# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_qwen2_5_omni.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved. # # # 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. from ...configuration_utils import PretrainedConfig from ...modeling_rope_utils import rope_config_validation from ...utils import logging logger = logging.get_logger(__name__) class Qwen2_5OmniVisionEncoderConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Qwen2_5OmniThinkerVision`]. It is used to instantiate a Qwen2.5-VL vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the audio encoder of the Qwen2.5-VL architecture. e.g. [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: depth (`int`, *optional*, defaults to 32): Number of layers (depth) in the model. hidden_size (`int`, *optional*, defaults to 3584): The size of the hidden layers. hidden_act (`str`, *optional*, defaults to `"quick_gelu"`): The non-linear activation function used in the model. Supported options include `"quick_gelu"` and others as applicable. mlp_ratio (`float`, *optional*, defaults to 4): The ratio used to determine the size of the MLP (Multi-Layer Perceptron) hidden layer. num_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer. in_channels (`int`, *optional*, defaults to 3): Number of input channels. patch_size (`int`, *optional*, defaults to 14): The size of the patches extracted from the input. spatial_merge_size (`int`, *optional*, defaults to 2): The size used for merging spatial dimensions. temporal_patch_size (`int`, *optional*, defaults to 2): The size used for patches along the temporal dimension. Example: ```python >>> from transformers import Qwen2_5OmniVisionEncoderConfig, Qwen2_5OmniVisionEncoder >>> # Initializing a Qwen2_5OmniVisionEncoderConfig >>> configuration = Qwen2_5OmniVisionEncoderConfig() >>> # Initializing a Qwen2_5OmniVisionEncoder (with random weights) >>> model = Qwen2_5OmniVisionEncoder(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "qwen2_5_omni_vision_encoder" base_config_key = "vision_config" def __init__( self, depth=32, hidden_size=3584, hidden_act="silu", intermediate_size=3420, num_heads=16, in_channels=3, patch_size=14, spatial_merge_size=2, temporal_patch_size=2, window_size=112, out_hidden_size=3584, fullatt_block_indexes=[7, 15, 23, 31], 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.window_size = window_size self.fullatt_block_indexes = fullatt_block_indexes self.out_hidden_size = out_hidden_size self.initializer_range = initializer_range class Qwen2_5OmniAudioEncoderConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Qwen2_5OmniAudioEncoder`]. It is used to instantiate a Qwen2.5-Omni-Thinker audio encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the audio encoder of the Qwen2-Audio architecture. e.g. [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: num_mel_bins (`int`, *optional*, defaults to 128): Number of mel features used per input features. Should correspond to the value used in the `Qwen2_5OmniProcessor` class. encoder_layers (`int`, *optional*, defaults to 32): Number of encoder layers. encoder_attention_heads (`int`, *optional*, defaults to 20): Number of attention heads for each attention layer in the Transformer encoder. encoder_ffn_dim (`int`, *optional*, defaults to 5120): Dimensionality of the "intermediate" (often named feed-forward) layer in encoder. d_model (`int`, *optional*, defaults to 1280): Dimensionality of the layers. dropout (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_function (`str`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. scale_embedding (`bool`, *optional*, defaults to `False`): Scale embeddings by diving by sqrt(d_model). initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. max_source_positions (`int`, *optional*, defaults to 1500): The maximum sequence length of log-mel filter-bank features that this model might ever be used with. n_window (`int`, *optional*, defaults to 100): The chunk for conv and flash attn in AudioEncoder. output_dim (`int`, *optional*, defaults to 3584): The output dimension of AudioEncoder. Example: ```python >>> from transformers import Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniAudioEncoder >>> # Initializing a Qwen2_5OmniAudioEncoderConfig >>> configuration = Qwen2_5OmniAudioEncoderConfig() >>> # Initializing a Qwen2_5OmniAudioEncoder (with random weights) >>> model = Qwen2_5OmniAudioEncoder(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "qwen2_5_omni_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, **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 