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sglang/python/sglang/srt/configs/qwen3_omni.py
T
Binyao Jiangyhyang201zRzRzRzRzRzRzRMinglei Zhu
 cf0478d602 [Glm46v] Bug fix for accuracy drop and unable to launch server (#14585) 
Co-authored-by: yhyang201 <yhyang201@gmail.com>
Co-authored-by: zRzRzRzRzRzRzR <2448370773@qq.com>
Co-authored-by: Minglei Zhu <mingleizhu1122@gmail.com>
2025-12-07 23:45:02 -08:00

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from transformers import PretrainedConfig
from transformers.configuration_utils import layer_type_validation
from sglang.utils import logger
class Qwen3OmniMoeAudioEncoderConfig(PretrainedConfig):
model_type = "qwen3_omni_moe_audio_encoder"
def __init__(
self,
num_mel_bins=128,
encoder_layers=32,
encoder_attention_heads=20,
encoder_ffn_dim=5120,
d_model=1280,
dropout=0,
attention_dropout=0,
activation_function="gelu",
activation_dropout=0,
scale_embedding=False,
initializer_range=0.02,
max_source_positions=1500,
n_window=100,
output_dim=3584,
n_window_infer=400,
conv_chunksize=500,
downsample_hidden_size=480,
**kwargs,
):
super().__init__(**kwargs)
self.num_mel_bins = num_mel_bins
self.d_model = d_model
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.encoder_ffn_dim = encoder_ffn_dim
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_function = activation_function
self.activation_dropout = activation_dropout
self.num_hidden_layers = encoder_layers
self.initializer_range = initializer_range
self.scale_embedding = (
scale_embedding # scale factor will be sqrt(d_model) if True
)
self.max_source_positions = max_source_positions
self.n_window = n_window
self.output_dim = output_dim
self.n_window_infer = n_window_infer
self.conv_chunksize = conv_chunksize
self.downsample_hidden_size = downsample_hidden_size
class Qwen3OmniMoeVisionEncoderConfig(PretrainedConfig):
model_type = "qwen3_omni_moe_vision_encoder"
base_config_key = "vision_config"
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
class Qwen3OmniMoeTextConfig(PretrainedConfig):
model_type = "qwen3_omni_moe_text"
keys_to_ignore_at_inference = ["past_key_values"]
# Default tensor parallel plan for base model `Qwen3OmniMoeText`
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.experts.*.gate_proj": "colwise",
"layers.*.mlp.experts.*.up_proj": "colwise",
"layers.*.mlp.experts.*.down_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
vocab_size=3584,
hidden_size=2048,
intermediate_size=18944,
num_hidden_layers=28,
num_attention_heads=28,
num_key_value_heads=4,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
tie_word_embeddings=False,
rope_theta=1000000.0,
rope_scaling=None,
attention_bias=False,
sliding_window=None,
attention_dropout=0,
decoder_sparse_step=1,
moe_intermediate_size=768,
num_experts_per_tok=8,
num_experts=128,
norm_topk_prob=True,
output_router_logits=False,
router_aux_loss_coef=0.001,
mlp_only_layers=None,
**kwargs,
):
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.sliding_window = sliding_window
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
# Validate the correctness of rotary position embeddings parameters
# BC: if there is a 'type' field, move it to 'rope_type'.
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
# MoE arguments
self.decoder_sparse_step = decoder_sparse_step
self.moe_intermediate_size = moe_intermediate_size
self.num_experts_per_tok = num_experts_per_tok
self.num_experts = num_experts
self.norm_topk_prob = norm_topk_prob
self.output_router_logits = output_router_logits
self.router_aux_loss_coef = router_aux_loss_coef
self.mlp_only_layers = [] if mlp_only_layers is None else mlp_only_layers
class Qwen3OmniMoeThinkerConfig(PretrainedConfig):
model_type = "qwen3_omni_moe_thinker"
attribute_map = {
"image_token_id": "image_token_index",
"video_token_id": "video_token_index",
"audio_token_id": "audio_token_index",
}
sub_configs = {
"audio_config": Qwen3OmniMoeAudioEncoderConfig,
"vision_config": Qwen3OmniMoeVisionEncoderConfig,
"text_config": Qwen3OmniMoeTextConfig,
}
def __init__(
self,
audio_config=None,
vision_config=None,
text_config=None,
audio_token_id=151646,
image_token_id=151655,
video_token_id=151656,
position_id_per_seconds=25,
audio_start_token_id=151647,
user_token_id=872,
initializer_range=0.02,
**kwargs,
):
super().__init__(**kwargs)
self.user_token_id = user_token_id
self.position_id_per_seconds = position_id_per_seconds
self.audio_start_token_id = audio_start_token_id
self.initializer_range = initializer_range
if isinstance(vision_config, dict):
vision_config = Qwen3OmniMoeVisionEncoderConfig(**vision_config)
elif vision_config is None:
vision_config = Qwen3OmniMoeVisionEncoderConfig()
