculative_config.num_speculative_tokens,
                )

            # 8. Async cpy
            post_process_event = paddle.device.cuda.create_event()
            if not self.speculative_decoding:
                self.share_inputs["sampled_token_ids"].copy_(sampler_output.sampled_token_ids, False)
                post_process_event.record()

        self.exist_prefill_flag = False
        return model_output_data, sampler_output, post_process_event

    def _save_model_output(
        self,
        model_output_data,
        sampler_output,
    ):
        save_output_normal(
            model_output=model_output_data,
            sampler_output=sampler_output,
            share_inputs=self.share_inputs,
            async_output_queue=self.async_output_queue,
            save_each_rank=self.parallel_config.use_ep,
        )

    def _pool(self, hidden_states: paddle.Tensor, num_running_requests: int) -> Optional[ModelRunnerOutput]:
        num_scheduled_tokens = int(self.share_inputs["seq_lens_this_time"][:num_running_requests].sum())
        hidden_states = hidden_states[:num_scheduled_tokens]

        prompt_lens = self.share_inputs["prompt_lens"][:num_running_requests]
        prompt_token_ids = self.share_inputs["token_ids_all"]

        pooling_metadata = PoolingMetadata(
            prompt_lens=prompt_lens,
            prompt_token_ids=prompt_token_ids,
            pooling_params=self.pooling_params,
        )

        num_scheduled_tokens_list = [
            int(self.share_inputs["seq_lens_this_time"][i]) for i in range(num_running_requests)
        ]

        device_str = "gpu" if hidden_states.place.is_gpu_place() else "cpu"
        pooling_metadata.build_pooling_cursor(num_scheduled_tokens_list, device=device_str)

        raw_pooler_output = self.model.pooler(hidden_states=hidden_states, pooling_metadata=pooling_metadata)

        seq_lens_decoder = self.share_inputs["seq_lens_decoder"][:num_running_requests]
        seq_lens_encoder = self.share_inputs["seq_lens_encoder"][:num_running_requests]

        pooler_output: list[Optional[paddle.Tensor]] = []
        pooler_output_idx = 0

        for i, prompt_len in enumerate(pooling_metadata.prompt_lens):
            current_seq_len = num_scheduled_tokens_list[i]

            if current_seq_len == 0:
                pooler_output.append(None)
                continue

            total_processed = int(seq_lens_decoder[i]) + int(seq_lens_encoder[i])

            if total_processed == int(prompt_len):
                output = raw_pooler_output[pooler_output_idx]
            else:
                output = None

            pooler_output.append(output)
            pooler_output_idx += 1

        return PoolerOutput(outputs=pooler_output)

    def _execute_empty_input(self, forward_meta) -> None:
        """
        In certain scenarios, such as during EP,
        the runner needs to execute partial modules of the model without input data.
        This requires the model to implement the `empty_input_forward` method.
        """
        if hasattr(self.model, "empty_input_forward"):
            self.model.empty_input_forward(forward_meta)
        else:
            raise ValueError(f"{type(self.model)} has no attribute 'empty_input_forward")

    @profile_run_guard(True)
    def profile_run(self) -> None:
        """Execute a forward pass with dummy inputs to profile the memory usage of the model"""
        # Initialize kv cache for profile run. After profile run kv cache will be reset.
        # TODO(gongshaotian): Optimize the management logic of kvcache
        self.num_gpu_blocks = self.cache_config.total_block_num
        self.initialize_kv_cache(profile=True)
        if self.spec_method == SpecMethod.MTP:
            self.proposer.initialize_kv_cache(main_model_num_blocks=self.num_gpu_blocks, profile=True)

        # 1. Profile with multimodal encoder & encoder cache

        # 2. Dummy run
        num_tokens = self.fd_config.get_max_chunk_tokens()
        logger.info(
            f"Dummy run with {num_tokens} tokens, mm_max_tokens_per_item: {self.model_config.mm_max_tokens_per_item}"
        )
        self._dummy_run(
            num_tokens=num_tokens,
            batch_size=self.scheduler_config.max_num_seqs,
        )