class Qwen2_5OmniTextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Qwen2_5OmniThinkerForConditionalGeneration`]. It is used to instantiate an Qwen2.5-Omni-Thinker model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Qwen2.5-Omni-Thinker. e.g. [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 152064): Vocabulary size of the QwenOmni model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Qwen2VLModel`] hidden_size (`int`, *optional*, defaults to 3584): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 18944): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 28): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 28): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 4): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 32768): The maximum sequence length that this model might ever be used with. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. rope_theta (`float`, *optional*, defaults to 1000000.0): The base period of the RoPE embeddings. use_sliding_window (`bool`, *optional*, defaults to `False`): Whether to use sliding window attention. sliding_window (`int`, *optional*, defaults to 32768): Sliding window attention (SWA) window size. If not specified, will default to `4096`. max_window_layers (`int`, *optional*, defaults to 28): The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. Example: ```python >>> from transformers import Qwen2_5OmniThinkerForConditionalGeneration, Qwen2_5OmniThinkerConfig, Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniVisionEncoderConfig >>> # Initializing a Qwen2_5OmniAudioEncoder config >>> audio_config = Qwen2_5OmniAudioEncoderConfig() >>> # Initializing a Qwen2_5OmniVisionEncoder config >>> vision_config = Qwen2_5OmniVisionEncoderConfig() >>> # Initializing a Qwen2.5OmniThinker configuration >>> configuration = Qwen2_5OmniThinkerConfig(audio_config, vision_config) >>> # Initializing a model from the Qwen-Omni style configuration >>> model = Qwen2_5OmniThinkerForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "qwen2_5_omni_text" keys_to_ignore_at_inference = ["past_key_values"] # Default tensor parallel plan for base model `Qwen25OmniText` 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=152064, hidden_size=3584, 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, use_sliding_window=False, sliding_window=32768, max_window_layers=28, attention_dropout=0.0, **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.use_sliding_window = use_sliding_window self.sliding_window = sliding_window # we check `use_sliding_window` in the modeling code self.max_window_layers = max_window_layers # 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.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_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"] rope_config_validation(self) if self.rope_scaling is None: self.rope_scaling = {"mrope_section": [16, 24, 24], "rope_type": "default", "type": "default"} class Qwen2_5OmniThinkerConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Qwen2_5OmniThinkerForConditionalGeneration`]. It is used to instantiate an Qwen2.5-Omni-Thinker model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Qwen2.5-Omni-Thinker. e.g. [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: audio_config (`dict`, *optional*): The config dictionary of the audio backbone. vision_config (`dict`, *optional*): The config dictionary of the vision backbone. text_config (`dict`, *optional*): The config dictionary of the text backbone. audio_token_index (`int`, *optional*, defaults to 151646): The audio token index to encode the audio prompt. image_token_index (`int`, *optional*, defaults to 151655): The image token index to encode the image prompt. video_token_index (`int`, *optional*, defaults to 151656): The video token index to encode the video prompt. position_id_per_seconds (`int`, *optional*, defaults to 25): The increment of position id per second. seconds_per_chunk (`int`, *optional*, defaults to 2): The duration in seconds of the chunk of audio and video data. audio_start_token_id (`int`, *optional*, defaults to 151647): The audio start token index to encode the audio prompt. audio_end_token_id (`int`, *optional*, defaults to 151648): The audio end token index to encode the audio prompt. user_token_id (`int, *optional*, defaults to 872): The user token index to encode the user token. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. Example: ```python >>> from transformers import Qwen2_5OmniThinkerForConditionalGeneration, Qwen2_5OmniThinkerConfig, Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniVisionEncoderConfig >>> # Initializing a Qwen2_5OmniAudioEncoder config >>> audio_config = Qwen2_5OmniAudioEncoderConfig() >>> # Initializing a Qwen2_5OmniVisionEncoder config >>> vision_config = Qwen2_5OmniVisionEncoderConfig() >>> # Initializing a Qwen2_5OmniTextConfig config >>> text_config = Qwen2_5OmniTextConfig() >>> # Initializing a Qwen2.5OmniThinker configuration >>> configuration = Qwen2_5OmniThinkerConfig(audio_config, vision_config, text_config) >>> # Initializing a model from the Qwen-Omni style configuration >>> model = Qwen2_5OmniThinkerForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "qwen2_5_omni_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": Qwen2_5OmniAudioEncoderConfig, "vision_config": Qwen2_5OmniVisionEncoderConfig, "text_config": Qwen2_5OmniTextConfig, } def __init__( self, audio_config=None, vision_config=None, text_config=None, audio_token_index=151646, image_token_index=151655, video_token_index=151656, position_id_per_seconds=25, seconds_per_chunk=2, audio_start_token_id=151647, audio_end_token_id=151648, user_token_id=872, initializer_range=0.02, **kwargs, ): self.audio_token_index = audio_token_index self.image_token_index = image_token_index self.video_token_index = video_token_index self.user_token_id = user_token_id self.position_id_per_seconds = position_id_per_seconds self.seconds_per_chunk = seconds_per_chunk self.audio_start_token_id = audio_start_token_id self.audio_end_token_id = audio_end_token_id self.initializer_range = initializer_range if isinstance(vision_config, dict): vision_config = Qwen2_5OmniVisionEncoderConfig(**vision_config) elif vision_config is None: vision_config = Qwen2_5OmniVisionEncoderConfig() self.vision_config = vision_config if isinstance(audio_config, dict): audio_config = Qwen2_5OmniAudioEncoderConfig(**audio_config) elif audio_config is None: audio_config = Qwen2_5OmniAudioEncoderConfig() self.audio_config = audio_config if isinstance(text_config, dict): text_config = Qwen2_5OmniTextConfig(**text_config) elif text_config is None: text_config = Qwen2_5OmniTextConfig() self.text_config = text_config super().__init__(**kwargs) class Qwen2_5OmniTalkerConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Qwen2_5OmniTalkerForConditionalGeneration`]. It is used to instantiate an Qwen2.5-Omni-Talker model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Qwen2.5-Omni-Thinker. e.g. [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: audio_token_index (`int`, *optional*, defaults to 151646): The audio token index to encode the audio prompt. image_token_index (`int`, *optional*, defaults to 151655): The image token index to encode the image prompt. video_token_index (`int`, *optional*, defaults to 151656): The video token index to encode the video prompt. vocab_size (`int`, *optional*, defaults to 8448): Vocabulary size of the QwenOmni model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Qwen2VLModel`] tts_text_start_token_id (`int`, *optional*, defaults to 151860): The tts text start token index to encode the start of tts text. tts_text_end_token_id (`int`, *optional*, defaults to 151861): The tts text end token index to encode the end of tts text. tts_text_pad_token_id (`int`, *optional*, defaults to 151859): The tts text pad token index to encode the pad of tts text. tts_codec_start_token_id (`int`, *optional*, defaults to 8293): The tts codec start token index to encode the start of tts codec. tts_codec_end_token_id (`int`, *optional*, defaults to 8294): The tts codec end token index to encode the end of tts codec. tts_codec_pad_token_id (`int`, *optional*, defaults to 8292): The tts codec pad token index to encode the pad of tts codec. tts_codec_mask_token_id (`int`, *optional*, defaults to 8296): The tts codec mask token index to encode the mask of tts codec. vision_start_token_id (`int`, *optional*, defaults to 151652): The tts vision start token index to encode the start of vision. vision_end_token_id (`int`, *optional*, defaults to 151653): The tts vision end token index to encode the end of vision. embedding_size (`int`, *optional*, defaults to 3584): Dimension of the embedding representations. hidden_size (`int`, *optional*, defaults to 3584): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 18944): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 28): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 28): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 4): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 32768): The maximum sequence length that this model might ever be used with. