# Copyright 2023 Meta AI 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. """MusicGen model configuration""" from ...configuration_utils import PreTrainedConfig from ...utils import logging from ..auto.configuration_auto import AutoConfig logger = logging.get_logger(__name__) class MusicgenDecoderConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of an [`MusicgenDecoder`]. It is used to instantiate a MusicGen decoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the MusicGen [facebook/musicgen-small](https://huggingface.co/facebook/musicgen-small) architecture. 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 2048): Vocabulary size of the MusicgenDecoder model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MusicgenDecoder`]. hidden_size (`int`, *optional*, defaults to 1024): Dimensionality of the layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 24): Number of decoder layers. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer block. ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer block. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the decoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, text_encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with. Typically, set this to something large just in case (e.g., 512 or 1024 or 2048). initializer_factor (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556) for more details. scale_embedding (`bool`, *optional*, defaults to `False`): Scale embeddings by diving by sqrt(hidden_size). use_cache (`bool`, *optional*, defaults to `True`): Whether the model should return the last key/values attentions (not used by all models) num_codebooks (`int`, *optional*, defaults to 4): The number of parallel codebooks forwarded to the model. tie_word_embeddings(`bool`, *optional*, defaults to `False`): Whether input and output word embeddings should be tied. audio_channels (`int`, *optional*, defaults to 1 Number of channels in the audio data. Either 1 for mono or 2 for stereo. Stereo models generate a separate audio stream for the left/right output channels. Mono models generate a single audio stream output. """ model_type = "musicgen_decoder" base_config_key = "decoder_config" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=2048, max_position_embeddings=2048, num_hidden_layers=24, ffn_dim=4096, num_attention_heads=16, layerdrop=0.0, use_cache=True, activation_function="gelu", hidden_size=1024, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, initializer_factor=0.02, scale_embedding=False, num_codebooks=4, audio_channels=1, pad_token_id=2048, bos_token_id=2048, eos_token_id=None, tie_word_embeddings=False, is_decoder=False, add_cross_attention=False, cross_attention_hidden_size=None, **kwargs, ): self.is_decoder = is_decoder self.add_cross_attention = add_cross_attention self.cross_attention_hidden_size = cross_attention_hidden_size self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.ffn_dim = ffn_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.initializer_factor = initializer_factor self.layerdrop = layerdrop self.use_cache = use_cache self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.num_codebooks = num_codebooks if audio_channels not in [1, 2]: raise ValueError(f"Expected 1 (mono) or 2 (stereo) audio channels, got {audio_channels} channels.") self.audio_channels = audio_channels self.tie_word_embeddings = tie_word_embeddings self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id super().__init__(**kwargs) class MusicgenConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`MusicgenModel`]. It is used to instantiate a MusicGen model according to the specified arguments, defining the text encoder, audio encoder and MusicGen decoder configs. Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: text_encoder (`Union[dict, `PretrainedConfig`]`): An instance of a configuration object that defines the text encoder config. audio_encoder (`Union[dict, `PretrainedConfig`]`): An instance of a configuration object that defines the audio encoder config. decoder (`Union[dict, `PretrainedConfig`]`): An instance of a configuration object that defines the decoder config. Example: ```python >>> from transformers import ( ... MusicgenConfig, ... MusicgenDecoderConfig, ... T5Config, ... EncodecConfig, ... MusicgenForConditionalGeneration, ... ) >>> # Initializing text encoder, audio encoder, and decoder model configurations >>> text_encoder_config = T5Config() >>> audio_encoder_config = EncodecConfig() >>> decoder_config = MusicgenDecoderConfig() >>> configuration = MusicgenConfig( ... text_encoder=text_encoder_config, ... audio_encoder=audio_encoder_config, ... decoder=decoder_config, ... ) >>> # Initializing a MusicgenForConditionalGeneration (with random weights) from the facebook/musicgen-small style configuration >>> model = MusicgenForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> config_text_encoder = model.config.text_encoder >>> config_audio_encoder = model.config.audio_encoder >>> config_decoder = model.config.decoder >>> # Saving the model, including its configuration >>> model.save_pretrained("musicgen-model") >>> # loading model and config from pretrained folder >>> musicgen_config = MusicgenConfig.from_pretrained("musicgen-model") >>> model = MusicgenForConditionalGeneration.from_pretrained("musicgen-model", config=musicgen_config) ```""" model_type = "musicgen" sub_configs = { "text_encoder": AutoConfig, "audio_encoder": AutoConfig, "decoder": MusicgenDecoderConfig, } has_no_defaults_at_init = True def __init__(self, text_encoder, audio_encoder, decoder, **kwargs): if isinstance(text_encoder, dict): text_encoder_model_type = text_encoder.pop("model_type") text_encoder = AutoConfig.for_model(text_encoder_model_type, **text_encoder) if isinstance(audio_encoder, dict): audio_encoder_model_type = audio_encoder.pop("model_type") audio_encoder = AutoConfig.for_model(audio_encoder_model_type, **audio_encoder) if isinstance(decoder, dict): decoder = MusicgenDecoderConfig(**decoder) self.text_encoder = text_encoder self.audio_encoder = audio_encoder self.decoder = decoder self.initializer_factor = self.decoder.initializer_factor self.tie_encoder_decoder = kwargs.get("tie_encoder_decoder", False) kwargs["is_encoder_decoder"] = True super().__init__(**kwargs) @property # This is a property because you might want to change the codec model on the fly def sampling_rate(self): return self.audio_encoder.sampling_rate __all__ = ["MusicgenConfig", "MusicgenDecoderConfig"]