vibevoice/modular/configuration_vibevoice_streaming.py

""" VibeVoice Streaming model configuration"""

from transformers.configuration_utils import PretrainedConfig 
from transformers.utils import logging

from transformers.models.qwen2.configuration_qwen2 import Qwen2Config

from .configuration_vibevoice import VibeVoiceAcousticTokenizerConfig, VibeVoiceDiffusionHeadConfig

logger = logging.get_logger(__name__)


class VibeVoiceStreamingConfig(PretrainedConfig):
    model_type = "vibevoice_streaming"
    is_composition = True
    sub_configs = {
        "acoustic_tokenizer_config": VibeVoiceAcousticTokenizerConfig,
        "decoder_config": Qwen2Config,
        "diffusion_head_config": VibeVoiceDiffusionHeadConfig,
    }
    # keys_to_ignore_at_inference = ["past_key_values"]
    # Default tensor parallel plan for base model `Qwen2`
    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",
    }
    
    def __init__(
        self,
        acoustic_tokenizer_config=None,
        decoder_config=None,
        diffusion_head_config=None,
        tts_backbone_num_hidden_layers=20,
        **kwargs
    ):

        # kwargs["_attn_implementation"] = "flash_attention_2"
        kwargs["_attn_implementation_autoset"] = False 

        if acoustic_tokenizer_config is None:
            self.acoustic_tokenizer_config = self.sub_configs["acoustic_tokenizer_config"]()
        elif isinstance(acoustic_tokenizer_config, dict):
            acoustic_tokenizer_config["model_type"] = "vibevoice_acoustic_tokenizer"
            self.acoustic_tokenizer_config = self.sub_configs["acoustic_tokenizer_config"](**acoustic_tokenizer_config)
        elif isinstance(acoustic_tokenizer_config, VibeVoiceAcousticTokenizerConfig):
            # If an instance of the config class is provided
            self.acoustic_tokenizer_config = acoustic_tokenizer_config

        if decoder_config is None:
            self.decoder_config = self.sub_configs["decoder_config"]()
        elif isinstance(decoder_config, dict):
            # If a dictionary is provided, instantiate the config class with it
            # self.decoder_config = self.sub_configs["decoder_config"](**decoder_config)
            if decoder_config.get("model_type", '') == "qwen2":
                self.decoder_config = Qwen2Config(**decoder_config)
            else:
                raise ValueError(f"Unsupported decoder model type: {decoder_config.get('model_type', '')}")
        elif isinstance(decoder_config, (Qwen2Config,)):
            # If an instance of the config class is provided
            self.decoder_config = decoder_config

        if diffusion_head_config is None:
            self.diffusion_head_config = self.sub_configs["diffusion_head_config"]()
        elif isinstance(diffusion_head_config, dict):
            diffusion_head_config["model_type"] = "vibevoice_diffusion_head"
            self.diffusion_head_config = self.sub_configs["diffusion_head_config"](**diffusion_head_config)
        elif isinstance(diffusion_head_config, VibeVoiceDiffusionHeadConfig):
            # If an instance of the config class is provided
            self.diffusion_head_config = diffusion_head_config

        # other parameters
        self.acoustic_vae_dim = getattr(self.acoustic_tokenizer_config, 'vae_dim', 64)
        # The decoder of the model is divided into two components. The lower Transformer layers are only used for encoding text, while the upper Transformer layers are used for encoding text and generating speech. `tts_backbone_num_hidden_layers` indicates the number of upper layers used for TTS.
        self.tts_backbone_num_hidden_layers = tts_backbone_num_hidden_layers

        super().__init__(**kwargs)

__all__ = [
    "VibeVoiceStreamingConfig"
]