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from transformers import LlamaConfig, PretrainedConfig
from transformers.utils import logging
from transformers import Qwen2Config, Qwen2Model, Qwen2ForCausalLM, AutoConfig, AutoModelForCausalLM
logger = logging.get_logger(__name__)
class InternVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`InternVisionModel`]. It is used to
instantiate a vision encoder according to the specified arguments, defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
num_channels (`int`, *optional*, defaults to 3):
Number of color channels in the input images (e.g., 3 for RGB).
patch_size (`int`, *optional*, defaults to 14):
The size (resolution) of each patch.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
qkv_bias (`bool`, *optional*, defaults to `False`):
Whether to add a bias to the queries and values in the self-attention layers.
hidden_size (`int`, *optional*, defaults to 3200):
Dimensionality of the encoder layers and the pooler layer.
num_attention_heads (`int`, *optional*, defaults to 25):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 12800):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
qk_normalization (`bool`, *optional*, defaults to `True`):
Whether to normalize the queries and keys in the self-attention layers.
num_hidden_layers (`int`, *optional*, defaults to 48):
Number of hidden layers in the Transformer encoder.
use_flash_attn (`bool`, *optional*, defaults to `True`):
Whether to use flash attention mechanism.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-6):
The epsilon used by the layer normalization layers.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
drop_path_rate (`float`, *optional*, defaults to 0.0):
Dropout rate for stochastic depth.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float`, *optional*, defaults to 0.1):
A factor for layer scale.
"""
model_type = 'intern_vit_6b'
def __init__(
self,
num_channels=3,
patch_size=14,
image_size=448,
qkv_bias=False,
hidden_size=3200,
num_attention_heads=25,
intermediate_size=12800,
qk_normalization=True,
num_hidden_layers=45,
use_flash_attn=True,
hidden_act='gelu',
layer_norm_eps=1e-6,
dropout=0.0,
drop_path_rate=0.0,
attention_dropout=0.0,
initializer_range=1e-10,
initializer_factor=0.1,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.dropout = dropout
self.drop_path_rate = drop_path_rate
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.patch_size = patch_size
self.image_size = image_size
self.initializer_range = initializer_range
self.initializer_factor = initializer_factor
self.attention_dropout = attention_dropout
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.qkv_bias = qkv_bias
self.qk_normalization = qk_normalization
self.use_flash_attn = use_flash_attn
class OmChatConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`OmChatForConditionalGeneration`]. It is used to instantiate an
Llava-NeXT 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 [llava-hf/llava-v1.6-mistral-7b-hf](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf)
model.
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 `CLIPVisionConfig`):
The config object or dictionary of the vision backbone.
text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`):
The config object or dictionary of the text backbone.
ignore_index (`int`, *optional*, defaults to -100):
The ignore index for the loss function.
image_token_index (`int`, *optional*, defaults to 32000):
The image token index to encode the image prompt.
projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
The activation function used by the multimodal projector.
vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
The feature selection strategy used to select the vision feature from the vision backbone.
Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features.
If `"full"`, the full vision features are used.
vision_feature_layer (`int`, *optional*, defaults to -2):
The index of the layer to select the vision feature.
image_grid_pinpoints (`List`, *optional*, defaults to `[[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]`):
A list of possible resolutions to use for processing high resolution images. Each item in the list should be a tuple or list
of the form `(height, width)`.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied.
Example:
```python
>>> from transformers import OmChatForConditionalGeneration, OmChatConfig, CLIPVisionConfig, LlamaConfig
>>> # Initializing a CLIP-vision config
>>> vision_config = CLIPVisionConfig()
>>> # Initializing a Llama config
>>> text_config = LlamaConfig()
>>> # Initializing a Llava-Next llava-hf/llava-v1.6-mistral-7b-hf style configuration
>>> configuration = OmChatConfig(vision_config, text_config)
>>> # Initializing a model from the llava-hf/llava-v1.6-mistral-7b-hf style configuration
>>> model = OmChatForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "omchat"
is_composition = False
def __init__(
self,
vision_config=None,
text_config=None,
ignore_index=-100,
image_token_index=32000,
projector_hidden_act="gelu",
vision_feature_select_strategy="default",
vision_feature_layer=-1,
image_grid_pinpoints=None,
tie_word_embeddings=False,
**kwargs,
):
self.ignore_index = ignore_index
self.image_token_index = image_token_index
self.projector_hidden_act = projector_hidden_act
if vision_feature_select_strategy not in ["default", "full"]:
raise ValueError(
"vision_feature_select_strategy should be one of 'default', 'full'."
f"Got: {vision_feature_select_strategy}"
)
self.vision_feature_select_strategy = vision_feature_select_strategy
self.vision_feature_layer = vision_feature_layer
image_grid_pinpoints = (
image_grid_pinpoints
if image_grid_pinpoints is not None
else [[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]
)
self.image_grid_pinpoints = image_grid_pinpoints
if isinstance(vision_config, dict):
vision_config = InternVisionConfig(**vision_config)
self.vision_config = vision_config
if isinstance(text_config, dict):
text_config = Qwen2Config(**text_config)
self.text_config = text_config
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
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