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# coding=utf-8
#
# Code mainly copied from:
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/configuration_clip.py
# and adjusted for Jina CLIP
import os
from copy import deepcopy
from typing import Any, Dict, Optional, Union
from transformers import PretrainedConfig, logging
logger = logging.get_logger(__name__)
""" Jina CLIP model configuration """
class JinaCLIPTextConfig(PretrainedConfig):
model_type = 'jina_clip_text'
def __init__(
self,
embed_dim: int = 768,
hf_model_name_or_path: str = 'jinaai/jina-bert-flash-implementation',
hf_model_config_kwargs: Optional[Dict[str, Any]] = None,
pooler_type: Optional[str] = None,
proj_type: Optional[str] = None,
proj_bias: bool = False,
**kwargs,
):
super().__init__(**kwargs)
self.embed_dim = embed_dim
self.hf_model_name_or_path = hf_model_name_or_path
self.hf_model_config_kwargs = hf_model_config_kwargs or {}
self.pooler_type = pooler_type
self.proj_type = proj_type
self.proj_bias = proj_bias
@classmethod
def from_pretrained(
cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
) -> 'PretrainedConfig':
cls._set_token_in_kwargs(kwargs)
configdict, kwargs = cls.get_config_dict(
pretrained_model_name_or_path, **kwargs
)
# get the text config dict if we are loading from JinaCLIPConfig
if configdict.get('model_type') == 'jina_clip':
configdict = configdict['text_config']
if (
'model_type' in configdict
and hasattr(cls, 'model_type')
and configdict['model_type'] != cls.model_type
):
logger.warning(
f'You are using a model of type {configdict["model_type"]} to '
f'instantiate a model of type {cls.model_type}. This is not supported '
'for all configurations of models and can yield errors.'
)
return cls.from_dict(configdict, **kwargs)
class JinaCLIPVisionConfig(PretrainedConfig):
model_type = 'jina_clip_vision'
def __init__(
self,
embed_dim: int = 768,
width: int = 768,
image_size: int = 224,
patch_size: int = 16,
layers: int = 12,
head_width: int = 64,
mlp_ratio: float = 4.0,
ls_init_value: Optional[float] = None,
patch_dropout: float = 0.0,
qkv_bias: bool = True,
fused_layer_norm: bool = False,
x_attention: bool = False,
post_norm: bool = False,
rope_embeddings: bool = False,
pt_hw_seq_len: int = 16,
intp_freq: bool = False,
naive_swiglu: bool = False,
subln: bool = False,
drop_path_rate: float = 0.0,
proj_type: Optional[str] = None,
**kwargs,
):
super().__init__(**kwargs)
self.layers = layers
self.embed_dim = embed_dim
self.width = width
self.head_width = head_width
self.mlp_ratio = mlp_ratio
self.image_size = image_size
self.patch_size = patch_size
self.ls_init_value = ls_init_value
self.patch_dropout = patch_dropout
self.qkv_bias = qkv_bias
self.fused_layer_norm = fused_layer_norm
self.x_attention = x_attention
self.post_norm = post_norm
self.rope_embeddings = rope_embeddings
self.pt_hw_seq_len = pt_hw_seq_len
self.intp_freq = intp_freq
self.naive_swiglu = naive_swiglu
self.subln = subln
self.drop_path_rate = drop_path_rate
self.proj_type = proj_type
@classmethod
def from_pretrained(
cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
) -> 'PretrainedConfig':
cls._set_token_in_kwargs(kwargs)
configdict, kwargs = cls.get_config_dict(
pretrained_model_name_or_path, **kwargs
)
# get the vision config dict if we are loading from JinaCLIPConfig
if configdict.get('model_type') == 'jina_clip':
configdict = configdict['vision_config']
if (
'model_type' in configdict
and hasattr(cls, 'model_type')
and configdict['model_type'] != cls.model_type
):
logger.warning(
f'You are using a model of type {configdict["model_type"]} to '
f'instantiate a model of type {cls.model_type}. This is not supported '
'for all configurations of models and can yield errors.'
