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import sys
from pathlib import Path
parent_root = Path().resolve().parent.parent
sys.path.append(str(parent_root))
from transformers import PretrainedConfig, DonutSwinConfig, GemmaConfig, CONFIG_MAPPING, SiglipVisionConfig
from typing import Tuple, Literal
class PamConfig(PretrainedConfig):
model_type = "pam"
def __init__(
self,
sequence_mapping_layer_type: Literal["linear_projection","bilinear_interpolation"] = "bilinear_interpolation",
student_fmap_dim: Tuple[int,int]=(80,60),
student_embedding_dim: int = 1024,
teacher_fmap_dim: Tuple[int,int] = (64,64),
teacher_embedding_dim: int = 1152,
**kwargs,
):
self.sequence_mapping_layer_type = sequence_mapping_layer_type
self.student_fmap_dim = student_fmap_dim
self.student_embedding_dim = student_embedding_dim
self.teacher_fmap_dim = teacher_fmap_dim
self.teacher_embedding_dim = teacher_embedding_dim
super().__init__(**kwargs)
class SwinPamVisionEncoderConfig(PretrainedConfig):
model_type = "swinpam"
sub_configs = {"encoder_config": DonutSwinConfig, "pam_config": PamConfig}
def __init__(
self,
encoder_config: DonutSwinConfig = None,
pam_config: PamConfig = None,
**kwargs
):
self.encoder_config = encoder_config
self.pam_config = pam_config
if isinstance(self.encoder_config, dict):
encoder_config["model_type"] = (
encoder_config["model_type"] if "model_type" in encoder_config else "donut-swin"
)
if encoder_config["model_type"] == "donut-swin":
self.encoder_config = DonutSwinConfig(**encoder_config)
else:
print(f"Encoder type: {encoder_config['model_type']}")
self.encoder_config = CONFIG_MAPPING[encoder_config["model_type"]](**encoder_config)
'''
elif encoder_config is None:
print("coucou2")
self.encoder_config = DonutSwinConfig()
'''
if isinstance(self.pam_config, dict):
'''
pam_config["model_type"] = (
pam_config["model_type"] if "model_type" in pam_config else "pam"
)
'''
if pam_config["model_type"] == "pam":
self.pam_config = PamConfig(**pam_config)
else:
raise ValueError(f"pam_config['model_type'] should be 'pam', got {pam_config['model_type']}")
'''
elif pam_config is None:
self.pam_config = PamConfig()
'''
super().__init__(**kwargs)
class SiglipPAMVisionEncoderConfig(PretrainedConfig):
model_type = "siglippam"
sub_configs = {"encoder_config": SiglipVisionConfig, "pam_config": PamConfig}
def __init__(
self,
encoder_config: SiglipVisionConfig = None,
pam_config: PamConfig = None,
**kwargs
):
self.encoder_config = encoder_config
self.pam_config = pam_config
if isinstance(self.encoder_config, dict):
encoder_config["model_type"] = (
encoder_config["model_type"] if "model_type" in encoder_config else "siglip_vision_model"
)
if encoder_config["model_type"] == "siglip_vision_model":
self.encoder_config = SiglipVisionConfig(**encoder_config)
else:
raise ValueError(f"Need siglip_model_type, got {encoder_config['model_type']}")
if isinstance(self.pam_config, dict):
if pam_config["model_type"] == "pam":
self.pam_config = PamConfig(**pam_config)
else:
raise ValueError(f"pam_config['model_type'] should be 'pam', got {pam_config['model_type']}")
super().__init__(**kwargs)
class DIVEdocConfig(PretrainedConfig):
keys_to_ignore_at_inference = ["past_key_values"]
sub_configs = {"vision_config": SwinPamVisionEncoderConfig, "text_config": GemmaConfig}
model_type = "DIVEdoc"
def __init__(
self,
vision_config=None,
text_config=None,
ignore_index=-100,
image_token_index=256000,
vocab_size=257152,
projection_dim=2048,
hidden_size=2048,
#_attn_implementation_autoset = True,
**kwargs,
):
self._ignore_index = ignore_index
self.image_token_index = image_token_index
self._vocab_size = vocab_size
self.projection_dim = projection_dim
self.hidden_size = hidden_size
self.vision_config = vision_config