        # 3. gc
        if self.spec_method == SpecMethod.MTP:
            self.proposer.clear_mtp_cache(profile=True)
        self.clear_cache(profile=True)

    def update_share_input_block_num(self, num_gpu_blocks: int) -> None:
        """
        Set a globally unified block number and update the model's shared input.
        Args:
            num_gpu_blocks:
        """
        self.num_gpu_blocks = num_gpu_blocks

        # Reset block table and kv cache with global block num
        self.initialize_kv_cache()

        # Reset free list
        free_list = list(
            range(
                self.num_gpu_blocks - 1,
                int(self.num_gpu_blocks * self.cache_config.kv_cache_ratio) - 1,
                -1,
            )
        )
        self.free_list_len = len(free_list)
        self.share_inputs.update(
            {
                "free_list": paddle.to_tensor(free_list, dtype="int32"),
                "free_list_len": paddle.full([1], self.free_list_len, dtype="int32"),
            }
        )

        if self.spec_method == SpecMethod.MTP:
            self.proposer.update_mtp_block_num(num_gpu_blocks)

    def cal_theortical_kvcache(self):
        """
        Calculate the total block memory required at the model level
        TODO(gongshaotian): Move to Attention Backend
        """
        """
        Byte of dtype:
        - default(bf16): 2
        - cache_int8: 1
        - cache_int4:
        """
        cache_quant_dtype = None
        if (
            self.quant_config
            and hasattr(self.quant_config, "kv_cache_quant_type")
            and self.quant_config.kv_cache_quant_type is not None
        ):
            cache_quant_dtype = self.quant_config.kv_cache_quant_type

        if cache_quant_dtype is not None:  # int8, int8_zp, fp8, fp8_zp
            byte_of_dtype = 1
        else:  # default
            byte_of_dtype = 2

        hidden_dim = self.model_config.head_dim * self.model_config.kv_num_heads
        # NOTE(liuzichang): Implement multi-layer MTP architecture in the future
        num_layers = (
            self.model_config.num_hidden_layers + self.speculative_config.num_gpu_block_expand_ratio
            if self.spec_method == SpecMethod.MTP
            else self.model_config.num_hidden_layers
        )

        # NOTE:(changwenbin) Determie whether it is Multi-Head Latent Attention,
        # To rationalize the allocation of kvcache.
        if self.fd_config.cache_config.use_mla_cache:
            required_memory = (
                byte_of_dtype
                * (self.fd_config.model_config.kv_lora_rank + self.fd_config.model_config.qk_rope_head_dim)
                * (self.cache_config.block_size)
                * num_layers
            )  # compress_kv + k_pe
        elif self.dsa_cache:
            required_memory = (
                1
                * (
                    self.fd_config.model_config.kv_lora_rank
                    + self.fd_config.model_config.kv_lora_rank // 128 * 4
                    + 2 * self.fd_config.model_config.qk_rope_head_dim
                    # indexer
                    + self.fd_config.model_config.index_head_dim
                    + self.fd_config.model_config.index_head_dim // 128 * 4
                )
                * (self.cache_config.block_size)
                * num_layers
            )
        else:
            required_memory = byte_of_dtype * 2 * (self.cache_config.block_size * hidden_dim) * num_layers  # k + v
        return required_memory

    def clear_cache(self, profile=False):
        """Clear cached data from shared inputs and forward metadata"""
        create_cache_tensor = profile or not (
            self.fd_config.cache_config.num_cpu_blocks > 0
            or self.fd_config.cache_config.kvcache_storage_backend
            or self.fd_config.scheduler_config.splitwise_role != "mixed"
        )
        local_rank = self.local_rank % self.parallel_config.tensor_parallel_size

