YaoyaoChang
104 lines
4.6 KiB
Python
104 lines
4.6 KiB
Python
""" VibeVoice Streaming model configuration"""
|
|
|
|
import torch
|
|
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, _convert_dtype_to_string
|
|
|
|
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)
|
|
|
|
def get_text_config(self, decoder=False):
|
|
"""Returns the decoder config (required for transformers >= 4.57 cache compatibility)."""
|
|
return self.decoder_config
|
|
|
|
@property
|
|
def num_hidden_layers(self):
|
|
"""Proxy to decoder_config.num_hidden_layers (required for transformers >= 4.57)."""
|
|
return self.decoder_config.num_hidden_layers
|
|
|
|
def to_dict(self):
|
|
"""
|
|
Override to_dict to handle torch.dtype serialization.
|
|
|
|
Fixes: https://github.com/microsoft/VibeVoice/issues/199
|
|
"""
|
|
output = super().to_dict()
|
|
return _convert_dtype_to_string(output)
|
|
|
|
__all__ = [
|
|
"VibeVoiceStreamingConfig"
|
|
] |