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the rms normalization layers. head_dim (`int`, *optional*, defaults to 128): The dimension of each attention head. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 1000000.0): The base period of the RoPE embeddings. use_sliding_window (`bool`, *optional*, defaults to `False`): Whether to use sliding window attention. sliding_window (`int`, *optional*, defaults to 32768): Sliding window attention (SWA) window size. If not specified, will default to `4096`. max_window_layers (`int`, *optional*, defaults to 28): The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE position_id_per_seconds (`int`, *optional*, defaults to 25): The increment of position id per second. seconds_per_chunk (`int`, *optional*, defaults to 2): The duration in seconds of the chunk of audio and video data. audio_start_token_id (`int`, *optional*, defaults to 151647): The audio start token index to encode the audio prompt. audio_end_token_id (`int`, *optional*, defaults to 151648): The audio end token index to encode the audio prompt. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. spatial_merge_size (`int`, *optional*, defaults to 2): The size used for merging spatial dimensions. Example: ```python >>> from transformers import Qwen2_5OmniTalkerForConditionalGeneration, Qwen2_5OmniThinkerConfig, Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniVisionEncoderConfig >>> # Initializing a Qwen2_5OmniAudioEncoder config >>> audio_config = Qwen2_5OmniAudioEncoderConfig() >>> # Initializing a Qwen2 config >>> text_config = Qwen2Config() >>> # Initializing a Qwen2_5Omni configuration >>> configuration = Qwen2_5OmniThinkerConfig(audio_config, text_config) >>> # Initializing a model from the qwen2-audio style configuration >>> model = Qwen2_5OmniTalkerForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "qwen2_5_omni_talker" attribute_map = { "image_token_id": "image_token_index", "video_token_id": "video_token_index", "audio_token_id": "audio_token_index", } def __init__( self, audio_token_index=151646, image_token_index=151655, video_token_index=151656, vocab_size=8448, tts_text_start_token_id=151860, tts_text_end_token_id=151861, tts_text_pad_token_id=151859, tts_codec_start_token_id=8293, tts_codec_end_token_id=8294, tts_codec_pad_token_id=8292, tts_codec_mask_token_id=8296, vision_start_token_id=151652, vision_end_token_id=151653, embedding_size=3584, hidden_size=3584, intermediate_size=18944, num_hidden_layers=28, num_attention_heads=28, num_key_value_heads=4, hidden_act="silu", max_position_embeddings=32768, rms_norm_eps=1e-06, head_dim=128, use_cache=True, tie_word_embeddings=False, rope_theta=1000000.0, use_sliding_window=False, sliding_window=32768, max_window_layers=28, attention_dropout=0.0, rope_scaling=None, position_id_per_seconds=25, seconds_per_chunk=2, audio_start_token_id=151647, audio_end_token_id=151648, initializer_range=0.02, spatial_merge_size=2, **kwargs, ): self.audio_token_index = audio_token_index self.image_token_index = image_token_index self.video_token_index = video_token_index self.tts_text_start_token_id = tts_text_start_token_id self.tts_text_end_token_id = tts_text_end_token_id self.tts_text_pad_token_id = tts_text_pad_token_id self.tts_codec_start_token_id = tts_codec_start_token_id self.tts_codec_end_token_id = tts_codec_end_token_id self.tts_codec_pad_token_id = tts_codec_pad_token_id self.tts_codec_mask_token_id = tts_codec_mask_token_id self.vision_start_token_id = vision_start_token_id self.vision_end_token_id = vision_end_token_id self.vocab_size = vocab_size self.head_dim = head_dim self.embedding_size = embedding_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.use_sliding_window = use_sliding_window self.sliding_window = sliding_window self.max_window_layers = max_window_layers # 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.hidden_act = hidden_act self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.attention_dropout = attention_dropout self.rope_scaling = rope_scaling self.position_id_per_seconds = position_id_per_seconds # zf self.seconds_per_chunk = seconds_per_chunk # zf self.audio_start_token_id = audio_start_token_id # zf self.audio_end_token_id = audio_end_token_id # zf self.initializer_range = initializer_range self.spatial_merge_size = spatial_merge_size super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) class