Spaces:
Running
Running
File size: 9,630 Bytes
52f1bcb |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 |
from transformers import PretrainedConfig
from surya.settings import settings
BOX_DIM = 1024
SPECIAL_TOKENS = 7
MAX_ROWS = 384
class SuryaTableRecConfig(PretrainedConfig):
model_type = "vision-encoder-decoder"
is_composition = True
def __init__(self, **kwargs):
super().__init__(**kwargs)
encoder_config = kwargs.pop("encoder")
decoder_config = kwargs.pop("decoder")
text_enc_config = kwargs.pop("text_encoder")
self.encoder = encoder_config
self.decoder = decoder_config
self.text_encoder = text_enc_config
self.is_encoder_decoder = True
if isinstance(decoder_config, dict):
self.decoder_start_token_id = decoder_config["bos_token_id"]
self.pad_token_id = decoder_config["pad_token_id"]
self.eos_token_id = decoder_config["eos_token_id"]
else:
self.decoder_start_token_id = decoder_config.bos_token_id
self.pad_token_id = decoder_config.pad_token_id
self.eos_token_id = decoder_config.eos_token_id
class DonutSwinTableRecConfig(PretrainedConfig):
model_type = "donut-swin"
attribute_map = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__(
self,
image_size=(settings.TABLE_REC_IMAGE_SIZE["width"], settings.TABLE_REC_IMAGE_SIZE["height"]),
patch_size=4,
num_channels=3,
embed_dim=128,
depths=[2, 2, 14, 2],
num_heads=[4, 8, 16, 32],
num_kv_heads=[4, 8, 16, 32],
window_size=8,
mlp_ratio=4.0,
qkv_bias=True,
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
drop_path_rate=0.1,
hidden_act="gelu",
use_absolute_embeddings=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
encoder_length=1024,
**kwargs,
):
super().__init__(**kwargs)
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.embed_dim = embed_dim
self.depths = depths
self.num_layers = len(depths)
self.num_heads = num_heads
self.num_kv_heads = num_kv_heads
self.window_size = window_size
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.use_absolute_embeddings = use_absolute_embeddings
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
self.encoder_length = encoder_length
class SuryaTableRecDecoderConfig(PretrainedConfig):
model_type = "surya_tablerec"
def __init__(
self,
num_hidden_layers=3,
vocab_size=settings.TABLE_REC_MAX_ROWS + SPECIAL_TOKENS,
hidden_size=512,
intermediate_size=4 * 512,
encoder_hidden_size=1024,
num_attention_heads=8,
lru_width=None,
attention_window_size=16,
conv1d_width=4,
logits_soft_cap=30.0,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
eos_token_id=1,
bos_token_id=2,
hidden_activation="gelu_pytorch_tanh",
rope_theta=10000.0,
block_types=("attention",),
cross_attn_layers=(0, 1, 2, 3),
encoder_cross_attn_layers=(0, 1, 2, 3),
self_attn_layers=(0, 1, 2, 3),
global_attn_layers=(0, 1, 2, 3),
attention_dropout=0.0,
num_key_value_heads=4,
attention_bias=False,
w_init_variance_scale=0.01,
init_std=0.02,
tie_word_embeddings=False,
aux_heads=0, # How many n-token-ahead heads to add
causal=True,
max_classes=2 + SPECIAL_TOKENS,
max_width=1024 + SPECIAL_TOKENS,
max_height=1024 + SPECIAL_TOKENS,
out_box_size=1024,
**kwargs,
):
self.num_hidden_layers = num_hidden_layers
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_attention_heads = num_attention_heads
self.lru_width = lru_width if lru_width is not None else hidden_size
self.attention_window_size = attention_window_size
self.conv1d_width = conv1d_width
self.logits_soft_cap = logits_soft_cap
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.block_types = list(block_types)
self.hidden_activation = hidden_activation
self.head_dim = self.hidden_size // self.num_attention_heads
self.num_key_value_heads = num_key_value_heads if num_key_value_heads is not None else num_attention_heads
if self.num_key_value_heads > self.num_attention_heads:
raise ValueError("The number of `num_key_value_heads` must be smaller than `num_attention_heads`")
self.cross_attn_layers = cross_attn_layers
self.self_attn_layers = self_attn_layers
self.global_attn_layers = global_attn_layers
self.attention_dropout = attention_dropout
self.attention_bias = attention_bias
self.w_init_variance_scale = w_init_variance_scale
self.final_w_init_variance_scale = 2.0 / self.num_hidden_layers
self.init_std = init_std
self.tie_word_embeddings = tie_word_embeddings
self.aux_heads = aux_heads
self.encoder_hidden_size=encoder_hidden_size
self.causal = causal
self.encoder_cross_attn_layers = encoder_cross_attn_layers
self.max_classes = max_classes
self.max_width = max_width
self.max_height = max_height
self.out_box_size = out_box_size
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
@property
def layers_block_type(self):
return (self.block_types * 100)[: self.num_hidden_layers]
class SuryaTableRecTextEncoderConfig(PretrainedConfig):
model_type = "surya_tablerec"
def __init__(
self,
num_hidden_layers=4,
vocab_size=settings.TABLE_REC_MAX_ROWS + SPECIAL_TOKENS,
hidden_size=1024,
intermediate_size=4 * 1024,
encoder_hidden_size=1024,
num_attention_heads=16,
lru_width=None,
attention_window_size=16,
conv1d_width=4,
logits_soft_cap=30.0,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
eos_token_id=1,
bos_token_id=2,
hidden_activation="gelu_pytorch_tanh",
rope_theta=10000.0,
block_types=("attention",),
cross_attn_layers=(0, 1, 2, 3, 4, 5),
self_attn_layers=(0, 1, 2, 3, 4, 5),
global_attn_layers=(0, 1, 2, 3, 4, 5),
attention_dropout=0.0,
num_key_value_heads=16,
attention_bias=False,
w_init_variance_scale=0.01,
init_std=0.02,
tie_word_embeddings=False,
causal=False,
max_width=BOX_DIM + SPECIAL_TOKENS,
max_height=BOX_DIM + SPECIAL_TOKENS,
max_position_embeddings=1024,
**kwargs,
):
self.num_hidden_layers = num_hidden_layers
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_attention_heads = num_attention_heads
self.lru_width = lru_width if lru_width is not None else hidden_size
self.attention_window_size = attention_window_size
self.conv1d_width = conv1d_width
self.logits_soft_cap = logits_soft_cap
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.block_types = list(block_types)
self.hidden_activation = hidden_activation
self.head_dim = self.hidden_size // self.num_attention_heads
self.num_key_value_heads = num_key_value_heads if num_key_value_heads is not None else num_attention_heads
if self.num_key_value_heads > self.num_attention_heads:
raise ValueError("The number of `num_key_value_heads` must be smaller than `num_attention_heads`")
self.cross_attn_layers = cross_attn_layers
self.self_attn_layers = self_attn_layers
self.global_attn_layers = global_attn_layers
self.attention_dropout = attention_dropout
self.attention_bias = attention_bias
self.w_init_variance_scale = w_init_variance_scale
self.final_w_init_variance_scale = 2.0 / self.num_hidden_layers
self.init_std = init_std
self.tie_word_embeddings = tie_word_embeddings
self.encoder_hidden_size = encoder_hidden_size
self.causal = causal
self.max_width = max_width
self.max_height = max_height
self.max_position_embeddings = max_position_embeddings
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
@property
def layers_block_type(self):
return (self.block_types * 100)[: self.num_hidden_layers] |