# coding=utf-8 # 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 typing import Optional from ...configuration_utils import PretrainedConfig from ...modeling_rope_utils import rope_config_validation 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_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. 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. """ 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_theta: float = 10000.0, rope_scaling: Optional[dict] = 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.rope_theta = rope_theta self.rope_scaling = rope_scaling # Validate the correctness of rotary position embeddings parameters # BC: if there is a 'type' field, copy it 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) self.initializer_range = initializer_range 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_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. 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. 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. """ 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_theta: float = 10000.0, rope_scaling: Optional[dict] = None, initializer_range: float = 0.02, use_cache: bool = True, is_encoder_decoder: bool = True, **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.rope_theta = rope_theta self.rope_scaling = rope_scaling # Validate the correctness of rotary position embeddings parameters # BC: if there is a 'type' field, copy it 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) self.initializer_range = initializer_range self.use_cache = use_cache 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*, defaults to 1025): Padding token id. eos_token_id (`int`, *optional*, defaults to 1024): End of stream token id. bos_token_id (`int`, *optional*, defaults to 1026): Beginning of stream token id. 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: Optional[DiaEncoderConfig] = None, decoder_config: Optional[DiaDecoderConfig] = None, norm_eps: float = 1e-5, is_encoder_decoder: bool = True, pad_token_id: int = 1025, eos_token_id: int = 1024, bos_token_id: int = 1026, delay_pattern: Optional[list[int]] = 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 assert self.decoder_config.num_channels == len(self.delay_pattern), ( "Number of channels must match delay pattern length." ) super().__init__( pad_token_id=pad_token_id, eos_token_id=eos_token_id, bos_token_id=bos_token_id, is_encoder_decoder=is_encoder_decoder, **kwargs, ) def get_text_config(self, decoder=False): """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"]