        if not create_cache_tensor:
            for name, tensor in self.cache_kvs_map.items():
                unset_data_ipc(tensor, name, True, False)
            self.cache_ready_signal.value[local_rank] = 0
        self.cache_kvs_map.clear()
        self.share_inputs.pop("caches", None)
        if self.forward_meta is not None:
            self.forward_meta.clear_caches()
        paddle.device.cuda.empty_cache()

    def clear_parameters(self, pid):
        """Dynamic model loader use to clear parameters use for RL"""
        # Clear CUDAGraph
        if self.use_cudagraph:
            self.model.clear_grpah_opt_backend()
        # Clear parameters and Send single
        self.dynamic_weight_manager.clear_parameters(
            pid, self.fd_config.parallel_config.shutdown_comm_group_if_worker_idle
        )
        if self.spec_method == SpecMethod.MTP:
            self.proposer.clear_mtp_cache()
        self.clear_cache()
        paddle.device.cuda.empty_cache()

        self.dynamic_weight_manager._log_memory("dynamic weight manager clear all memory")

    def clear_requests(self):
        """Dynamic model loader use to clear requests use for RL"""
        self.share_inputs["stop_flags"][:] = True
        # prompt_logprobs
        self.prompt_logprobs_reqs.clear()
        self.in_progress_prompt_logprobs.clear()
        self.forward_batch_reqs_list = [None for _ in range(self.scheduler_config.max_num_seqs)]

        # Routing Replay
        if self.routing_replay_manager:
            self.routing_replay_manager.clear_all_request()

    def update_parameters(self, pid):
        """Dynamic model loader use to update parameters use for RL"""
        # Update parameters
        self.dynamic_weight_manager.update_parameters(
            pid, self.fd_config.parallel_config.shutdown_comm_group_if_worker_idle
        )

        # Reset share_inputs
        self.share_inputs.reset_share_inputs()
        if self.spec_method == SpecMethod.MTP:
            self.proposer.model_inputs.reset_model_inputs()
            self.proposer.initialize_kv_cache(main_model_num_blocks=self.num_gpu_blocks)
        self.initialize_kv_cache()
        # Recapture CUDAGraph
        if self.use_cudagraph:
            self.capture_model()
        # Send single
        self.dynamic_weight_manager.finalize_update(pid)

        self.dynamic_weight_manager._log_memory("dynamic weight manager update all memory")

    def update_weights(self, version: str = None, rsync_config: Dict[str, Any] = None):
        return self.dynamic_weight_manager.update_weights_by_rdma(version, rsync_config)

    def padding_cudagraph_inputs(self) -> None:
        """
        Clean buffers used for the CUDA graph when replaying the CUDA graph with the padded batch.
        In FastDeploy, almost all input tensors have a buffer. So, just keep the buffer clean when replaying the CUDA graph with the padded batch.
        """
        # In init_attention_metadata, the decode buffer has already been cleared

        # To adapt to CUDA Graph, keep the forward pass at the maximum batch size.
        if self.use_cudagraph:
            self.real_token_num = self.forward_meta.ids_remove_padding.shape[0]
        return

    def _init_image_preprocess(self) -> None:
        processor = DataProcessor(
            tokenizer_name=self.model_config.model,
            image_preprocessor_name=str(self.model_config.model),
        )
        processor.eval()
        image_preprocess = processor.image_preprocessor
        image_preprocess.image_mean_tensor = paddle.to_tensor(image_preprocess.image_mean, dtype="float32").reshape(
            [1, 3, 1, 1]
        )
        image_preprocess.image_std_tensor = paddle.to_tensor(image_preprocess.image_std, dtype="float32").reshape(
            [1, 3, 1, 1]
        )
        image_preprocess.rescale_factor = paddle.to_tensor(image_preprocess.rescale_factor, dtype="float32")
        image_preprocess.image_mean_tensor = image_preprocess.image_mean_tensor.squeeze([-2, -1]).repeat_interleave(
            self.model_config.vision_config.patch_size**2 * 1, -1
        )
        image_preprocess.image_std_tensor = image_preprocess.image_std_tensor.squeeze([-2, -1]).repeat_interleave(
            self.model_config.vision_config.patch_size**2 * 1, -1
        )
        self.image_preprocess = image_preprocess