)
return cls.from_dict(configdict, **kwargs)
class JinaCLIPConfig(PretrainedConfig):
model_type = 'jina_clip'
is_composition = True
def __init__(
self,
text_config: Optional[Dict] = None,
vision_config: Optional[Dict] = None,
add_projections: bool = False,
projection_dim: int = 768,
logit_scale_init_value: float = 2.6592,
use_text_flash_attn: Optional[bool] = None,
use_vision_xformers: Optional[bool] = None,
**kwargs,
):
# If `_config_dict` exist, we use them for the backward compatibility.
# We pop out these 2 attributes before calling `super().__init__` to avoid
# them being saved (which causes a lot of confusion!).
text_config_dict: Optional[Dict] = kwargs.pop('text_config_dict', None)
vision_config_dict: Optional[Dict] = kwargs.pop('vision_config_dict', None)
self.use_text_flash_attn = use_text_flash_attn
self.use_vision_xformers = use_vision_xformers
super().__init__(**kwargs)
if text_config_dict is not None:
if text_config is None:
text_config = {}
# This is the complete result when using `text_config_dict`.
_text_config_dict = JinaCLIPTextConfig(**text_config_dict).to_dict()
# Give a warning if the values exist in both `_text_config_dict` and
# `text_config` but being different.
for key, value in _text_config_dict.items():
if (
key in text_config
and value != text_config[key]
and key not in ['transformers_version']
):
# If specified in `text_config_dict`
if key in text_config_dict:
message = (
f'`{key}` is found in both `text_config_dict` and '
f'`text_config` but with different values. '
f'The value `text_config_dict["{key}"]` will be used '
f'instead.'
)
# If inferred from default argument values (
# just to be super careful)
else:
message = (
f'`text_config_dict` is provided which will be used to '
f'initialize `JinaCLIPTextConfig`. The '
f'value `text_config["{key}"]` will be overriden.'
)
logger.info(message)
# Update all values in `text_config` with the ones in `_text_config_dict`.
text_config.update(_text_config_dict)
if vision_config_dict is not None:
if vision_config is None:
vision_config = {}
# This is the complete result when using `vision_config_dict`.
_vision_config_dict = JinaCLIPVisionConfig(**vision_config_dict).to_dict()
# convert keys to string instead of integer
if 'id2label' in _vision_config_dict:
_vision_config_dict['id2label'] = {
str(key): value
for key, value in _vision_config_dict['id2label'].items()
}
# Give a warning if the values exist in both `_vision_config_dict`
# and `vision_config` but being different.
for key, value in _vision_config_dict.items():
if (
key in vision_config
and value != vision_config[key]
and key not in ['transformers_version']
):
# If specified in `vision_config_dict`
if key in vision_config_dict:
message = (
f'`{key}` is found in both `vision_config_dict` and '
f'`vision_config` but with different '
f'values. The value `vision_config_dict["{key}"]` will '
f'be used instead.'
)
# If inferred from default argument values
# (just to be super careful)
else:
message = (
f'`vision_config_dict` is provided which will be used to '
f'initialize `JinaCLIPVisionConfig`. '
f'The value `vision_config["{key}"]` will be overriden.'
)
logger.info(message)
# Update all values in `vision_config` with the ones in
# `_vision_config_dict`.
vision_config.update(_vision_config_dict)
if text_config is None:
text_config = {}
logger.info(
'`text_config` is `None`. Initializing the `JinaCLIPTextConfig` with '
'default values.'
)
if vision_config is None:
vision_config = {}
logger.info(
'`vision_config` is `None`. initializing the `JinaCLIPVisionConfig` '
'with default values.'
)
self.text_config = JinaCLIPTextConfig(**text_config)
self.vision_config = JinaCLIPVisionConfig(**vision_config)
self.add_projections = add_projections
self.projection_dim = projection_dim
self.logit_scale_init_value = logit_scale_init_value
self.initializer_factor = 1.0
if not self.add_projections:
if self.text_config.embed_dim != self.vision_config.embed_dim:
raise ValueError(
'When projections are disabled (`add_projections=False`), text '
'and vision towers need to have the same embedding dimensionality. '
f'Currently text embedding dim is {self.text_config.embed_dim} != '
f'{self.vision_config.embed_dim} of the vision tower. '
'Either set the same output dim for both towers, or enable '
'projections with `add_projections=True`.'
)
@classmethod
def from_text_vision_configs(
cls,
text_config: JinaCLIPTextConfig,
vision_config: JinaCLIPVisionConfig,
**kwargs,
):
return cls(
text_config=text_config.to_dict(),
vision_config=vision_config.to_dict(),
projection_dim=text_config.projection_dim,
**kwargs,
)
def to_dict(self):
output = deepcopy(self.__dict__)
output['text_config'] = self.text_config.to_dict()
output['vision_config'] = self.vision_config.to_dict()
output['model_type'] = self.__class__.model_type
return output
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