FastDeploy/fastdeploy/config.py

"""
# Copyright (c) 2023 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.
"""

from __future__ import annotations

import json
import os
from enum import Enum
from typing import Any, Dict, List, Literal, Optional, Union

import paddle
import paddle.distributed as dist
from paddleformers.transformers.configuration_utils import PretrainedConfig

import fastdeploy
from fastdeploy import envs
from fastdeploy.model_executor.layers.quantization.quant_base import QuantConfigBase
from fastdeploy.multimodal.registry import MultimodalRegistry
from fastdeploy.platforms import current_platform
from fastdeploy.scheduler import SchedulerConfig
from fastdeploy.utils import ceil_div, check_unified_ckpt, get_host_ip, get_logger

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

TaskOption = Literal["generate"]


class MoEPhase:
    """
    The generation phase of the moe.
    """

    def __init__(self, phase="prefill"):
        self._phase = phase

    @property
    def phase(self):
        return self._phase

    @phase.setter
    def phase(self, value):
        if value not in ["prefill", "decode"]:
            raise ValueError(f"The moe_phase is invalid, only support prefill and decode, but got {value}")
        else:
            self._phase = value


class ErnieArchitectures:
    """Helper class for ERNIE architecture check."""

    ARCHITECTURES = {
        "Ernie4_5ForCausalLM",  # 0.3B-PT
        "Ernie4_5_ForCausalLM",
        "Ernie4_5_MoeForCausalLM",
        "Ernie4_5_VLMoeForConditionalGeneration",
    }

    @classmethod
    def register_ernie_model_arch(cls, model_class):
        if model_class.name().startswith("Ernie") and model_class.name() not in cls.ARCHITECTURES:
            cls.ARCHITECTURES.add(model_class.name())

    @classmethod
    def contains_ernie_arch(cls, architectures):
        """Check if any ERNIE architecture is present in the given architectures."""
        return any(arch in architectures for arch in cls.ARCHITECTURES)

    @classmethod
    def is_ernie_arch(cls, architecture):
        """Check if the given architecture is an ERNIE architecture."""
        return architecture in cls.ARCHITECTURES


PRETRAINED_INIT_CONFIGURATION = {
    "top_p": 1.0,
    "temperature": 1.0,
    "rope_theta": 10000.0,
    "penalty_score": 1.0,
    "frequency_score": 0.0,
    "presence_score": 0.0,
    "min_length": 1,
    "num_key_value_heads": -1,
    "start_layer_index": 0,
    "moe_num_shared_experts": 0,
    "moe_layer_start_index": 0,
    "num_max_dispatch_tokens_per_rank": 128,
    "moe_use_aux_free": False,
    "vocab_size": -1,
    "hidden_dropout_prob": 0.0,
    "initializer_range": 0.02,
    "max_position_embeddings": 512,
    "quantization_config": None,
    "tie_word_embeddings": False,
    "rms_norm_eps": 1e-5,
    "moe_num_experts": None,
    "moe_layer_end_index": None,
}


class ModelConfig:
    """
    The configuration class to store the configuration of a `LLM`.
    """

    def __init__(
        self,
        args,
    ):
        self.model = ""
        self.is_quantized = False
        self.max_model_len = 0
        self.dtype = ""
        self.enable_logprob = False
        self.enable_redundant_experts = False
        self.redundant_experts_num = 0
        self.seed = 0
        self.quantization = None
        self.pad_token_id: int = -1
        self.eos_tokens_lens: int = 2
        self.lm_head_fp32: bool = False
        self.model_format = "auto"
        self.num_nextn_predict_layers = 0
        for key, value in args.items():
            if hasattr(self, key):
                setattr(self, key, value)

        assert self.model != ""
        pretrained_config, _ = PretrainedConfig.get_config_dict(self.model)
        self.pretrained_config = PretrainedConfig.from_dict(pretrained_config)

        # set attribute from pretrained_config
        for key, value in pretrained_config.items():
            setattr(self, key, value)

        # we need set default value when not exist
        for key, value in PRETRAINED_INIT_CONFIGURATION.items():
            if not hasattr(self, key):
                setattr(self, key, value)

        if not hasattr(self, "head_dim"):
            self.head_dim = self.hidden_size // self.num_attention_heads

        if hasattr(self, "vision_config"):
            self.vision_config = PretrainedConfig.from_dict(self.vision_config)

        self.ori_vocab_size = args.get("ori_vocab_size", self.vocab_size)

        architectures = self.architectures[0]
        if MultimodalRegistry.contains_model(architectures):
            self.enable_mm = True
        else:
            self.enable_mm = False

        self.is_unified_ckpt = check_unified_ckpt(self.model)

        self.override_name_from_config()
        self.read_from_env()
        self.read_model_config()

    def override_name_from_config(self):
        """
        Override attribute names from the exported model's configuration.
        """

        if not self.is_unified_ckpt and hasattr(self, "infer_model_mp_num"):
            self.tensor_parallel_size = self.infer_model_mp_num
            del self.infer_model_mp_num

        if hasattr(self, "num_hidden_layers"):
            if hasattr(self, "remove_tail_layer"):
                if self.remove_tail_layer is True:
                    self.num_hidden_layers -= 1
                elif isinstance(self.remove_tail_layer, int):
                    self.num_hidden_layers -= self.remove_tail_layer

        if not hasattr(self, "mla_use_absorb"):
            self.mla_use_absorb = False

    def read_from_env(self):
        """
        Read configuration information from environment variables and update the object's attributes.

        If an attribute is not present or is an empty string in the environment variables, use the default value.
        """
        self.max_stop_seqs_num = int(envs.FD_MAX_STOP_SEQS_NUM)
        self.stop_seqs_max_len = int(envs.FD_STOP_SEQS_MAX_LEN)

        def reset_config_value(key, value):
            if not hasattr(self, key.lower()):
                if os.getenv(key, None):
                    value = eval(os.getenv(key))
                    logger.info(f"Get parameter `{key}` = {value} from environment.")
                else:
                    logger.info(f"Parameter `{key}` will use default value {value}.")
                setattr(self, key.lower(), value)

        reset_config_value("COMPRESSION_RATIO", 1.0)
        reset_config_value("ROPE_THETA", 10000)

    def read_model_config(self):
        config_path = os.path.join(self.model, "config.json")
        if os.path.exists(config_path):
            self.model_config = json.load(open(config_path, "r", encoding="utf-8"))
            if "torch_dtype" in self.model_config and "dtype" in self.model_config:
                raise ValueError(
                    "Only one of 'torch_dtype' or 'dtype' should be present in config.json. "
                    "Found both, which indicates an ambiguous model format. "
                    "Please ensure your config.json contains only one dtype field."
                )
            elif "torch_dtype" in self.model_config:
                self.model_format = "torch"
                logger.info("The model format is Hugging Face")
            elif "dtype" in self.model_config:
                self.model_format = "paddle"
                logger.info("The model format is Paddle")
            else:
                raise ValueError(
                    "Unknown model format. Please ensure your config.json contains "
                    "either 'torch_dtype' (for Hugging Face models) or 'dtype' (for Paddle models) field. "
                    f"Config file path: {config_path}"
                )

    def _get_download_model(self, model_name, model_type="default"):
        # TODO: Provide dynamic graph for self-downloading and save to the specified download directory.
        pass

    def print(self):
        """
        Print all configuration information.
        """
        logger.info("Model Configuration Information :")
        for k, v in self.__dict__.items():
            logger.info("{:<20}:{:<6}{}".format(k, "", v))
        logger.info("=============================================================")


class ParallelConfig:
    """Configuration for the distributed execution."""

