# Copyright 2025 The Nari Labs and 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. """Dia model configuration""" from ...configuration_utils import PreTrainedConfig from ...modeling_rope_utils import RopeParameters from ...utils import logging logger = logging.get_logger(__name__) class DiaEncoderConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`DiaEncoder`]. It is used to instantiate a Dia encoder according to the specified arguments, defining the encoder architecture. Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: max_position_embeddings (`int`, *optional*, defaults to 1024): The maximum sequence length that this model might ever be used with. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. hidden_size (`int`, *optional*, defaults to 1024): Dimensionality of the encoder layers and the pooler layer. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 16): Number of key and value heads for each attention layer in the Transformer encoder. head_dim (`int`, *optional*, defaults to 128): Dimensionality of the attention head. intermediate_size (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the normalization layers. vocab_size (`int`, *optional*, defaults to 256): Vocabulary size of the Dia model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DiaModel`]. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"swish"` and `"gelu_new"` are supported. rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. """ model_type = "dia_encoder" def __init__( self, max_position_embeddings: int = 1024, num_hidden_layers: int = 12, hidden_size: int = 1024, num_attention_heads: int = 16, num_key_value_heads: int = 16, head_dim: int = 128, intermediate_size: int = 4096, norm_eps: float = 1e-5, vocab_size: int = 256, hidden_act: str = "silu", rope_parameters: RopeParameters | None = None, initializer_range: float = 0.02, **kwargs, ): self.max_position_embeddings = max_position_embeddings self.num_hidden_layers = num_hidden_layers self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_attention_heads = num_attention_heads self.head_dim = head_dim self.norm_eps = norm_eps self.vocab_size = vocab_size self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rope_parameters = rope_parameters super().__init__(**kwargs) class DiaDecoderConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`DiaDecoder`]. It is used to instantiate a Dia decoder according to the specified arguments, defining the decoder architecture. Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: max_position_embeddings (`int`, *optional*, defaults to 3072): The maximum sequence length that this model might ever be used with. num_hidden_layers (`int`, *optional*, defaults to 18): Number of hidden layers in the Transformer decoder. hidden_size (`int`, *optional*, defaults to 2048): Dimensionality of the decoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 8192): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 4): Number of key and value heads for each attention layer in the Transformer decoder. head_dim (`int`, *optional*, defaults to 128): Dimensionality of the attention head. cross_num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each cross-attention layer in the Transformer decoder. cross_head_dim (`int`, *optional*, defaults to 128): Dimensionality of the cross-attention head. cross_num_key_value_heads (`int`, *optional*, defaults to 16): Number of key and value heads for each cross-attention layer in the Transformer decoder. cross_hidden_size (`int`, *optional*, defaults to 1024): Dimensionality of the cross-attention layers. norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the normalization layers. vocab_size (`int`, *optional*, defaults to 1028): Vocabulary size of the Dia model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DiaModel`]. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. If string, `"gelu"`, `"relu"`, `"swish"` and `"gelu_new"` are supported. num_channels (`int`, *optional*, defaults to 9): Number of channels for the Dia decoder. rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). is_encoder_decoder (`bool`, *optional*, defaults to `True`): Indicating that this model is part of an encoder-decoder architecture. pad_token_id (`int`, *optional*, defaults to 1025): The token id used for padding sequences to the same length within a batch. eos_token_id (`int`, *optional*, defaults to 1024): The token id representing the end-of-sequence token, indicating that generation should stop. bos_token_id (`int`, *optional*, defaults to 1026): The token id representing the beginning-of-sequence token, used to initialize decoding. """ model_type = "dia_decoder" def __init__( self, max_position_embeddings: int = 3072, num_hidden_layers: int = 18, hidden_size: int = 2048, intermediate_size: int = 8192, num_attention_heads: int = 16, num_key_value_heads: int = 4, head_dim: int = 128, cross_num_attention_heads: int = 16, cross_head_dim: int = 128, cross_num_key_value_heads: int = 16, cross_hidden_size: int = 1024, norm_eps: float = 1e-5, vocab_size: int = 1028, hidden_act: str = "silu", num_channels: int = 9, rope_parameters: RopeParameters | None = None, initializer_range: float = 0.02, use_cache: bool = True, is_encoder_decoder: bool = True, pad_token_id: int = 1025, eos_token_id: int = 1024, bos_token_id: int = 1026, **kwargs, ): self.max_position_embeddings = max_position_embeddings self.num_hidden_layers = num_hidden_layers 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.head_dim = head_dim self.cross_num_key_value_heads = cross_num_key_value_heads self.cross_num_attention_heads = cross_num_attention_heads self.cross_head_dim = cross_head_dim self.cross_hidden_size = cross_hidden_size self.norm_eps = norm_eps self.vocab_size = vocab_size self.hidden_act = hidden_act self.num_channels = num_channels self.initializer_range = initializer_range self.use_cache = use_cache self.rope_parameters = rope_parameters self.pad_token_id = pad_token_id self.eos_token_id = eos_token_id self.bos_token_id = bos_token_id super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs) class DiaConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`DiaModel`]. It is used to instantiate a Dia 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 [nari-labs/Dia-1.6B](https://huggingface.co/nari-labs/Dia-1.6B) architecture. Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: encoder_config (`DiaEncoderConfig`, *optional*): Configuration for the encoder part of the model. If not provided, a default `DiaEncoderConfig` will be used. decoder_config (`DiaDecoderConfig`, *optional*): Configuration for the decoder part of the model. If not provided, a default `DiaDecoderConfig` will be used. norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the normalization layers. is_encoder_decoder (`bool`, *optional*, defaults to `True`): Indicating that this model uses an encoder-decoder architecture. pad_token_id (`int`, *optional*): Deprecated. Please set this on `DiaDecoderConfig` directly. If provided, it will be forwarded to `decoder_config`. eos_token_id (`int`, *optional*): Deprecated. Please set this on `DiaDecoderConfig` directly. If provided, it will be forwarded to `decoder_config`. bos_token_id (`int`, *optional*): Deprecated. Please set this on `DiaDecoderConfig` directly. If provided, it will be forwarded to `decoder_config`. delay_pattern (`list[int]`, *optional*, defaults to `[0, 8, 9, 10, 11, 12, 13, 14, 15]`): The delay pattern for the decoder. The length of this list must match `decoder_config.num_channels`. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Example: ```python >>> from transformers import DiaConfig, DiaModel >>> # Initializing a DiaConfig with default values >>> configuration = DiaConfig() >>> # Initializing a DiaModel (with random weights) from the configuration >>> model = DiaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` """ model_type = "dia" keys_to_ignore_at_inference = ["past_key_values"] sub_configs = {"encoder_config": DiaEncoderConfig, "decoder_config": DiaDecoderConfig} def __init__( self, encoder_config: DiaEncoderConfig | None = None, decoder_config: DiaDecoderConfig | None = None, norm_eps: float = 1e-5, is_encoder_decoder: bool = True, pad_token_id: int | None = None, eos_token_id: int | None = None, bos_token_id: int | None = None, delay_pattern: list[int] | None = None, initializer_range: float = 0.02, use_cache: bool = True, **kwargs, ): if isinstance(encoder_config, dict): encoder_config = DiaEncoderConfig(**encoder_config) if isinstance(decoder_config, dict): decoder_config = DiaDecoderConfig(**decoder_config) self.encoder_config = encoder_config if encoder_config is not None else DiaEncoderConfig() self.decoder_config = decoder_config if decoder_config is not None else DiaDecoderConfig() self.norm_eps = norm_eps self.delay_pattern = delay_pattern if delay_pattern is not None else [0, 8, 9, 10, 11, 12, 13, 14, 15] self.initializer_range = initializer_range self.use_cache = use_cache # TODO: Remove token ID forwarding once the `nari-labs/Dia-1.6B` # checkpoint is updated if pad_token_id is not None: logger.warning_once( "Passing `pad_token_id` to `DiaConfig` is deprecated. " "Please set it directly on `DiaDecoderConfig` instead." ) self.decoder_config.pad_token_id = pad_token_id if eos_token_id is not None: logger.warning_once( "Passing `eos_token_id` to `DiaConfig` is deprecated. " "Please set it directly on `DiaDecoderConfig` instead." ) self.decoder_config.eos_token_id = eos_token_id if bos_token_id is not None: logger.warning_once( "Passing `bos_token_id` to `DiaConfig` is deprecated. " "Please set it directly on `DiaDecoderConfig` instead." ) self.decoder_config.bos_token_id = bos_token_id assert self.decoder_config.num_channels == len(self.delay_pattern), ( "Number of channels must match delay pattern length." ) super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs) def get_text_config(self, *args, **kwargs): """Defaulting to audio config as it's the decoder in this case which is usually the text backbone""" return self.decoder_config __all__ = ["DiaConfig", "DiaEncoderConfig", "DiaDecoderConfig"]