self.vision_config = vision_config
if isinstance(audio_config, dict):
audio_config = Qwen3OmniMoeAudioEncoderConfig(**audio_config)
elif audio_config is None:
audio_config = Qwen3OmniMoeAudioEncoderConfig()
self.audio_config = audio_config
if isinstance(text_config, dict):
text_config = Qwen3OmniMoeTextConfig(**text_config)
elif text_config is None:
text_config = Qwen3OmniMoeTextConfig()
self.text_config = text_config
self.audio_token_id = audio_token_id
self.image_token_id = image_token_id
self.video_token_id = video_token_id
class Qwen3OmniMoeTalkerCodePredictorConfig(PretrainedConfig):
model_type = "qwen3_omni_moe_talker_code_predictor"
keys_to_ignore_at_inference = ["past_key_values"]
# Default tensor parallel plan for base model `Qwen3OmniMoeTalkerCodePredictor`
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
vocab_size=2048,
hidden_size=1024,
intermediate_size=3072,
num_hidden_layers=5,
num_attention_heads=16,
num_key_value_heads=8,
head_dim=128,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=0.000001,
use_cache=True,
tie_word_embeddings=False,
rope_theta=10000,
rope_scaling=None,
attention_bias=False,
sliding_window=None,
layer_types=None,
attention_dropout=0,
num_code_groups=32,
**kwargs,
):
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.sliding_window = sliding_window
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.head_dim = head_dim
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
# Validate the correctness of rotary position embeddings parameters
# BC: if there is a 'type' field, move it to 'rope_type'.
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
self.layer_types = layer_types
if self.layer_types is None:
self.layer_types = [
(
"sliding_attention"
if self.sliding_window is not None and i >= self.max_window_layers
else "full_attention"
)
for i in range(self.num_hidden_layers)
]
layer_type_validation(self.layer_types, self.num_hidden_layers)
self.num_code_groups = num_code_groups
class Qwen3OmniMoeTalkerTextConfig(PretrainedConfig):
model_type = "qwen3_omni_moe_talker_text"
keys_to_ignore_at_inference = ["past_key_values"]
# Default tensor parallel plan for base model `Qwen3OmniMoeTalkerText`
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.experts.*.gate_proj": "colwise",
"layers.*.mlp.experts.*.up_proj": "colwise",
"layers.*.mlp.experts.*.down_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
vocab_size=3072,
hidden_size=1024,
intermediate_size=2048,
num_hidden_layers=20,
num_attention_heads=16,
num_key_value_heads=2,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=0.000001,
use_cache=True,
tie_word_embeddings=False,
rope_theta=10000,
rope_scaling=None,
attention_bias=False,
sliding_window=None,
attention_dropout=0,
decoder_sparse_step=1,
moe_intermediate_size=384,
num_experts_per_tok=8,
num_experts=128,
norm_topk_prob=False,
output_router_logits=False,
router_aux_loss_coef=0.001,
mlp_only_layers=None,
**kwargs,
):
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.sliding_window = sliding_window
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
# Validate the correctness of rotary position embeddings parameters
# BC: if there is a 'type' field, move it to 'rope_type'.
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
# MoE arguments
self.decoder_sparse_step = decoder_sparse_step
self.moe_intermediate_size = moe_intermediate_size
self.num_experts_per_tok = num_experts_per_tok
self.num_experts = num_experts
self.norm_topk_prob = norm_topk_prob
self.output_router_logits = output_router_logits
self.router_aux_loss_coef = router_aux_loss_coef
self.mlp_only_layers = [] if mlp_only_layers is None else mlp_only_layers
class Qwen3OmniMoeTalkerConfig(PretrainedConfig):
sub_configs = {
"code_predictor_config": Qwen3OmniMoeTalkerCodePredictorConfig,
"text_config": Qwen3OmniMoeTalkerTextConfig,
}
def __init__(
self,
code_predictor_config=None,
text_config=None,
num_code_groups=32,
thinker_hidden_size=2048,
codec_eos_token_id=4198,
accept_hidden_layer=18,
codec_nothink_id=4203,
codec_think_bos_id=4204,
codec_think_eos_id=4205,
codec_pad_id=4196,
codec_bos_id=4197,
audio_token_id=151646,
image_token_id=151655,
video_token_id=151656,
vision_start_token_id=151652,
position_id_per_seconds=25,
audio_start_token_id=151669,
speaker_id=None,
**kwargs,
):
super().__init__(**kwargs)
if code_predictor_config is None:
code_predictor_config = {}
self.code_predictor_config = Qwen3OmniMoeTalkerCodePredictorConfig()
logger.info(
"code_predictor_config is None. Initializing code_predictor_config model with default values"
)
elif isinstance(code_predictor_config, Qwen3OmniMoeTalkerCodePredictorConfig):
self.code_predictor_config = code_predictor_config