    def __init__(
        self,
        args,
    ):
        self.sequence_parallel = False  # Whether to enable sequence parallelism.
        self.use_ep = False  # Whether to enable Expert Parallelism
        self.moe_phase = MoEPhase("prefill")  # Generation phase
        self.msg_queue_id = 1  # mesage queue id

        self.tensor_parallel_rank = 0  # TP rank ID
        self.tensor_parallel_size = 1  # TP degree
        self.expert_parallel_rank = 0  # EP rank ID
        self.expert_parallel_size = 1  # EP degree
        self.data_parallel_size = 1  # DP degree
        self.enable_expert_parallel = False
        self.local_data_parallel_id = 0
        # The embedding weight distributed on your gpu cards is divided by row or column.
        # Defaults to False means divide by row. When vocab_size can not be divided by world_size
        # but hidden_size can, we can consider split embedding weight by column.
        """
        From old wersion worker args
        TODO(gongshaotian): Reclassify
        """
        self.max_num_seqs: int = 34
        # Set default block num for profile run
        self.total_block_num: int = 2000
        # block size
        self.block_size: int = 64
        # Engine worker queue port
        self.engine_worker_queue_port: str = "9923"
        # Max model len
        self.max_model_len: int = 3072  # max_seq_len
        # cuda visible devices
        self.device_ids: str = "0"
        # Input dtype
        self.dtype: str = "bfloat16"
        # Encoder's decoder num
        self.enc_dec_block_num: int = 1
        # First token id
        self.first_token_id: int = 1
        # Process ID of engine
        self.engine_pid: Optional[int] = None
        # Do profile or not
        self.do_profile: bool = False
        # Use internode_ll_two_stage or not
        self.use_internode_ll_two_stage: bool = False

        self.max_num_batched_tokens: int = 2048
        # splitwise role
        self.splitwise_role: str = "mixed"
        # guided decoding backend
        self.guided_decoding_backend: str = None
        # disable any whitespace for guided decoding
        self.disable_any_whitespace: bool = True
        self.pod_ip: str = None
        # enable the custom all-reduce kernel and fall back to NCCL(dist.all_reduce).
        self.disable_custom_all_reduce: bool = False
        for key, value in args.items():
            if hasattr(self, key):
                setattr(self, key, value)
        if isinstance(self.engine_worker_queue_port, str):
            self.engine_worker_queue_port = [int(port) for port in self.engine_worker_queue_port.split(",")]
            logger.info(f"engine_worker_queue_port: {self.engine_worker_queue_port}")
        elif isinstance(self.engine_worker_queue_port, int):
            self.engine_worker_queue_port = [self.engine_worker_queue_port]
        # currently, the expert parallel size is equal data parallel size
        if self.enable_expert_parallel:
            self.expert_parallel_size = self.data_parallel_size * self.tensor_parallel_size
        else:
            self.expert_parallel_size = 1
        self.use_ep = self.expert_parallel_size > 1
        if self.splitwise_role == "mixed":
            self.moe_phase = MoEPhase(phase="prefill")
        elif self.splitwise_role == "prefill":
            self.moe_phase = MoEPhase(phase="prefill")
        elif self.splitwise_role == "decode":
            self.moe_phase = MoEPhase(phase="decode")
        else:
            raise NotImplementedError

        # pd_disaggregation
        use_pd_disaggregation: int = int(os.getenv("FLAGS_use_pd_disaggregation", 0))
        use_pd_disaggregation_per_chunk: int = int(os.getenv("FLAGS_use_pd_disaggregation_per_chunk", 0))
        if use_pd_disaggregation_per_chunk:
            self.pd_disaggregation_mode = "per_chunk"
        elif use_pd_disaggregation:
            self.pd_disaggregation_mode = "per_query"
        else:
            self.pd_disaggregation_mode = "None"

    def set_communicate_group(self):
        # different tp group id
        # prevent different tp_groups using the same group_id
        tp_gid_offset = envs.FD_TP_GROUP_GID_OFFSET
        dist.collective._set_custom_gid(self.data_parallel_rank + tp_gid_offset)

        self.tp_group = dist.new_group(
            range(
                self.data_parallel_rank * self.tensor_parallel_size,
                (self.data_parallel_rank + 1) * self.tensor_parallel_size,
            )
        )
        dist.collective._set_custom_gid(None)
        # same ep group id
        if self.enable_expert_parallel:
            dist.collective._set_custom_gid(self.data_parallel_size + tp_gid_offset)
            self.ep_group = dist.new_group(range(self.expert_parallel_size))
            dist.collective._set_custom_gid(None)

        logger.info(
            f"data_parallel_size: {self.data_parallel_size}, tensor_parallel_size: {self.tensor_parallel_size}, expert_parallel_size: {self.expert_parallel_size}, data_parallel_rank: {self.data_parallel_rank}, tensor_parallel_rank: {self.tensor_parallel_rank}, expert_parallel_rank: {self.expert_parallel_rank}, tp_group: {self.tp_group}."
        )
        dist.collective._set_custom_gid(None)

    def print(self):
        """
        print all config

        """
        logger.info("Parallel Configuration Information :")
        for k, v in self.__dict__.items():
            logger.info("{:<20}:{:<6}{}".format(k, "", v))
        logger.info("=============================================================")


class SpeculativeConfig:
    """
    Configuration for speculative decoding.
    """

    def __init__(
        self,
        args,
    ):
        self.method_list = ["ngram_match", "mtp"]
        self.mtp_strategy_list = ["default", "with_ngram"]