    def _preprocess_mm_task(self, one: dict) -> None:
        """process batch"""

        input_ids = one["input_ids"][np.newaxis, :]
        input_ids = paddle.to_tensor(input_ids, dtype=paddle.int64)
        token_type_ids = one["token_type_ids"][np.newaxis, :]
        token_type_ids = paddle.to_tensor(token_type_ids, dtype=paddle.int64)

        if "images" in one and one["images"] is not None:
            image_type_ids = one["image_type_ids"][np.newaxis, :]
            images = one["images"]
            image_type_ids = paddle.to_tensor(image_type_ids, dtype=paddle.int64)
            images = paddle.to_tensor(images, dtype="uint8" if "ernie" in self.model_config.model_type else "bfloat16")
            grid_thw = paddle.to_tensor(one["grid_thw"], dtype="int64")
        else:
            image_type_ids = None
            images = None
            grid_thw = None

        if one["position_ids"] is not None:
            position_ids = paddle.to_tensor(one["position_ids"], dtype="int64")
        else:
            position_ids = None

        result = dict(
            input_ids=input_ids,
            image_type_ids=image_type_ids,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            grid_thw=grid_thw,
            images=images,
        )
        return result

    def extract_vision_features_ernie(self, vision_inputs: dict[str, list[paddle.Tensor]]) -> paddle.Tensor:
        """
        vision feature extactor for ernie-vl
        """
        assert len(vision_inputs["images_lst"]) > 0, "at least one image needed"

        grid_thw = paddle.to_tensor(vision_inputs["grid_thw_lst"], dtype=paddle.int64)
        # ernie-vl has images norm
        images = paddle.concat(vision_inputs["images_lst"]).cast("float32")
        images = self.image_preprocess.rescale_factor * images - self.image_preprocess.image_mean_tensor
        images = images / self.image_preprocess.image_std_tensor
        images = images.cast("bfloat16")

        with paddle.amp.auto_cast(
            True,
            custom_black_list=self.amp_black,
            custom_white_list=self.amp_white,
            level="O2",
            dtype=self.model_config.dtype,
        ):
            image_features = self.model.vision_model.extract_feature(images, grid_thw)
            if self.parallel_config.tensor_parallel_size > 1:
                S, C = image_features.shape
                image_features = image_features.reshape([-1, C * self.model_config.spatial_conv_size**2])
                image_features = ScatterOp.apply(image_features, axis=-1)  # mp 切 Fea
                image_features = image_features.reshape([S, -1])
            # ernie-vl has resampler_model
            image_features = self.model.resampler_model(
                image_features,
                grid_thw,
            )
        return image_features

    def extract_vision_features_qwen(self, vision_inputs: dict[str, list[paddle.Tensor]]) -> paddle.Tensor:
        assert len(vision_inputs["images_lst"]) > 0, "at least one image needed"

        grid_thw = paddle.to_tensor(vision_inputs["grid_thw_lst"], dtype=paddle.int64)
        images = paddle.concat(vision_inputs["images_lst"]).cast("bfloat16")
        with paddle.amp.auto_cast(
            True,
            custom_black_list=self.amp_black,
            custom_white_list=self.amp_white,
            level="O2",
            dtype=self.model_config.dtype,
        ):
            image_features = self.model.visual.extract_feature(images, grid_thw)

        return image_features

    def extract_vision_features_paddleocr(self, inputs: dict[str, list[paddle.Tensor]]) -> paddle.Tensor:
        if envs.FD_ENABLE_MAX_PREFILL:
            inputs["vit_position_ids_lst"] = np.concatenate(inputs["vit_position_ids_lst"])
            images = paddle.concat(inputs["images_lst"]).cast("bfloat16")
            grid_thw = paddle.to_tensor(inputs["grid_thw_lst"], dtype="int64")
            position_ids = paddle.to_tensor(inputs["vit_position_ids_lst"], dtype="int64")
            cu_seqlens = paddle.cumsum(paddle.to_tensor(inputs["cu_seqlens"])).cast("int32")
        else:
            assert inputs["images"] is not None
            grid_thw = inputs["grid_thw"]
            images = inputs["images"]

