sgl-project--sglang/python/sglang/srt/configs/qwen3_5.py

from transformers import PretrainedConfig

from sglang.srt.configs.qwen3_next import Qwen3NextConfig
from sglang.srt.configs.qwen3_vl import Qwen3VLVisionConfig


class Qwen3_5VisionConfig(Qwen3VLVisionConfig):
    model_type = "qwen3_5"
    base_config_key = "vision_config"

    def __init__(self, **kwargs):
        super().__init__(**kwargs)


class Qwen3_5TextConfig(Qwen3NextConfig):
    model_type = "qwen3_5_text"
    base_config_key = "text_config"

    def __init__(
        self,
        **kwargs,
    ):
        # HF Qwen3.5 checkpoints may provide RoPE settings under rope_parameters.
        # Normalize it before parent init so downstream code sees the expected values.
        rope_parameters = kwargs.pop("rope_parameters", None)
        if kwargs.get("rope_scaling") is None and rope_parameters is not None:
            kwargs["rope_scaling"] = rope_parameters

        super().__init__(**kwargs)
        if self.rope_scaling is None:
            self.rope_scaling = rope_parameters or {}

        # Keep both names for compatibility with model code paths that read either.
        self.rope_parameters = rope_parameters or self.rope_scaling


class Qwen3_5Config(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Qwen3_5Model`]. It is used to instantiate a
    Qwen3.5 model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of
    Qwen3.5.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        text_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Qwen3_5TextConfig`):
            The config object or dictionary of the text backbone.
        vision_config (`Union[PreTrainedConfig, dict]`,  *optional*, defaults to `Qwen3_5VisionConfig`):
            The config object or dictionary of the vision backbone.
        image_token_id (`int`, *optional*, defaults to 151655):
            The image token index to encode the image prompt.
        video_token_id (`int`, *optional*, defaults to 151656):
            The video token index to encode the image prompt.
        vision_start_token_id (`int`, *optional*, defaults to 151652):
            The start token index to encode the image prompt.
        vision_end_token_id (`int`, *optional*, defaults to 151653):
            The end token index to encode the image prompt.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie the word embeddings.

    ```python
    >>> from transformers import Qwen3_5ForConditionalGeneration, Qwen3_5Config

    >>> # Initializing a Qwen3.5 style configuration
    >>> configuration = Qwen3_5Config()

    >>> # Initializing a model from the Qwen3.5 style configuration
    >>> model = Qwen3_5ForConditionalGeneration(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "qwen3_5"
    sub_configs = {
        "vision_config": Qwen3_5VisionConfig,
        "text_config": Qwen3_5TextConfig,
    }
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        text_config=None,
        vision_config=None,
        image_token_id=151655,
        video_token_id=151656,
        vision_start_token_id=151652,
        vision_end_token_id=151653,
        tie_word_embeddings=False,
        **kwargs,
    ):
        if isinstance(vision_config, dict):
            self.vision_config = self.sub_configs["vision_config"](**vision_config)
        elif vision_config is None:
            self.vision_config = self.sub_configs["vision_config"]()

        if isinstance(text_config, dict):
            self.text_config = self.sub_configs["text_config"](**text_config)
        elif text_config is None:
            self.text_config = self.sub_configs["text_config"]()

        self.image_token_id = image_token_id
        self.video_token_id = video_token_id
        self.vision_start_token_id = vision_start_token_id
        self.vision_end_token_id = vision_end_token_id
        super().__init__(**kwargs, tie_word_embeddings=tie_word_embeddings)


class Qwen3_5MoeVisionConfig(Qwen3_5VisionConfig):
    model_type = "qwen3_5_moe"

    def __init__(self, **kwargs):
        super().__init__(**kwargs)


class Qwen3_5MoeTextConfig(Qwen3_5TextConfig):
    model_type = "qwen3_5_moe_text"

    def __init__(self, **kwargs):
        super().__init__(**kwargs)


# All Moe variant classes need explicit __init__ because the kw_only=True
# dataclass decorator in transformers v5.5.3+ auto-generates __init__ for
# subclasses, bypassing parent __init__ methods that set up attributes
# (e.g. norm_topk_prob, rope_scaling) and convert sub-config dicts to objects.
class Qwen3_5MoeConfig(Qwen3_5Config):
    model_type = "qwen3_5_moe"
    sub_configs = {
        "vision_config": Qwen3_5MoeVisionConfig,
        "text_config": Qwen3_5MoeTextConfig,
    }

    def __init__(self, **kwargs):
        super().__init__(**kwargs)