        # speculative method, choose in [None, "ngram_match", "mtp", "hybrid_mtp_ngram"]
        self.method: Optional[str] = None
        # mtp strategy in mtp-method
        self.mtp_strategy = "default"
        # the max length of speculative tokens
        self.num_speculative_tokens: int = 1
        # the model runner step of draft model/mtp...
        self.num_model_steps: int = 1
        # the max length of candidate tokens for speculative method
        self.max_candidate_len: int = 5
        # the max length of verify window for speculative method
        self.verify_window: int = 2
        # ngram match
        self.max_ngram_size: int = 5
        self.min_ngram_size: int = 2
        # model for mtp/eagle/draft_model
        self.model: Optional[str] = None
        # quantization of model
        self.quantization: Optional[str] = None
        # allocate more blocks to prevent mtp from finishing the block earlier than the main model
        # Fixed now
        self.num_gpu_block_expand_ratio: Optional[float] = 1
        # To distinguish the main model and draft model(mtp/eagle/draftmodel)
        # ["main", "mtp"]
        self.model_type: Optional[str] = "main"
        # TODO(liuzichang): To reduce memory usage, MTP shares the main model's lm_head and embedding layers.
        # A trick method is currently used to enable this sharing.
        # This will be replaced with a more standardized solution in the future.
        self.sharing_model = None
        # During benchmarking, we need to enforce that the number of accepted tokens is 1.
        # This means no tokens from MTP are accepted.
        # This ensures that the specified simulation acceptance rate is not affected.
        self.benchmark_mode: bool = False

        self.num_extra_cache_layer = 0

        for key, value in args.items():
            if hasattr(self, key):
                setattr(self, key, value)

        self.read_model_config()
        self.reset()

    def read_model_config(self):
        """
        Read configuration from file.
        """
        self.model_config = {}
        if not self.enabled_speculative_decoding():
            return

        self.is_unified_ckpt = check_unified_ckpt(self.model)
        if self.model is None:
            return

        self.config_path = os.path.join(self.model, "config.json")
        if os.path.exists(self.config_path):
            self.model_config = json.load(open(self.config_path, "r", encoding="utf-8"))

    def reset(self):
        """
        Reset configuration.
        """

        def reset_value(cls, value_name, key=None, default=None):
            if key is not None and key in cls.model_config:
                setattr(cls, value_name, cls.model_config[key])
            elif getattr(cls, value_name, None) is None:
                setattr(cls, value_name, default)

        if not self.enabled_speculative_decoding():
            return

        # NOTE(liuzichang): We will support multi-layer in future
        if self.method in ["mtp"]:
            self.num_extra_cache_layer = 1

    def enabled_speculative_decoding(self):
        """
        Check if speculative decoding is enabled.
        """
        if self.method is None:
            return False
        return True

    def to_json_string(self):
        """
        Convert speculative_config to json string.
        """
        return json.dumps({key: value for key, value in self.__dict__.items() if value is not None})

    def print(self):
        """
        print all config

        """
        logger.info("Speculative Decoding Configuration Information :")
        for k, v in self.__dict__.items():
            logger.info("{:<20}:{:<6}{}".format(k, "", v))
        logger.info("=============================================================")

    def check_legality_parameters(
        self,
    ) -> None:
        """Check the legality of parameters passed in from the command line"""
        if self.method is not None:
            assert (
                self.method in self.method_list
            ), f"speculative method only support {self.method_list} now, but get {self.method}."

            assert (
                self.num_speculative_tokens >= 1 and self.num_speculative_tokens <= 5
            ), f"num_speculative_tokens only support in range[1, 5], but get {self.num_speculative_tokens}."
            assert (
                self.num_model_steps >= 1 and self.num_model_steps <= 5
            ), f"num_model_steps only support in range[1, 5], but get {self.num_model_steps}."

            if self.method in ["mtp", "hybrid_mtp_ngram"]:
                if self.num_speculative_tokens < self.num_model_steps:
                    logger.warning(
                        f"Get num_model_steps > num_speculative_tokens. Reset num_speculative_tokens to {self.num_model_steps}"
                    )
                    self.num_speculative_tokens = self.num_model_steps

            assert (
                self.mtp_strategy in self.mtp_strategy_list
            ), f"mtp_strategy_list only support {self.mtp_strategy_list}, but get {self.mtp_strategy}"

    def __str__(self) -> str:
        return self.to_json_string()


class DeviceConfig:
    """
    Configuration for device settings.
    """

    def __init__(
        self,
        args,
    ):
        self.device_type = "cuda"
        for key, value in args.items():
            if hasattr(self, key):
                setattr(self, key, value)


class GraphOptimizationConfig:
    """
    Configuration for compute graph level optimization.
    """

    def __init__(
        self,
        args,
    ):
        """The Top-level graph optimization contral corresponds to different backends.
        - 0: dyncmic graph
        - 1: static graph
        - 2: static graph + cinn compilation backend
        """
        self.graph_opt_level: int = 0

        # CUDA Graph Config
        """ Whether to use cudagraph.
        - False: cudagraph is not used.
        - True: cudagraph is used.
            It requires that all input buffers have fixed addresses, and all
            splitting ops write their outputs to input buffers.
            - With dyncmic graph backend: ...
            - With static grpah backend: WIP
        """
        self.sot_warmup_sizes: list[int] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 16, 32, 64, 128]
        """  Number of warmup runs for SOT warmup. """
        self.use_cudagraph: bool = False
        """Sizes to capture cudagraph.
        - None (default): capture sizes are inferred from llm config.
        - list[int]: capture sizes are specified as given."""
        self.cudagraph_capture_sizes: Optional[list[int]] = None
        """ Number of warmup runs for cudagraph. """
        self.cudagraph_num_of_warmups: int = 2
        """Whether to copy input tensors for cudagraph.
        If the caller can guarantee that the same input buffers
        are always used, it can set this to False. Otherwise, it should
        set this to True."""
        self.cudagraph_copy_inputs: bool = False
        """ In static graph, this is an operation list that does not need to be captured by the CUDA graph.
        CudaGraphBackend will split these operations from the static graph.
        Example usage:
            cudagraph_splitting_ops = ["paddle.unified_attention"]

        Note: If want to use subgraph capture functionality in a dynamic graph,
        can manually split the model into multiple layers and apply the @support_graph_optimization decorator
        only to the layer where CUDA graph functionality is required.
        """
        self.cudagraph_splitting_ops: list[str] = []
        """ Whether to use a full cuda graph for the entire forward pass rather than
        splitting certain operations such as attention into subgraphs.
        Thus this flag cannot be used together with splitting_ops."""
        self.full_cuda_graph: bool = True