Qwen2_5OmniDiTConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of the Qwen2_5OmniToken2WavDiT used in the Qwen2.5-Omni-Token2Wav model. It defines the architecture of the DiT model, which is used for generating mel-spectrograms from tokens. Args: hidden_size (`int`, *optional*, defaults to 1024): The dimension of the model. num_hidden_layers (`int`, *optional*, defaults to 22): The number of transformer blocks in the DiT model. num_attention_heads (`int`, *optional*, defaults to 16): The number of attention heads in each transformer block. ff_mult (`int`, *optional*, defaults to 2): The multiplier for the feedforward layer in each transformer block. emb_dim (`int`, *optional*, defaults to 512): The dimension of the embedding layer. head_dim (`int`, *optional*, defaults to 64): The dimension of each attention head. repeats (`int`, *optional*, defaults to 2): The number of times the codec embeddings are repeated. num_embeds (`int`, *optional*, defaults to 8193): The number of unique embeddings in the codec. mel_dim (`int`, *optional*, defaults to 80): The dimension of the mel-spectrogram. dropout (`float`, *optional*, defaults to 0.1): The dropout rate for the transformer blocks. enc_emb_dim (`int`, *optional*, defaults to 192): The dimension of the pre-trained speaker embedding. enc_dim (`int`, *optional*, defaults to 128): The dimension of the encoder output. enc_channels (`List[int]`, *optional*, defaults to `[256, 256, 256, 256, 768]`): A list of output channels for each TDNN/SERes2Net layer in the encoder. enc_kernel_sizes (`List[int]`, *optional*, defaults to `[5, 3, 3, 3, 1]`): A list of kernel sizes for each layer in the encoder. enc_dilations (`List[int]`, *optional*, defaults to `[1, 2, 3, 4, 1]`): A list of dilations for each layer in the encoder. enc_attention_channels (`int`, *optional*, defaults to 64): The number of attention channels in the SqueezeExcitationBlock. enc_res2net_scale (`int`, *optional*, defaults to 2): The scale of the Res2Net block in the encoder. enc_se_channels (`int`, *optional*, defaults to 64): The number of output channels after squeeze in the SqueezeExcitationBlock. """ model_type = "qwen2_5_omni_dit" def __init__( self, hidden_size=1024, num_hidden_layers=22, num_attention_heads=16, ff_mult=2, emb_dim=512, head_dim=64, rope_theta=10000.0, max_position_embeddings=32768, block_size=24, look_ahead_layers=[10], look_backward_layers=[0, 20], repeats=2, num_embeds=8193, mel_dim=80, dropout=0.1, enc_emb_dim=192, enc_dim=128, enc_channels=[256, 256, 256, 256, 768], enc_kernel_sizes=[5, 3, 3, 3, 1], enc_dilations=[1, 2, 3, 4, 1], enc_attention_channels=64, enc_res2net_scale=2, enc_se_channels=64, **kwargs, ): self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.ff_mult = ff_mult self.emb_dim = emb_dim self.head_dim = head_dim self.rope_theta = rope_theta self.max_position_embeddings = max_position_embeddings self.block_size = block_size self.look_ahead_layers = look_ahead_layers self.look_backward_layers = look_backward_layers self.repeats = repeats self.num_embeds = num_embeds self.mel_dim = mel_dim self.dropout = dropout self.enc_emb_dim = enc_emb_dim self.enc_dim = enc_dim self.enc_channels = enc_channels self.enc_kernel_sizes = enc_kernel_sizes self.enc_dilations = enc_dilations self.enc_attention_channels = enc_attention_channels self.enc_res2net_scale = enc_res2net_scale self.enc_se_channels = enc_se_channels super().__init__(**kwargs) class Qwen2_5OmniBigVGANConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of the Qwen2_5OmniToken2WavBigVGAN module used in the Qwen2.5-Omni-Token2Wav model. It defines the architecture of the BigVGAN model, which is used for converting mel-spectrograms to waveforms. Args: mel_dim (`int`, *optional*, defaults to 80): The dimension of the mel-spectrogram. upsample_initial_channel (`int`, *optional*, defaults to 1536): The number of channels in the initial upsampling layer. resblock_kernel_sizes (`List[int]`, *optional*, defaults to `[3, 7, 11]`): A list of kernel sizes for each residual block. resblock_dilation_sizes (`List[List[int]]`, *optional*, defaults to `[[1, 3, 5], [1, 3, 5], [1, 3, 5]]`): A list of dilation sizes for each residual block. upsample_rates (`List[int]`, *optional*, defaults to `[5, 3, 2, 2, 2, 2]`): A list of upsampling rates for each upsampling layer. upsample_kernel_sizes (`List[int]`, *optional*, defaults to `[11, 7, 4, 4, 4, 4]`): A list of kernel sizes for each upsampling layer. """ model_type = "qwen2_5_omni_bigvgan" def __init__( self, mel_dim=80, upsample_initial_channel=1536, resblock_kernel_sizes=[3, 7, 11], resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]], upsample_rates=[5, 3, 2, 2, 2, 2], upsample_kernel_sizes=[11, 7, 4, 4, 4, 4], **kwargs, ): self.mel_dim = mel_dim self.upsample_initial_channel = upsample_initial_channel self.resblock_kernel_sizes = resblock_kernel_sizes self.resblock_dilation_sizes = resblock_dilation_sizes self.upsample_rates = upsample_rates self.upsample_kernel_sizes = upsample_kernel_sizes super().