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()