        """ Whether to use shared memory pool for multi capture_size """
        self.use_unique_memory_pool: bool = False

        self.max_capture_size: int = None
        self.real_shape_to_captured_size: dict[int, int] = None
        # CINN Config ...
        if args is not None:
            for key, value in args.items():
                if hasattr(self, key):
                    setattr(self, key, value)

        self.check_legality_parameters()

    def init_with_cudagrpah_size(self, max_num_seqs: int = 0) -> None:
        """
        Initialize cuda graph capture sizes and
        pre-compute the mapping from batch size to padded graph size
        """
        # Regular capture sizes
        self.cudagraph_capture_sizes = [size for size in self.cudagraph_capture_sizes if size <= max_num_seqs]
        dedup_sizes = list(set(self.cudagraph_capture_sizes))
        if len(dedup_sizes) < len(self.cudagraph_capture_sizes):
            logger.info(
                ("cudagraph sizes specified by model runner" " %s is overridden by config %s"),
                self.cudagraph_capture_sizes,
                dedup_sizes,
            )
        self.cudagraph_capture_sizes = dedup_sizes

        # Sort to make sure cudagraph capture sizes are in descending order
        self.cudagraph_capture_sizes.sort(reverse=True)
        self.max_capture_size = self.cudagraph_capture_sizes[0] if self.cudagraph_capture_sizes else 0

        # Pre-compute the mapping from shape to padded graph size
        self.real_shape_to_captured_size = {}
        for end, start in zip(self.cudagraph_capture_sizes, self.cudagraph_capture_sizes[1:] + [0]):
            for bs in range(start, end):
                if bs == start:
                    self.real_shape_to_captured_size[bs] = start
                else:
                    self.real_shape_to_captured_size[bs] = end
        self.real_shape_to_captured_size[self.max_capture_size] = self.max_capture_size

    def _set_cudagraph_sizes(self, max_num_seqs: int = 0):
        """
        Calculate a series of candidate capture sizes,
        and then extract a portion of them as the capture list for the CUDA graph based on user input.
        """
        # Shape [1, 2, 4, 8, 16, ... 120, 128]
        draft_capture_sizes = [1, 2, 4] + [8 * i for i in range(1, 17)]
        # Shape [128, 144, ... 240, 256]
        draft_capture_sizes += [16 * i for i in range(9, 17)]
        # Shape [256, 288, ... 992, 1024]
        draft_capture_sizes += [32 * i for i in range(17, 33)]

        draft_capture_sizes.append(max_num_seqs)
        self.cudagraph_capture_sizes = sorted(draft_capture_sizes)

    def to_json_string(self):
        """
        Convert speculative_config to json string.
        """
        return json.dumps({key: value for key, value in self.__dict__.items()})

    def __str__(self) -> str:
        return self.to_json_string()

    def check_legality_parameters(
        self,
    ) -> None:
        """Check the legality of parameters passed in from the command line"""

        if self.graph_opt_level is not None:
            assert self.graph_opt_level in [
                0,
                1,
                2,
            ], "In graph optimization config, graph_opt_level can only take the values of 0, 1 and 2."
        if self.use_cudagraph is not None:
            assert (
                type(self.use_cudagraph) is bool
            ), "In graph optimization config, type of use_cudagraph must is bool."
        if self.cudagraph_capture_sizes is not None:
            assert (
                type(self.cudagraph_capture_sizes) is list
            ), "In graph optimization config, type of cudagraph_capture_sizes must is list."
            assert (
                len(self.cudagraph_capture_sizes) > 0
            ), "In graph optimization config, When opening the CUDA graph, it is forbidden to set the capture sizes to an empty list."

    def update_use_cudagraph(self, argument: bool):
        """
        Unified user specifies the use_cudagraph parameter through two methods,
        '--use-cudagraph' and '--graph-optimization-config'
        """
        if self.use_cudagraph is None:
            # User only set '--use-cudagraph'
            self.use_cudagraph = argument
        else:
            # User both set '--use-cudagraph' and '--graph-optimization-config'
            if self.use_cudagraph is False and argument is True:
                raise ValueError(
                    "Invalid parameter: Cannot set --use-cudagraph and --graph-optimization-config '{\"use_cudagraph\":false}' simultaneously."
                )
            argument = self.use_cudagraph


class MobaAttentionConfig:
    def __init__(
        self,
        args,
    ):
        self.moba_encoder_top_k_left: int = None
        self.moba_encoder_top_k_right: int = None
        "The sparse topk of encoder attention is located at [moba_encoder_top_k_left, moba_encoder top_k_right]"
        self.moba_decoder_top_k_left: int = None
        self.moba_decoder_top_k_right: int = None
        "The sparse topk of decoder attention is located at [moba_decoder_top_k_left, moba_decoder top_k_right]"
        self.moba_use_encoder_seq_limit: int = None
        "When the number of encdoer token is less than moba_use_encoder_seq_limit, it is not sparse"
        self.moba_use_decoder_seq_limit: int = None
        "When the number of decdoer token is less than moba_use_decoder_seq_limit, it is not sparse"
        self.moba_block_size: int = 128
        self.mlp_weight_name: str = "moba_mlp_weight.safetensors"
        self.moba_max_seq_length: int = 128 * 1024
        if args is not None:
            for key, value in args.items():
                if hasattr(self, key):
                    setattr(self, key, value)
            if self.moba_use_encoder_seq_limit is None and self.moba_encoder_top_k_left is not None:
                self.moba_use_encoder_seq_limit = self.moba_encoder_top_k_left * self.moba_block_size
            if self.moba_use_decoder_seq_limit is None and self.moba_decoder_top_k_left is not None:
                self.moba_use_decoder_seq_limit = self.moba_decoder_top_k_left * self.moba_block_size
            self.check_legality_parameters()

    def check_legality_parameters(
        self,
    ) -> None:
        if self.moba_encoder_top_k_left is not None:
            assert self.moba_encoder_top_k_left > 0, "moba_encoder_top_k_left must large than 0"

        if self.moba_encoder_top_k_right is not None:
            assert self.moba_encoder_top_k_right > 0, "moba_encoder_top_k_right must large than 0"
            assert (
                self.moba_encoder_top_k_right >= self.moba_encoder_top_k_left
            ), "moba_encoder_top_k_right must large than moba_encoder_top_k_left"

        if self.moba_decoder_top_k_left is not None:
            assert self.moba_decoder_top_k_left > 0, "moba_decoder_top_k_left must large than 0"

        if self.moba_decoder_top_k_right is not None:
            assert self.moba_decoder_top_k_right > 0, "moba_decoder_top_k_right must large than 0"
            assert (
                self.moba_decoder_top_k_right >= self.moba_decoder_top_k_left
            ), "moba_decoder_top_k_right must large than moba_decoder_top_k_left"

        if self.moba_use_encoder_seq_limit is not None and self.moba_encoder_top_k_left is not None:
            assert self.moba_use_encoder_seq_limit >= self.moba_encoder_top_k_left * self.moba_block_size
        if self.moba_use_decoder_seq_limit is not None and self.moba_decoder_top_k_left is not None:
            assert self.moba_use_decoder_seq_limit >= self.moba_decoder_top_k_left * self.moba_block_size

    def to_json_string(self):
        """
        Convert moba_attention_config to json string.
        """
        return json.dumps({key: value for key, value in self.__dict__.items() if value is not None})


class EarlyStopConfig:
    def __init__(
        self,
        args,
    ):
        """
        Early Stop Configuration class.