__init__(**kwargs) class Qwen2_5OmniToken2WavConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Qwen2_5OmniToken2WavModel`]. It is used to instantiate the Qwen2.5-Omni-Token2Wav model which combines a Diffusion Transformer (DiT) for mel-spectrogram generation with a BigVGAN model for waveform synthesis. The configuration contains sub-configurations for both components. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: dit_config ([`DiT_Args`], *optional*): Configuration class for the Diffusion Transformer (DiT) module responsible for generating mel-spectrograms. bigvgan_config ([`BigVGAN_Args`], *optional*): Configuration class for the BigVGAN module responsible for converting mel-spectrograms to waveforms. Example: ```python >>> from transformers import Qwen2_5OmniToken2WavModel, DiT_Args, BigVGAN_Args >>> # Initialize DiT configuration >>> dit_config = DiT_Args( ... dim=1024, ... depth=22, ... heads=16, ... ff_mult=2 ... ) >>> # Initialize BigVGAN configuration >>> bigvgan_config = BigVGAN_Args( ... mel_dim=80, ... upsample_rates=[5,3,2,2,2,2] ... ) >>> # Initialize main configuration >>> config = Qwen2_5OmniToken2WavConfig(dit_config, bigvgan_config) >>> # Initialize model with config >>> model = Qwen2_5OmniToken2Wav(config) >>> # Accessing the model configuration >>> configuration = model.config ``` """ model_type = "qwen2_5_omni_token2wav" sub_configs = { "dit_config": Qwen2_5OmniDiTConfig, "bigvgan_config": Qwen2_5OmniBigVGANConfig, } def __init__(self, dit_config=None, bigvgan_config=None, **kwargs): if dit_config is None: dit_config = {} if bigvgan_config is None: bigvgan_config = {} self.dit_config = Qwen2_5OmniDiTConfig(**dit_config) self.bigvgan_config = Qwen2_5OmniBigVGANConfig(**bigvgan_config) super().__init__(**kwargs) class Qwen2_5OmniConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`Qwen2_5OmniForConditionalGeneration`]. It is used to instantiate a Qwen2.5Omni model according to the specified sub-models configurations, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: thinker_config (`dict`, *optional*): Configuration of the underlying thinker sub-model. talker_config (`dict`, *optional*): Configuration of the underlying talker sub-model. token2wav_config (`dict`, *optional*): Configuration of the underlying codec sub-model. enable_audio_output (`bool`, *optional*, defaults to `True`): Whether enable audio output and load talker and token2wav module. Example: ```python >>> from transformers import ( ... Qwen2_5OmniThinkerConfig, ... Qwen2_5OmniTalkerConfig, ... Qwen2_5OmniToken2WavConfig, ... Qwen2_5OmniForConditionalGeneration, ... Qwen2_5OmniConfig, ... ) >>> # Initializing sub-modules configurations. >>> thinker_config = Qwen2_5OmniThinkerConfig() >>> talker_config = Qwen2_5OmniTalkerConfig() >>> token2wav_config = Qwen2_5OmniToken2WavConfig() >>> # Initializing a module style configuration >>> configuration = Qwen2_5OmniConfig.from_sub_model_configs( ... thinker_config, talker_config, token2wav_config ... ) >>> # Initializing a model (with random weights) >>> model = Qwen2_5OmniForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` """ model_type = "qwen2_5_omni" sub_configs = { "thinker_config": Qwen2_5OmniThinkerConfig, "talker_config": Qwen2_5OmniTalkerConfig, "token2wav_config": Qwen2_5OmniToken2WavConfig, } def __init__( self, thinker_config=None, talker_config=None, token2wav_config=None, enable_audio_output: bool = True, **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 token2wav_config is None: token2wav_config = {} logger.info("token2wav_config is None. Initializing token2wav model with default values") self.thinker_config = Qwen2_5OmniThinkerConfig(**thinker_config) self.talker_config = Qwen2_5OmniTalkerConfig(**talker_config) self.token2wav_config = Qwen2_5OmniToken2WavConfig(**token2wav_config) self.enable_audio_output = enable_audio_output super().__init__(**kwargs) 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() __all__ = ["Qwen2_5OmniConfig", "Qwen2_5OmniThinkerConfig", "Qwen2_5OmniTalkerConfig", "Qwen2_5OmniToken2WavConfig"]