            position_ids = []
            cu_seqlens = [0]
            for idx, thw in enumerate(grid_thw):
                numel = np.prod(np.array(thw))
                position_ids.append(paddle.arange(numel) % np.prod(thw[1:]))
                cu_seqlens.append(cu_seqlens[-1] + numel)

            position_ids = paddle.concat(position_ids, axis=0).to(images.place)
            cu_seqlens = paddle.to_tensor(cu_seqlens, dtype=paddle.int32).to(images.place)

        with paddle.amp.auto_cast(
            True,
            custom_black_list=self.amp_black,
            custom_white_list=self.amp_white,
            level="O2",
            dtype=self.model_config.dtype,
        ):
            image_features = self.model.visual(
                pixel_values=images,
                image_grid_thw=grid_thw,
                position_ids=position_ids,
                interpolate_pos_encoding=True,
                cu_seqlens=cu_seqlens,
                use_rope=True,
                window_size=-1,
            )
            image_features = self.model.projector(image_features, grid_thw)
            image_features = paddle.concat(image_features, axis=0)

        return image_features

    @paddle.no_grad()
    def extract_vision_features(self, multi_vision_inputs: dict[str, list[paddle.Tensor]]) -> paddle.Tensor:
        """extract_vision_features"""
        if "ernie" in self.model_config.model_type:
            return self.extract_vision_features_ernie(multi_vision_inputs)
        elif "qwen" in self.model_config.model_type:
            return self.extract_vision_features_qwen(multi_vision_inputs)
        elif "paddleocr" in self.model_config.model_type:
            return self.extract_vision_features_paddleocr(multi_vision_inputs)
        else:
            raise ValueError(f"multiple modalities model {self.model_config.model_type} is not supported")

    @paddle.no_grad()
    def _dummy_run_extract_vision_features(self):
        grid_thw_list = ([(1, 10, 88), (1, 10, 80)], [(1, 14, 62), (1, 20, 42), (1, 14, 60)])
        for grid_thw in grid_thw_list:
            images = []
            position_ids = []
            cu_seqlens = [0]
            for idx, thw in enumerate(grid_thw):
                numel = np.prod(np.array(thw))
                images.append(paddle.uniform(shape=[numel, 3, 14, 14], dtype="float32", min=0.0, max=1.0))
                position_ids.append(paddle.arange(numel) % np.prod(thw[1:]))
                cu_seqlens.append(cu_seqlens[-1] + numel)

            images = paddle.concat(images, axis=0)
            position_ids = paddle.concat(position_ids, axis=0).to(images.place)
            cu_seqlens = paddle.to_tensor(cu_seqlens, dtype=paddle.int32).to(images.place)

            with paddle.amp.auto_cast(
                True,
                custom_black_list=self.amp_black,
                custom_white_list=self.amp_white,
                level="O2",
                dtype=self.model_config.dtype,
            ):
                self.model.visual(
                    pixel_values=images,
                    image_grid_thw=grid_thw,
                    position_ids=position_ids,
                    interpolate_pos_encoding=True,
                    cu_seqlens=cu_seqlens,
                    use_rope=True,
                    window_size=-1,
                )