        Attributes:
            window_size: size of the window
            threshold: trigger early stop when the ratio of probs exceeds the threshold
        """
        """enable to use early stop"""
        self.enable_early_stop: bool = False
        """strategy for early stop, the strategy lists are ['repetition']"""
        self.strategy: str = "repetition"
        """ the maximum length of verify window for early stop """
        self.window_size: int = 3000
        """ the probs threshold for early stop """
        self.threshold: float = 0.99

        if args is not None:
            for key, value in args.items():
                if hasattr(self, key):
                    setattr(self, key, value)
        self.check_legality_parameters()

    def to_json_string(self):
        """
        Convert early_stop_config to json string.
        """
        return json.dumps({key: value for key, value in self.__dict__.items()})

    def __str__(self) -> str:
        return self.to_json_string()

    def check_legality_parameters(
        self,
    ) -> None:
        """Check the legality of parameters passed in from the command line"""
        if self.enable_early_stop is not None:
            assert isinstance(
                self.enable_early_stop, bool
            ), "In early stop config, type of enable_early_stop must is bool."
        if self.window_size is not None:
            assert isinstance(self.window_size, int), "In early stop config, type of window_size must be int."
            assert self.window_size > 0, "window_size must large than 0"
        if self.threshold is not None:
            assert isinstance(self.threshold, float), "In early stop config, type of threshold must be float."
            assert self.threshold >= 0 and self.threshold <= 1, "threshold must between 0 and 1"

    def update_enable_early_stop(self, argument: bool):
        """
        Unified user specifies the enable_early_stop parameter through two methods,
        '--enable-early-stop' and '--early-stop-config'
        """
        if self.enable_early_stop is None:
            # User only set '--enable-early-stop'
            self.enable_early_stop = argument
        else:
            # User both set '--enable-early-stop' and '--early-stop-config'
            if self.enable_early_stop is False and argument is True:
                raise ValueError(
                    "Invalid parameter: Cannot set ---enable-early-stop and --early-stop-config '{\"enable_early_stop\":false}' simultaneously."
                )
            argument = self.enable_early_stop


class LoadChoices(str, Enum):
    """LoadChoices"""

    DEFAULT = "default"
    DEFAULT_V1 = "default_v1"


class LoadConfig:
    """
    Configuration for dynamic weight loading strategies

    Attributes:
        dynamic_load_weight: Whether to enable dynamic weight loading
        load_strategy: Specifies the weight loading method when enabled:
            - 'ipc': Real-time IPC streaming with automatic resharding
            - 'ipc_snapshot': Load from disk snapshot of IPC weights
            - 'meta': Only model meta messages
            - None: No dynamic loading
    """

    def __init__(
        self,
        args,
    ):
        self.load_choices: Union[str, LoadChoices] = LoadChoices.DEFAULT.value
        self.use_fastsafetensor = int(envs.FD_USE_FASTSAFETENSOR) == 1
        self.dynamic_load_weight: bool = False
        self.load_strategy: Optional[Literal["ipc", "ipc_snapshot", "meta", "normal"]] = "normal"
        for key, value in args.items():
            if hasattr(self, key):
                setattr(self, key, value)


class LoRAConfig:
    """LoRA Config"""

    pass


class CacheConfig:
    """
    Configuration for the KV cache.

    Attributes:
        block_size (int): Size of a cache block in number of tokens.
        gpu_memory_utilization (float): Fraction of GPU memory to use for model execution.
        cache_dtype (str): Data type for kv cache storage. Default is 'bfloat16'.
        num_gpu_blocks_override (Optional[int]): Number of GPU blocks to use.
        Overrides profiled num_gpu_blocks if provided.
        kv_cache_ratio (float): Ratio for calculating the maximum block number.
        enc_dec_block_num (int): Number of encoder-decoder blocks.
        prealloc_dec_block_slot_num_threshold (int): Number of token slot threadshold to allocate next blocks for decoding.
        enable_prefix_caching (bool): Flag to enable prefix caching.
    """

    def __init__(self, args):
        """
        Initialize the CacheConfig class.

        Args:
            block_size (int): Size of a cache block in number of tokens.
            gpu_memory_utilization (float): Fraction of GPU memory to use.
            cache_dtype (str): Data type for cache storage. Default is 'bfloat16'.
            num_gpu_blocks_override (Optional[int]): Override for number of GPU blocks.
            num_cpu_blocks (Optional[int]): Number of CPU blocks.
            kv_cache_ratio (float): Ratio for max block calculation.
            enc_dec_block_num (int): Number of encoder-decoder blocks.
            prealloc_dec_block_slot_num_threshold (int): Number of token slot threadshold to allocate next blocks for decoding, used when ENABLE_V1_KVCACHE_SCHEDULER=1.
            enable_prefix_caching (bool): Enable prefix caching.
        """
        self.block_size = 64
        self.gpu_memory_utilization = 0.9
        self.num_gpu_blocks_override = None
        if envs.ENABLE_V1_KVCACHE_SCHEDULER:
            self.kv_cache_ratio = 1.0
        else:
            self.kv_cache_ratio = 0.75
        self.enc_dec_block_num = 0 if current_platform.is_iluvatar() else 2
        self.prealloc_dec_block_slot_num_threshold = 12
        self.cache_dtype = "bfloat16"
        self.model_cfg = None
        self.enable_chunked_prefill = False
        self.rdma_comm_ports = None
        self.cache_transfer_protocol = None
        self.pd_comm_port = None
        self.enable_prefix_caching = False
        self.enable_ssd_cache = False
        self.cache_queue_port = None
        self.swap_space = None
        for key, value in args.items():
            if hasattr(self, key):
                setattr(self, key, value)

        if self.rdma_comm_ports is not None and isinstance(self.rdma_comm_ports, str):
            self.rdma_comm_ports = self.rdma_comm_ports.split(",")

        if self.pd_comm_port is not None and isinstance(self.pd_comm_port, str):
            self.pd_comm_port = [int(port) for port in self.pd_comm_port.split(",")]

        if self.swap_space is None:
            self.enable_hierarchical_cache = False
        else:
            self.enable_hierarchical_cache = True