self.is_encoder_decoder = False
#self._attn_implementation_autoset = _attn_implementation_autoset
if isinstance(self.vision_config, dict):
vision_config["model_type"] = (
vision_config["model_type"] if "model_type" in vision_config else "swinpam"
)
if vision_config["model_type"] == "swinpam":
self.vision_config = SwinPamVisionEncoderConfig(encoder_config=vision_config["encoder_config"],pam_config=vision_config["pam_config"])
elif vision_config["model_type"] == "siglippam":
self.vision_config = SiglipPAMVisionEncoderConfig(encoder_config=vision_config["encoder_config"],pam_config=vision_config["pam_config"])
else:
self.vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
elif vision_config is None:
self.vision_config = get_vision_config("swinpam")
self.text_config = text_config
if isinstance(self.text_config, dict):
text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "gemma"
self.text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
elif text_config is None:
self.text_config = CONFIG_MAPPING["gemma"](
hidden_size=2048,
num_hidden_layers=18,
intermediate_size=16384,
num_attention_heads=8,
num_key_value_heads=1,
is_encoder_decoder=False,
vocab_size=vocab_size,
)
self.text_config.num_image_tokens = self.vision_config.pam_config.teacher_fmap_dim[0] *\
self.vision_config.pam_config.teacher_fmap_dim[1]
self.vision_config.projection_dim = projection_dim
super().__init__(**kwargs)
def to_dict(self):
output = super().to_dict()
output.pop("_ignore_index", None)
return output
def get_siglip_vision_config(image_size=[896,896],num_image_token = 4096,hidden_size = 768):
encoder_config = SiglipVisionConfig(
hidden_size = hidden_size,
image_size = image_size,
intermediate_size = 2860,
model_type = "siglip_vision_model",
num_attention_heads = 8,
num_hidden_layers = 12,
num_image_tokens = num_image_token,
patch_size = 14,
projection_dim = 2048,
projector_hidden_act = "gelu_fast",
torch_dtype = "float32",
vision_use_head = False
)
return encoder_config
def get_swin_vision_config(image_size=[2560,1920],hidden_size = 1024):
encoder_config = DonutSwinConfig(
attention_probs_dropout_prob= 0.0,
depths =[
2,
2,
14,
2
],
drop_path_rate= 0.1,
embed_dim =128,
hidden_act ="gelu",
hidden_dropout_prob = 0.0,
hidden_size = hidden_size,
image_size = image_size,
initializer_range = 0.02,
layer_norm_eps = 1e-05,
mlp_ratio = 4.0,
model_type = "donut-swin",
num_channels = 3,
num_heads =[
4,
8,
16,
32
],
num_layers =4,
patch_size = 4,
path_norm = True,
qkv_bias = True,
use_absolute_embeddings = False,
window_size = 10
)
return encoder_config
def get_vision_config( visual_encoder_type:Literal["swinpam","siglip80m"],
image_size=[2560,1920],
sequence_mapping_layer_type= "bilinear",
student_fmap_dim=(80,60),
student_embedding_dim= 1024,
teacher_fmap_dim= (64,64),
teacher_embedding_dim= 1152):
pam_config = PamConfig(
sequence_mapping_layer_type = sequence_mapping_layer_type,
student_fmap_dim = student_fmap_dim,
student_embedding_dim = student_embedding_dim,
teacher_fmap_dim = teacher_fmap_dim,
teacher_embedding_dim = teacher_embedding_dim)
if visual_encoder_type == "swinpam":
encoder_config = get_swin_vision_config(image_size=image_size,hidden_size = student_embedding_dim)
ve_config = SwinPamVisionEncoderConfig(encoder_config=encoder_config,pam_config=pam_config)
return ve_config
elif visual_encoder_type =="siglip80m":
encoder_config = get_siglip_vision_config(image_size=image_size,num_image_token = (image_size//14)**2, hidden_size = student_embedding_dim)
ve_config = SiglipPAMVisionEncoderConfig(encoder_config=encoder_config,pam_config=pam_config)
return ve_config
else:
raise ValueError(f"Unknown visual encoder type, need 'swinpam' or 'siglip80m, got {visual_encoder_type}.") |