else:
self.code_predictor_config = Qwen3OmniMoeTalkerCodePredictorConfig(
**code_predictor_config
)
if text_config is None:
text_config = {}
self.text_config = Qwen3OmniMoeTalkerTextConfig()
logger.info(
"talker text_config is None. Initializing talker text model with default values"
)
elif isinstance(text_config, Qwen3OmniMoeTalkerTextConfig):
self.text_config = text_config
else:
self.text_config = Qwen3OmniMoeTalkerTextConfig(**text_config)
self.num_code_groups = num_code_groups
self.thinker_hidden_size = thinker_hidden_size
self.codec_eos_token_id = codec_eos_token_id
self.accept_hidden_layer = accept_hidden_layer
self.codec_nothink_id = codec_nothink_id
self.codec_think_bos_id = codec_think_bos_id
self.codec_think_eos_id = codec_think_eos_id
self.codec_pad_id = codec_pad_id
self.codec_bos_id = codec_bos_id
self.audio_token_id = audio_token_id
self.image_token_id = image_token_id
self.video_token_id = video_token_id
self.position_id_per_seconds = position_id_per_seconds
self.audio_start_token_id = audio_start_token_id
self.vision_start_token_id = vision_start_token_id
self.speaker_id = speaker_id
class Qwen3OmniMoeCode2WavConfig(PretrainedConfig):
def __init__(
self,
codebook_size=2048,
hidden_size=1024,
max_position_embeddings=8000,
rope_theta=10000,
num_attention_heads=16,
num_key_value_heads=16,
attention_bias=False,
sliding_window=72,
intermediate_size=3072,
hidden_act="silu",
layer_scale_initial_scale=0.01,
rms_norm_eps=1e-5,
num_hidden_layers=8,
num_quantizers=16,
upsample_rates=(8, 5, 4, 3),
upsampling_ratios=(2, 2),
decoder_dim=1536,
attention_dropout=0.0,
**kwargs,
):
super().__init__(**kwargs)
self.codebook_size = codebook_size
self.hidden_size = hidden_size
self.max_position_embeddings = max_position_embeddings
self.rope_theta = rope_theta
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.attention_bias = attention_bias
self.sliding_window = sliding_window
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.layer_scale_initial_scale = layer_scale_initial_scale
self.rms_norm_eps = rms_norm_eps
self.num_hidden_layers = num_hidden_layers
self.num_quantizers = num_quantizers
self.upsample_rates = upsample_rates
self.upsampling_ratios = upsampling_ratios
self.decoder_dim = decoder_dim
self.attention_dropout = attention_dropout
@property
def layer_types(self):
"""
All layer in code2wav should be sliding attention
"""
return ["sliding_attention"] * self.num_hidden_layers
class Qwen3OmniMoeConfig(PretrainedConfig):
model_type = "qwen3_omni_moe"
sub_configs = {
"thinker_config": Qwen3OmniMoeThinkerConfig,
"talker_config": Qwen3OmniMoeTalkerConfig,
"code2wav_config": Qwen3OmniMoeCode2WavConfig,
}
def __init__(
self,
thinker_config=None,
talker_config=None,
code2wav_config=None,
enable_audio_output=True,
im_start_token_id=151644,
im_end_token_id=151645,
tts_pad_token_id=151671,
tts_bos_token_id=151672,
tts_eos_token_id=151673,
system_token_id=8948,
user_token_id=872,
assistant_token_id=77091,
**kwargs,
):
super().__init__(**kwargs)
if thinker_config is None:
thinker_config = {}
logger.info(
"thinker_config is None. Initializing thinker model with default values"
)
if talker_config is None:
talker_config = {}
logger.info(
"talker_config is None. Initializing talker model with default values"
)
if code2wav_config is None:
code2wav_config = {}
logger.info(
"code2wav_config is None. Initializing code2wav model with default values"
)
self.thinker_config = Qwen3OmniMoeThinkerConfig(**thinker_config)
self.talker_config = Qwen3OmniMoeTalkerConfig(**talker_config)
self.code2wav_config = Qwen3OmniMoeCode2WavConfig(**code2wav_config)
self.enable_audio_output = enable_audio_output
self.im_start_token_id = im_start_token_id
self.im_end_token_id = im_end_token_id
self.tts_pad_token_id = tts_pad_token_id
self.tts_bos_token_id = tts_bos_token_id
self.tts_eos_token_id = tts_eos_token_id
self.system_token_id = system_token_id
self.user_token_id = user_token_id
self.assistant_token_id = assistant_token_id
def get_text_config(self, decoder=False) -> "PretrainedConfig":
"""
Returns the config that is meant to be used with text IO. On most models, it is the original config instance
itself. On specific composite models, it is under a set of valid names.
Args:
decoder (`Optional[bool]`, *optional*, defaults to `False`):
If set to `True`, then only search for decoder config names.
"""
# Overridden for deeply nested config like Qwen2-Omni. We don't have any omni model
# except for Qwen yet. This has to be generalized if more deeply nested configs are
# added. NOTE: currently method used only by vLLM
return self.thinker_config.get_text_config()
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