        if self.model_cfg is not None:
            if self.model_cfg.quantization_config is not None:
                self.cache_dtype = self.model_cfg.quantization_config.get("kv_cache_quant_type", self.cache_dtype)
            if (
                hasattr(self.model_cfg, "num_key_value_heads")
                and hasattr(self.model_cfg, "num_key_value_heads")
                and self.model_cfg.num_key_value_heads is not None
                and int(self.model_cfg.num_key_value_heads) > 0
            ):
                kv_num_head = int(self.model_cfg.num_key_value_heads)
            else:
                kv_num_head = self.model_cfg.num_attention_heads
            self.model_cfg.kv_num_head = kv_num_head
            # TODO check name
            if "int4" in self.cache_dtype.lower() or "float4" in self.cache_dtype.lower():
                byte_size = 0.5
                self.cache_dtype = "uint8"
            elif "int8" in self.cache_dtype.lower() or "float8" in self.cache_dtype.lower():
                self.cache_dtype = "uint8"
                byte_size = 1
            else:
                byte_size = 2
            self.each_token_cache_space = int(
                self.model_cfg.num_hidden_layers * kv_num_head * self.model_cfg.head_dim * byte_size
            )
            self.bytes_per_block = int(self.each_token_cache_space * self.block_size)
            self.bytes_per_layer_per_block = int(
                self.block_size
                * self.model_cfg.kv_num_head
                * self.model_cfg.head_dim
                // args["tensor_parallel_size"]
                * byte_size
            )

        if self.swap_space is None:
            self.num_cpu_blocks = 0
        else:
            self.num_cpu_blocks = int(self.swap_space * 1024**3 / self.bytes_per_block)
        self._verify_args()

    def metrics_info(self):
        """Convert cache_config to dict(key: str, value: str) for prometheus metrics info."""
        return {key: str(value) for key, value in self.__dict__.items()}

    def _verify_args(self):
        if self.gpu_memory_utilization > 1.0:
            raise ValueError("GPU memory utilization must be less than 1.0. Got " f"{self.gpu_memory_utilization}.")
        if self.kv_cache_ratio > 1.0:
            raise ValueError("KV cache ratio must be less than 1.0. Got " f"{self.kv_cache_ratio}.")

    def postprocess(self, num_total_tokens, number_of_tasks):
        """
        calculate block num
        """
        self.dec_token_num = self.enc_dec_block_num * self.block_size
        if self.num_gpu_blocks_override is not None:
            self.total_block_num = self.num_gpu_blocks_override
            if envs.ENABLE_V1_KVCACHE_SCHEDULER:
                self.prefill_kvcache_block_num = self.total_block_num
            else:
                self.prefill_kvcache_block_num = int(self.total_block_num * self.kv_cache_ratio)
        else:
            length = num_total_tokens // number_of_tasks
            block_num = (length + self.block_size - 1 + self.dec_token_num) // self.block_size
            self.total_block_num = block_num * number_of_tasks
            self.prefill_kvcache_block_num = self.total_block_num
            logger.info(f"Doing profile, the total_block_num:{self.total_block_num}")

    def reset(self, num_gpu_blocks):
        """
        reset gpu block number
        """
        self.total_block_num = num_gpu_blocks
        if envs.ENABLE_V1_KVCACHE_SCHEDULER:
            self.prefill_kvcache_block_num = self.total_block_num
        else:
            self.prefill_kvcache_block_num = int(self.total_block_num * self.kv_cache_ratio)
        logger.info(
            f"Reset block num, the total_block_num:{self.total_block_num},"
            f" prefill_kvcache_block_num:{self.prefill_kvcache_block_num}"
        )

    def print(self):
        """
        print all config

        """
        logger.info("Cache Configuration Information :")
        for k, v in self.__dict__.items():
            logger.info("{:<20}:{:<6}{}".format(k, "", v))
        logger.info("=============================================================")


class DecodingConfig:
    """
    Configuration for decoding
    """

    def __init__(
        self,
        args,
    ):
        self.pad_token_id = None
        for key, value in args.items():
            if hasattr(self, key):
                setattr(self, key, value)


class CommitConfig:
    """
    Configuration for tracking version information from version.txt

    Attributes:
        fastdeploy_commit: Full FastDeploy git commit hash
        paddle_version: PaddlePaddle version string
        paddle_commit: PaddlePaddle git commit hash
        cuda_version: CUDA version string
        compiler_version: CXX compiler version string
    """

    def __init__(
        self,
    ):
        self.fastdeploy_commit: str = ""
        self.paddle_version: str = ""
        self.paddle_commit: str = ""
        self.cuda_version: str = ""
        self.compiler_version: str = ""

        self._load_from_version_file()

    def _load_from_version_file(self, file_path: str = None):
        """Internal method to load version info from file"""
        if file_path is None:
            file_path = os.path.join(fastdeploy.__path__[0], "version.txt")
        try:
            with open(file_path, "r") as f:
                for line in f:
                    line = line.strip()
                    if line.startswith("fastdeploy GIT COMMIT ID:"):
                        self.fastdeploy_commit = line.split(":")[1].strip()
                    elif line.startswith("Paddle version:"):
                        self.paddle_version = line.split(":")[1].strip()
                    elif line.startswith("Paddle GIT COMMIT ID:"):
                        self.paddle_commit = line.split(":")[1].strip()
                    elif line.startswith("CUDA version:"):
                        self.cuda_version = line.split(":")[1].strip()
                    elif line.startswith("CXX compiler version:"):
                        self.compiler_version = line.split(":")[1].strip()
        except FileNotFoundError:
            logger.info(f"Warning: Version file not found at {file_path}")
        except Exception as e:
            logger.info(f"Warning: Could not read version file - {e!s}")

    def print(self):
        """
        print all config

        """
        logger.info("Fasedeploy Commit Information :")
        for k, v in self.__dict__.items():
            logger.info("{:<20}:{:<6}{}".format(k, "", v))
        logger.info("=============================================================")


class FDConfig:
    """
    The configuration class which contains all fastdeploy-related configuration. This
    simplifies passing around the distinct configurations in the codebase.
    """