    @paddle.no_grad()
    def prepare_rope3d(
        self, position_ids: paddle.Tensor, max_len_lst: list[int], cumsum_seqlens: list[int]
    ) -> list[paddle.Tensor]:
        """prepare_rope3d"""

        rope_emb_lst = get_rope_3d(
            position_ids=position_ids,
            rotary_dim=self.model_config.head_dim,
            partial_rotary_factor=1.0,
            base=self.model_config.rope_theta,
            max_position=self.model_config.max_model_len,
            freq_allocation=getattr(self.model_config, "freq_allocation", 20),
            rope_scaling=getattr(self.model_config, "rope_scaling", {}),
            model_type=self.model_config.model_type,
            max_len_lst=max_len_lst,
            cumsum_seqlens=cumsum_seqlens,
        )
        return rope_emb_lst

    def _get_prompt_logprobs_list(
        self,
        hidden_states: paddle.Tensor,
    ) -> list[Optional[LogprobsTensors]]:
        if len(self.prompt_logprobs_reqs) > 0:
            assert (
                not self.fd_config.cache_config.enable_prefix_caching
            ), "prompt_logprobs must disable prefix caching, --no-enable-prefix-caching."
        logprobs_mode = self.fd_config.model_config.logprobs_mode
        prompt_logprobs_list: list[Optional[LogprobsTensors]] = self.scheduler_config.max_num_seqs * [None]
        completed_prefill_reqs: list[Request] = []
        for req_id, request in self.prompt_logprobs_reqs.items():
            num_prompt_logprobs = request.sampling_params.prompt_logprobs
            if request.prompt_token_ids is None or num_prompt_logprobs is None:
                continue
            if num_prompt_logprobs == -1:
                num_prompt_logprobs = self.ori_vocab_size

            num_tokens = request.prefill_end_index - request.prefill_start_index
            num_prompt_tokens = len(request.prompt_token_ids)

            logprobs_tensors = self.in_progress_prompt_logprobs.get(req_id)
            if not logprobs_tensors:
                logprobs_tensors = LogprobsTensors.empty_cpu(num_prompt_tokens - 1, num_prompt_logprobs + 1)
                self.in_progress_prompt_logprobs[req_id] = logprobs_tensors
            start_idx = request.prefill_start_index
            start_tok = start_idx + 1
            num_remaining_tokens = num_prompt_tokens - start_tok
            batch_id = self.share_inputs.get_index_by_batch_id(request.idx)
            if num_tokens <= num_remaining_tokens:
                # This is a chunk, more tokens remain.
                # In the == case, there are no more prompt logprobs to produce
                # but we want to defer returning them to the next step where we
                # have new generated tokens to return.
                num_logits = num_tokens
            else:
                # This is the last chunk of prompt tokens to return.
                num_logits = num_remaining_tokens
                completed_prefill_reqs.append(request)
                prompt_logprobs_list[batch_id] = logprobs_tensors
            if num_logits <= 0:
                # This can happen for the final chunk if we prefilled exactly
                # (num_prompt_tokens - 1) tokens for this request in the prior
                # step. There are no more prompt logprobs to produce.
                continue
            offset = self.share_inputs["cu_seqlens_q"][batch_id]
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
            logits = self.model.compute_logits(prompt_hidden_states)
            prompt_token_ids = request.prompt_token_ids[start_tok : start_tok + num_logits]
            prompt_token_ids_tensor = paddle.to_tensor(prompt_token_ids, dtype="int64")
            if logprobs_mode == "raw_logprobs":
                raw_logprobs = self.sampler.compute_logprobs(logits)
            elif logprobs_mode == "raw_logits":
                raw_logprobs = logits
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
                raw_logprobs, num_prompt_logprobs, prompt_token_ids_tensor
            )
            # Synchronize before using token_ids, logprobs and ranks to ensure async copy are completed.
            paddle.device.synchronize()
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(token_ids, False)
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, False)
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(ranks, False)

        for req in completed_prefill_reqs:
            del self.prompt_logprobs_reqs[req.request_id]
            del self.in_progress_prompt_logprobs[req.request_id]
        return prompt_logprobs_list

    def initialize_routing_replay_manager(self):
        """Initialize the routing replay manager after initialize the KVCache"""
        # Use updated block number
        self.routing_replay_manager = RoutingReplayManager(
            fd_config=self.fd_config,
            block_table=self.share_inputs["block_tables"],
            total_block_num=self.num_gpu_blocks,
        )