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#
# 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.
"""BailingMoE V3 VL model configuration"""
import os
from typing import Union
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class BailingMoeV3Config(PretrainedConfig):
def __init__(
self,
vocab_size=157184,
hidden_size=2048,
intermediate_size=5120,
num_hidden_layers=20,
num_attention_heads=16,
num_key_value_heads=4,
hidden_act="silu",
use_qkv_bias=False, # bailing only
use_bias=False, # bailing only
rms_norm_eps=1e-06,
tie_word_embeddings=False, # PretrainedConfig key, here change default value.
embedding_dropout=0.0,
attention_dropout=0.0,
output_dropout=0.0,
initializer_range=0.02,
max_position_embeddings=32768,
rope_theta=600000.0,
use_cache=True,
max_window_layers=20,
rope_scaling=None,
pad_token_id=156892,
eos_token_id=156892,
num_experts=256,
num_shared_experts=1,
num_experts_per_tok=8,
n_group=8,
topk_group=4,
moe_intermediate_size=512,
moe_shared_expert_intermediate_size=512,
first_k_dense_replace=1,
head_dim=128,
output_router_logits=False,
use_qk_norm=True,
num_nextn_predict_layers=0,
mtp_loss_scaling_factor=0,
moe_router_enable_expert_bias=True,
routed_scaling_factor=1.0,
layer_group_size=5,
kv_lora_rank=512,
q_lora_rank=None,
qk_rope_head_dim=64,
v_head_dim=128,
qk_nope_head_dim=128,
rope_interleave=True,
score_function="sigmoid",
scoring_func="sigmoid",
seq_aux=True,
topk_method="noaux_tc",
router_dtype="fp32",
gated_attention_proj_granularity_type=None,
no_kda_lora=False,
kda_safe_gate=False,
kda_lower_bound=None,
short_conv_kernel_size=4,
**kwargs,
):
self.num_hidden_layers = num_hidden_layers
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.use_qkv_bias = use_qkv_bias
self.use_bias = use_bias
self.rms_norm_eps = rms_norm_eps
self.embedding_dropout = embedding_dropout
self.attention_dropout = attention_dropout
self.output_dropout = output_dropout
self.num_nextn_predict_layers = num_nextn_predict_layers
self.mtp_loss_scaling_factor = mtp_loss_scaling_factor
self.initializer_range = initializer_range
self.max_position_embeddings = max_position_embeddings
self.rope_theta = rope_theta
self.use_cache = use_cache
self.max_window_layers = max_window_layers
self.head_dim = head_dim or self.hidden_size // self.num_attention_heads
self.rope_scaling = rope_scaling
self.use_qk_norm = use_qk_norm
self.moe_router_enable_expert_bias = moe_router_enable_expert_bias
self.routed_scaling_factor = routed_scaling_factor
# MoE configs
self.num_experts = num_experts
self.num_shared_experts = num_shared_experts
self.num_experts_per_tok = num_experts_per_tok
self.n_group = n_group
self.topk_group = topk_group
self.moe_intermediate_size = moe_intermediate_size
self.moe_shared_expert_intermediate_size = moe_shared_expert_intermediate_size
self.first_k_dense_replace = first_k_dense_replace
self.output_router_logits = output_router_logits
# Linear configs
self.layer_group_size = layer_group_size
# mla
self.kv_lora_rank = kv_lora_rank
self.q_lora_rank = q_lora_rank
self.qk_rope_head_dim = qk_rope_head_dim
self.score_function = score_function
self.scoring_func = scoring_func
self.seq_aux = seq_aux
self.topk_method = topk_method
self.v_head_dim = v_head_dim
self.qk_nope_head_dim = qk_nope_head_dim
self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
self.rope_interleave = rope_interleave
self.router_dtype = router_dtype
self.gated_attention_proj_granularity_type = gated_attention_proj_granularity_type
self.no_kda_lora = no_kda_lora
self.kda_safe_gate = kda_safe_gate
self.kda_lower_bound = kda_lower_bound
self.short_conv_kernel_size = short_conv_kernel_size
super().__init__(
pad_token_id=pad_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
)
class Qwen3VLMoeVisionConfig(PretrainedConfig):
model_type = "qwen3_moe_vit"
def __init__(
self,
depth=27,
hidden_size=1152,
hidden_act="gelu_pytorch_tanh",
intermediate_size=4304,
num_heads=16,
in_channels=3,
patch_size=16,
spatial_merge_size=2,
temporal_patch_size=2,
out_hidden_size=3584,
num_position_embeddings=2304,
deepstack_visual_indexes=[8, 16, 24],
initializer_range=0.02,
**kwargs,
):
super().__init__(**kwargs)
self.depth = depth
self.hidden_size = hidden_size
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.num_heads = num_heads
self.in_channels = in_channels
self.patch_size = patch_size
self.spatial_merge_size = spatial_merge_size
self.temporal_patch_size = temporal_patch_size
self.out_hidden_size = out_hidden_size
self.num_position_embeddings = num_position_embeddings
self.initializer_range = initializer_range
self.deepstack_visual_indexes = deepstack_visual_indexes
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
if 'vision_config' in config_dict:
config_dict = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
)
return cls.from_dict(config_dict, **kwargs)
class BailingMoeV3VLConfig(PretrainedConfig):
model_type = "bailing_moe_v3_vl"
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,
mrope_section=None,
**kwargs,
):
if isinstance(vision_config, dict):
vision_config = Qwen3VLMoeVisionConfig(**vision_config)
elif vision_config is None:
vision_config = Qwen3VLMoeVisionConfig()
if isinstance(text_config, dict):
text_config = BailingMoeV3Config(**text_config)
elif text_config is None:
text_config = BailingMoeV3Config()
self.vision_config = vision_config
self.text_config = 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
# M-RoPE section: split qk_rope_head_dim // 2 frequencies into [T, H, W]
# Default: [12, 10, 10] sums to 32 = qk_rope_head_dim(64) // 2
self.mrope_section = mrope_section if mrope_section is not None else [12, 10, 10]
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
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