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# coding=utf-8
"""OcisMllama model configuration"""
from typing import Dict, List, Optional
from transformers import WhisperConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
from transformers.models.mllama.configuration_mllama import (
MllamaTextConfig, MllamaVisionConfig)
from transformers.utils import logging
logger = logging.get_logger(__name__)
class MllamaAudioConfig(PretrainedConfig):
model_type = "ocismllama"
def __init__(
self,
output_hidden_size: int = 4096,
hidden_size: int = 4096,
audio_model_id: str = 'taipei-1-mllama-project-2024/whisper-large-v3-turbo-encoder',
stack_factor: int = 8,
norm_init: float = 0.4,
**kwargs,
):
self.output_hidden_size = output_hidden_size
self.hidden_size = hidden_size
self.stack_factor = stack_factor
self.norm_init = norm_init
self.audio_model_id = audio_model_id
whisper_config = WhisperConfig.from_pretrained(audio_model_id)
self.input_hidden_size = whisper_config.hidden_size
super().__init__(**kwargs)
class OcisMllamaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MllamaForConditionalGeneration`]. It is used to instantiate an
Mllama 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 Mllama-9B.
e.g. [meta-llama/Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `MllamaVisionConfig`):
The config object or dictionary of the vision backbone.
text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `MllamaTextConfig`):
The config object or dictionary of the text backbone.
image_token_index (`int`, *optional*, defaults to 128256):
The image token index to encode the image prompt.
Example:
```python
>>> from transformers import MllamaForConditionalGeneration, MllamaConfig, MllamaVisionConfig, MllamaTextConfig
>>> # Initializing a CLIP-vision config
>>> vision_config = MllamaVisionConfig()
>>> # Initializing a Llama config
>>> text_config = MllamaTextConfig()
>>> # Initializing a mllama-11b style configuration
>>> configuration = MllamaConfig(vision_config, text_config)
>>> # Initializing a model from the mllama-11b style configuration
>>> model = MllamaForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "ocismllama"
sub_configs = {"vision_config": MllamaVisionConfig,
"text_config": MllamaTextConfig,
"audio_config": MllamaAudioConfig}
is_composition = True
def __init__(
self,
vision_config=None,
text_config=None,
audio_config=None,
image_token_index=128256,
**kwargs,
):
if vision_config is None:
self.vision_config = MllamaVisionConfig()
logger.info("vision_config is None, using default mllama vision config")
elif isinstance(vision_config, dict):
self.vision_config = MllamaVisionConfig(**vision_config)
elif isinstance(vision_config, MllamaVisionConfig):
self.vision_config = vision_config
self.image_token_index = image_token_index
if text_config is None:
self.text_config = MllamaTextConfig()
logger.info("text_config is None, using default mllama text config")
elif isinstance(text_config, dict):
self.text_config = MllamaTextConfig(**text_config)
elif isinstance(text_config, MllamaTextConfig):
self.text_config = text_config
if audio_config is None:
self.audio_config = MllamaAudioConfig(output_hidden_size=self.text_config.hidden_size)
logger.info("audio_config is None, using default mllama audio config")
elif isinstance(audio_config, dict):
self.audio_config = MllamaAudioConfig(**audio_config)
elif isinstance(audio_config, MllamaAudioConfig):
self.audio_config = audio_config
super().__init__(**kwargs) |