    def __init__(
        self,
        model_config: ModelConfig = None,
        cache_config: CacheConfig = None,
        parallel_config: ParallelConfig = None,
        load_config: LoadConfig = None,
        commit_config: CommitConfig = CommitConfig(),
        scheduler_config: SchedulerConfig = None,
        device_config: DeviceConfig = None,
        decoding_config: DecodingConfig = None,
        quant_config: QuantConfigBase = None,
        graph_opt_config: GraphOptimizationConfig = None,
        moba_attention_config: MobaAttentionConfig = None,
        speculative_config: SpeculativeConfig = None,
        tokenizer: str = None,
        max_model_len: int = 8192,
        max_num_seqs: int = 8,
        max_num_batched_tokens: Optional[int] = None,
        ips: str = None,
        use_warmup: bool = False,
        engine_worker_queue_port: str = "8002",
        limit_mm_per_prompt: Optional[Dict[str, Any]] = None,
        mm_processor_kwargs: Optional[Dict[str, Any]] = None,
        splitwise_role: str = "mixed",
        innode_prefill_ports: Optional[List[int]] = None,
        max_num_partial_prefills: int = 1,
        max_long_partial_prefills: int = 1,
        long_prefill_token_threshold: int = 0,
        reasoning_parser: str = None,
        guided_decoding_backend: Optional[str] = None,
        disable_any_whitespace: bool = False,
        early_stop_config: Optional[Dict[str, Any]] = None,
        tool_parser: str = None,
        test_mode=False,
    ):
        self.model_config: ModelConfig = model_config  # type: ignore
        self.cache_config: CacheConfig = cache_config  # type: ignore
        self.scheduler_config: SchedulerConfig = scheduler_config  # type: ignore
        self.parallel_config = parallel_config  # type: ignore
        self.speculative_config: SpeculativeConfig = speculative_config
        self.device_config: DeviceConfig = device_config  # type: ignore
        self.load_config: LoadConfig = load_config
        self.quant_config: Optional[QuantConfigBase] = quant_config
        self.graph_opt_config: Optional[GraphOptimizationConfig] = graph_opt_config
        self.early_stop_config: Optional[EarlyStopConfig] = early_stop_config
        self.decoding_config: DecodingConfig = decoding_config  # type: ignore
        self.cache_config: CacheConfig = cache_config  # type: ignore
        self.moba_attention_config: Optional[MobaAttentionConfig] = moba_attention_config
        # Initialize cuda graph capture list
        if self.graph_opt_config.cudagraph_capture_sizes is None:
            self.graph_opt_config._set_cudagraph_sizes(max_num_seqs=self.parallel_config.max_num_seqs)

        if self.speculative_config is not None and self.speculative_config.method == "mtp":
            max_shape = self.parallel_config.max_num_seqs * (self.speculative_config.num_speculative_tokens + 1)
            if max_shape % 2 == 1:
                max_shape = max_shape + 1
            self.graph_opt_config.init_with_cudagrpah_size(max_num_seqs=min(512, max_shape))
        else:
            self.graph_opt_config.init_with_cudagrpah_size(max_num_seqs=self.parallel_config.max_num_seqs)

        # TODO(wangmingkai02): change graph_opt_level=2 when using static mode with cinn
        if self.graph_opt_config.graph_opt_level == 2:
            self.graph_opt_config.graph_opt_level = 1

        self.tokenizer = tokenizer
        self.max_num_batched_tokens = max_num_batched_tokens
        self.ips = ips
        self.tool_parser = tool_parser

        if self.ips is None:
            self.master_ip = "0.0.0.0"
        elif isinstance(self.ips, str):
            self.ips = self.ips.split(",")

        self.host_ip = get_host_ip()

        if self.ips is None:
            self.nnode = 1
            self.node_rank = 0
        else:
            self.nnode = len(self.ips)

            for idx, ip in enumerate(self.ips):
                if ip == self.host_ip:
                    self.node_rank = idx

        self.max_model_len = max_model_len
        self.max_num_seqs = max_num_seqs
        self.limit_mm_per_prompt = limit_mm_per_prompt
        self.mm_processor_kwargs = mm_processor_kwargs
        self.use_warmup = use_warmup
        self.splitwise_role = splitwise_role
        self.innode_prefill_ports = innode_prefill_ports
        self.max_num_partial_prefills = max_num_partial_prefills
        self.max_long_partial_prefills = max_long_partial_prefills
        self.long_prefill_token_threshold = long_prefill_token_threshold
        self.reasoning_parser = reasoning_parser
        self.guided_decoding_backend = guided_decoding_backend
        self.disable_any_whitespace = disable_any_whitespace
        self.engine_worker_queue_port = engine_worker_queue_port
        self._str_to_list("innode_prefill_ports", int)
        if isinstance(engine_worker_queue_port, int):
            self.engine_worker_queue_port = str(engine_worker_queue_port)
        self._str_to_list("engine_worker_queue_port", str)

        if envs.FD_FOR_TORCH_MODEL_FORMAT:
            self.model_config.model_format = "torch"

        # TODO
        self.max_prefill_batch = 3
        if current_platform.is_xpu():
            self.max_prefill_batch = 1
        if self.model_config is not None and self.model_config.enable_mm:
            self.max_prefill_batch = 1  # TODO:当前多模prefill阶段只支持并行度为1,待优化

        num_ranks = self.parallel_config.tensor_parallel_size * self.parallel_config.data_parallel_size
        self.max_chips_per_node = 16 if current_platform.is_iluvatar() else 8
        if num_ranks > self.max_chips_per_node and self.load_config.load_strategy != "meta":
            self.worker_num_per_node = self.max_chips_per_node
            nnode = ceil_div(num_ranks, self.worker_num_per_node)
            assert nnode == self.nnode, f"nnode: {nnode}, but got {self.nnode}"
        else:
            self.worker_num_per_node = num_ranks

        self.device_ids = ",".join([str(i) for i in range(self.worker_num_per_node)])
        self.device_ids = os.getenv("CUDA_VISIBLE_DEVICES", self.device_ids)
        if current_platform.is_xpu():
            self.device_ids = os.getenv("XPU_VISIBLE_DEVICES", self.device_ids)

        self.read_from_config()
        self.postprocess()
        if test_mode:
            return
        self.check()
        self.print()

    def postprocess(self):
        """
        calculate some parameters
        """
        self.local_device_ids = self.device_ids.split(",")[: self.parallel_config.tensor_parallel_size]

        if self.parallel_config.tensor_parallel_size <= self.worker_num_per_node:
            self.is_master = True
            self.master_ip = "0.0.0.0"
        else:
            self.is_master = False
            self.master_ip = self.ips[0]

        self.paddle_commit_id = paddle.version.commit

        if self.max_num_batched_tokens is None:
            if int(envs.ENABLE_V1_KVCACHE_SCHEDULER):
                if paddle.is_compiled_with_xpu():
                    self.max_num_batched_tokens = self.max_model_len
                else:
                    self.max_num_batched_tokens = 8192  # if set to max_model_len, it's easy to be OOM
            else:
                if self.cache_config.enable_chunked_prefill:
                    self.max_num_batched_tokens = 2048
                else:
                    self.max_num_batched_tokens = self.max_model_len

        if self.long_prefill_token_threshold == 0:
            self.long_prefill_token_threshold = int(self.max_model_len * 0.04)

