Files
sglang/python/sglang/srt/configs/qwen3_5.py
2026-02-14 22:09:44 -08:00

123 lines
4.5 KiB
Python

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"
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"
class Qwen3_5MoeTextConfig(Qwen3_5TextConfig):
model_type = "qwen3_5_moe_text"
class Qwen3_5MoeConfig(Qwen3_5Config):
model_type = "qwen3_5_moe"
sub_configs = {
"vision_config": Qwen3_5MoeVisionConfig,
"text_config": Qwen3_5MoeTextConfig,
}