File size: 9,188 Bytes
f0b90d2 c57624d f0b90d2 c57624d f0b90d2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 | """
Molmo2 configuration
"""
from typing import Optional
from transformers import PretrainedConfig, LogitsProcessor
from transformers.utils import logging
from .configuration_molmo2 import Molmo2TextConfig, Molmo2VitConfig, \
Molmo2AdapterConfig
logger = logging.get_logger(__name__)
class MolmoPointAdapterConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of Molmo2Adapter. With Molmo2VitConfig,
It is used to instantiate an Molmo2VisionBackbone 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.
Example:
```python
>>> from transformers import Molmo2VitConfig, Molmo2AdapterConfig, Molmo2VisionBackbone
>>> # Initializing a Molmo2VitConfig and a Molmo2AdapterConfig
>>> vit_config = Molmo2VitConfig()
>>> adapter_config = MolmoPoolingConfig()
>>> # Initializing a Molmo2VisionBackbone (with random weights)
>>> model = Molmo2VisionBackbone(vit_config, adapter_config)
>>> # Accessing the model configuration
>>> vit_configuration = model.vit_config
>>> adapter_configuration = model.adapter_config
```"""
model_type = "molmo_point"
base_config_key = "adapter_config"
def __init__(
self,
vit_layers: tuple = (-3, -9),
pooling_attention_mask: bool = False,
hidden_size: int = 1152,
num_attention_heads: int = 16,
num_key_value_heads: int = 16,
head_dim: int = 72,
float32_attention: bool = True,
attention_dropout: float = 0.0,
residual_dropout: float = 0.0,
hidden_act: str = "silu",
intermediate_size: int = 18944,
text_hidden_size: int = 3584,
image_feature_dropout: float = 0.0,
initializer_range: float = 0.02,
attn_implementation: str = "eager",
positional_embeddings: int = 16,
attention_pooling_out_layer: bool = False,
**kwargs,
):
self.attn_implementation = attn_implementation
super().__init__(
attn_implementation=attn_implementation,
**kwargs
)
self.vit_layers = vit_layers
self.pooling_attention_mask = pooling_attention_mask
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.head_dim = head_dim
self.float32_attention = float32_attention
self.attention_dropout = attention_dropout
self.residual_dropout = residual_dropout
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.text_hidden_size = text_hidden_size
self.image_feature_dropout = image_feature_dropout
self.initializer_range = initializer_range
self.positional_embeddings = positional_embeddings
self.attention_pooling_out_layer = attention_pooling_out_layer
class MolmoPointConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MolmoPointForConditionalGeneration`].
It is used to instantiate an Molmo2 model according to the specified arguments, defining the model architecture.
Example:
```python
>>> from transformers import Molmo2Config, Molmo2VitConfig, Molmo2AdapterConfig, Molmo2TextConfig
>>> # Initializing a Molmo2VitConfig
>>> vit_config = Molmo2VitConfig()
>>> # Initializing a Molmo2AdapterConfig
>>> adapter_config = MolmoPointAdapterConfig()
>>> # Initializing a Molmo2TextConfig
>>> text_config = Molmo2TextConfig()
>>> # Initializing a Molmo2Config
>>> configuration = MolmoPointConfig(
>>> vit_config=vit_config,
>>> adapter_config=adapter_config,
>>> text_config=text_config,
>>> image_start_token_id=151936,
>>> image_end_token_id=151937,
>>> image_patch_id=151938,
>>> image_col_id=151939,
>>> low_res_image_start_token_id=151940,
>>> image_low_res_id=151942,
>>> frame_start_token_id=151943,
>>> frame_end_token_id=151944,
>>> )
>>> # Initializing a model
>>> model = MolmoPointForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "molmo_point"
sub_configs = {
"text_config": Molmo2TextConfig,
"vit_config": Molmo2VitConfig,
"adapter_config": MolmoPointAdapterConfig,