        self.cache_config.postprocess(self.max_num_batched_tokens, self.max_num_seqs)
        self.cache_config.max_block_num_per_seq = int(self.max_model_len // self.cache_config.block_size)

        if self.guided_decoding_backend == "auto":
            if self.model_config.enable_mm:
                self.guided_decoding_backend = "off"
            else:
                self.guided_decoding_backend = "xgrammar"

    def check(self):
        """
        check the legality of config
        """
        assert self.max_num_seqs <= 256, (
            "The parameter `max_num_seqs` is not allowed to exceed 256, " f"but now it's {self.max_num_seqs}."
        )
        assert self.nnode >= 1, f"nnode: {self.nnode} should no less than 1"
        assert self.max_model_len >= 16, f"max_model_len: {self.max_model_len} should be larger than 16"
        assert self.max_num_seqs >= 1, f"max_num_seqs: {self.max_num_seqs} should be larger than 1"
        assert self.max_num_batched_tokens >= self.max_num_seqs, (
            f"max_num_batched_tokens: {self.max_num_batched_tokens} "
            f"should be larger than or equal to max_num_seqs: {self.max_num_seqs}"
        )
        assert self.max_num_batched_tokens <= self.max_model_len * self.max_num_seqs, (
            f"max_num_batched_tokens: {self.max_num_batched_tokens} should be larger"
            f"than or equal to max_num_seqs: {self.max_num_seqs} * max_model_len: {self.max_model_len}"
        )
        assert (
            self.max_num_partial_prefills >= 1
        ), f"max_num_partial_prefills: {self.max_num_partial_prefills} should be larger than or equal to 1"

        assert (
            self.max_long_partial_prefills >= 1
        ), f"max_long_partial_prefills: {self.max_long_partial_prefills} should be larger than or equal to 1"
        assert self.max_long_partial_prefills <= self.max_num_partial_prefills, (
            f"max_long_partial_prefills: {self.max_long_partial_prefills} should "
            f"be less than or equal to max_num_partial_prefills: {self.max_num_partial_prefills}"
        )
        assert self.splitwise_role in ["mixed", "prefill", "decode"]

        if not self.cache_config.enable_chunked_prefill:
            if not envs.ENABLE_V1_KVCACHE_SCHEDULER:
                assert self.max_num_batched_tokens >= self.max_model_len, (
                    f"max_num_batched_tokens: {self.max_num_batched_tokens} "
                    f"should be larger than or equal to max_model_len: {self.max_model_len}"
                )
        else:
            assert self.max_num_batched_tokens >= self.cache_config.block_size, (
                f"max_num_batched_tokens: {self.max_num_batched_tokens} "
                f"should be larger than or equal to block_size: {self.cache_config.block_size}"
            )

        if self.max_num_partial_prefills > 1:
            assert (
                self.cache_config.enable_chunked_prefill is True
            ), "Chunked prefill must be enabled to set max_num_partial_prefills > 1"
            assert self.long_prefill_token_threshold < self.max_model_len, (
                f"long_prefill_token_threshold: {self.long_prefill_token_threshold} should be less than"
                f" max_model_len: {self.max_model_len}"
            )

        if self.guided_decoding_backend is not None:
            assert self.guided_decoding_backend in [
                "xgrammar",
                "XGrammar",
                "auto",
                "off",
            ], f"Only support xgrammar、auto guided decoding backend, but got {self.guided_decoding_backend}."

            if self.guided_decoding_backend != "off":
                # TODO: mm support guided_decoding
                assert (
                    self.model_config.enable_mm is False
                ), "Multimodal model currently do not support guided_decoding"

                # TODO: speculative decoding support guided_decoding

                # TODO: xpu support guided_decoding
                assert not current_platform.is_xpu(), "XPU currently do not support guided_decoding"

                try:
                    import xgrammar  # noqa
                except Exception as e:
                    raise Exception(
                        f"import XGrammar failed, please install XGrammar use `pip install xgrammar==0.1.19`. \n\t {e}"
                    )
        if self.scheduler_config is not None:
            self.scheduler_config.check()

    def print(self):
        """
        print all config
        """
        logger.info("=================== Configuration Information ===============")
        for k, v in self.__dict__.items():
            if k == "generation_config" and v is not None:
                for gck, gcv in v.to_dict().items():
                    logger.info("{:<20}:{:<6}{}".format(gck, "", gcv))
            elif (
                k == "cache_config"
                or k == "model_config"
                or k == "scheduler_config"
                or k == "parallel_config"
                or k == "commit_config"
            ):
                if v is not None:
                    v.print()
            else:
                logger.info("{:<20}:{:<6}{}".format(k, "", v))
        logger.info("=============================================================")

    def init_cache_info(self):
        """
        initialize cache info
        """
        disaggregate_info = {}
        if self.splitwise_role != "mixed":
            disaggregate_info["role"] = self.splitwise_role
            disaggregate_info["cache_info"] = dict()
            current_protocol = self.cache_config.cache_transfer_protocol.split(",")
            disaggregate_info["transfer_protocol"] = current_protocol
            for protocol in current_protocol:
                if protocol == "ipc":
                    disaggregate_info["cache_info"][protocol] = {
                        "ip": self.host_ip,
                        "port": self.engine_worker_queue_port[self.parallel_config.local_data_parallel_id],
                        "device_ids": self.local_device_ids,
                    }
                elif protocol == "rdma":
                    disaggregate_info["cache_info"][protocol] = {
                        "ip": self.host_ip,
                        "port": self.cache_config.pd_comm_port[0],
                        "rdma_port": self.cache_config.rdma_comm_ports,
                    }
        self.disaggregate_info = disaggregate_info
        logger.info(f"disaggregate_info: {self.disaggregate_info}")

    def read_from_config(self):
        """
        reset model config from json file
        """

        def reset_value(cls, value_name, key):
            if hasattr(cls, key):
                value = getattr(cls, key)
                setattr(cls, value_name, value)
                logger.info(f"Reset parameter {value_name} = {value} from configuration.")

        reset_value(self.cache_config, "block_size", "infer_model_block_size")
        reset_value(
            self.model_config,
            "return_full_hidden_states",
            "return_full_hidden_states",
        )
        reset_value(self.cache_config, "cache_dtype", "infer_model_dtype")

    def _check_master(self):
        return self.is_master

    def _str_to_list(self, attr_name, default_type):
        if hasattr(self, attr_name):
            val = getattr(self, attr_name)
            if val is None:
                return
            if type(val) is str:
                setattr(self, attr_name, [default_type(i) for i in val.split(",")])
            else:
                setattr(self, attr_name, [default_type(i) for i in val])

    def __str__(self) -> str:
        return json.dumps(self.__dict__, indent=4)