}
def __init__(
self,
vit_config: Molmo2VitConfig = None,
adapter_config: MolmoPointAdapterConfig = None,
text_config: Molmo2TextConfig = None,
image_start_token_id: int = None,
low_res_image_start_token_id: int = None,
image_end_token_id: int = None,
image_patch_id: int = None,
image_non_indexable_patch_id: int = None,
image_col_id: int = None,
frame_start_token_id: int = None,
frame_end_token_id: int = None,
patch_token_id: int = None,
subpatch_token_id: int = None,
location_token_id: int = None,
use_frame_special_tokens: bool = True,
initializer_range: float = 0.02,
# point config
patch_location: Optional[str]="3x3",
no_more_points_class: bool=False,
patch_embed_dim: int=256,
patch_embedding_kind: str="linear",
embed_selected_vit_patch: Optional[str]="linear",
embed_location: bool=False,
layer_norm_x: bool=True,
norm_logits: bool=True,
# FIXME figure out how infernce params work
mask_patches: Optional[str]="always",
mask_subpatches: str="inference",
mask_repeats: Optional[str]="inference",
token_prediction_rotary: bool=True,
token_prediction_rotary_theta: Optional[float]=50000,
**kwargs,
):
super().__init__(**kwargs)
if vit_config is None:
self.vit_config = Molmo2VitConfig()
elif isinstance(vit_config, dict):
self.vit_config = Molmo2VitConfig(**vit_config)
else:
self.vit_config = vit_config
if adapter_config is None:
self.adapter_config = Molmo2AdapterConfig()
elif isinstance(adapter_config, dict):
self.adapter_config = Molmo2AdapterConfig(**adapter_config)
else:
self.adapter_config = adapter_config
if text_config is None:
self.text_config = Molmo2TextConfig()
elif isinstance(text_config, dict):
self.text_config = Molmo2TextConfig(**text_config)
else:
self.text_config = text_config
self.image_start_token_id = image_start_token_id
self.low_res_image_start_token_id = low_res_image_start_token_id
self.image_end_token_id = image_end_token_id
self.image_high_res_id = image_patch_id
self.image_non_indexable_patch_id = image_non_indexable_patch_id
self.image_patch_id = image_patch_id
self.image_col_id = image_col_id
self.frame_start_token_id = frame_start_token_id
self.frame_end_token_id = frame_end_token_id
self.patch_token_id = patch_token_id
self.subpatch_token_id = subpatch_token_id
self.location_token_id = location_token_id
self.use_frame_special_tokens = use_frame_special_tokens
self.initializer_range = initializer_range
self.patch_location = patch_location
self.no_more_points_class = no_more_points_class
self.patch_embed_dim = patch_embed_dim
self.patch_embedding_kind = patch_embedding_kind
self.embed_selected_vit_patch = embed_selected_vit_patch
self.embed_location = embed_location
self.layer_norm_x = layer_norm_x
self.norm_logits = norm_logits
self.mask_patches = mask_patches
self.mask_subpatches = mask_subpatches
self.mask_repeats = mask_repeats
self.token_prediction_rotary = token_prediction_rotary
self.token_prediction_rotary_theta = token_prediction_rotary_theta
@property
def image_num_patch(self):
assert self.vit_config is not None
return self.vit_config.image_num_patch
@property
def num_attention_heads(self):
return self.text_config.num_attention_heads
@property
def num_key_value_heads(self):
return self.text_config.num_key_value_heads
@property
def head_dim(self):
return self.text_config.head_dim
@property
def num_hidden_layers(self):
return self.text_config.num_hidden_layers
@property
def hidden_size(self):
return self.text_config.hidden_size
@property
def vocab_size(self):
return self.text_config.vocab_size
@property
def max_position_embeddings(self):
return self.text_config.max_position_embeddings
MolmoPointAdapterConfig.register_for_auto_class()
MolmoPointConfig.register_for_auto_class() |