EVA-CLIP-8B-448 / configuration_evaclip.py

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	# coding=utf-8
	""" EvaCLIP model configuration"""
	# Code mainly copied here: https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/configuration_clip.py
	# and adjusted for evaclip

	import copy
	import os
	from collections import OrderedDict
	from typing import TYPE_CHECKING, Any, Mapping, Optional, Union


	if TYPE_CHECKING:
	from transformers.processing_utils import ProcessorMixin
	from transformers.utils import TensorType

	from transformers.configuration_utils import PretrainedConfig
	from transformers.utils import logging


	logger = logging.get_logger(__name__)


	class EvaCLIPTextConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`CLIPTextModel`]. It is used to instantiate a CLIP
	text encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
	with the defaults will yield a similar configuration to that of the text encoder of the CLIP
	[openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) architecture.

	Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
	documentation from [`PretrainedConfig`] for more information.

	Args:
	vocab_size (`int`, optional, defaults to 49408):
	Vocabulary size of the CLIP text model. Defines the number of different tokens that can be represented by
	the `inputs_ids` passed when calling [`CLIPModel`].
	hidden_size (`int`, optional, defaults to 512):
	Dimensionality of the encoder layers and the pooler layer.
	intermediate_size (`int`, optional, defaults to 2048):
	Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
	num_hidden_layers (`int`, optional, defaults to 12):
	Number of hidden layers in the Transformer encoder.
	num_attention_heads (`int`, optional, defaults to 8):
	Number of attention heads for each attention layer in the Transformer encoder.
	max_position_embeddings (`int`, optional, defaults to 77):
	The maximum sequence length that this model might ever be used with. Typically set this to something large
	just in case (e.g., 512 or 1024 or 2048).
	hidden_act (`str` or `function`, optional, defaults to `"quick_gelu"`):
	The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
	`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
	layer_norm_eps (`float`, optional, defaults to 1e-5):
	The epsilon used by the layer normalization layers.
	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 1):
	A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
	testing).

	Example:

	```python
	>>> from transformers import CLIPTextConfig, CLIPTextModel

	>>> # Initializing a CLIPTextConfig with openai/clip-vit-base-patch32 style configuration
	>>> configuration = CLIPTextConfig()

	>>> # Initializing a CLIPTextModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
	>>> model = CLIPTextModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```"""
	model_type = "clip_text_model"

	def __init__(
	self,
	vocab_size=49408,
	hidden_size=512,
	intermediate_size=2048,
	projection_dim=512,
	num_hidden_layers=12,
	num_attention_heads=8,
	max_position_embeddings=77,
	hidden_act="gelu",
	layer_norm_eps=1e-5,
	attention_dropout=0.0,
	initializer_range=0.02,
	initializer_factor=1.0,
	q_bias=True,
	k_bias=True,
	v_bias=True,
	post_layernorm=False,
	pad_token_id=1,
	bos_token_id=0,
	eos_token_id=2,
	use_rms_norm=False,
	**kwargs,
	):
	super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

	self.vocab_size = vocab_size
	self.hidden_size = hidden_size
	self.intermediate_size = intermediate_size
	self.projection_dim = projection_dim
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads
	self.max_position_embeddings = max_position_embeddings
	self.layer_norm_eps = layer_norm_eps
	self.hidden_act = hidden_act
	self.initializer_range = initializer_range
	self.initializer_factor = initializer_factor
	self.q_bias=q_bias
	self.k_bias=k_bias
	self.v_bias=v_bias
	self.post_layernorm = post_layernorm
	self.attention_dropout = attention_dropout
	self.use_rms_norm = use_rms_norm

	@classmethod
	def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
	config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

	# get the text config dict if we are loading from CLIPConfig
	if config_dict.get("model_type") == "clip":
	config_dict = config_dict["text_config"]

	if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
	logger.warning(
	f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
	f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
	)

	return cls.from_dict(config_dict, **kwargs)


	class EvaCLIPVisionConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`CLIPVisionModel`]. It is used to instantiate a
	CLIP vision encoder according to the specified arguments, defining the model architecture. Instantiating a
	configuration with the defaults will yield a similar configuration to that of the vision encoder of the CLIP
	[openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) architecture.

	Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
	documentation from [`PretrainedConfig`] for more information.

	Args:
	hidden_size (`int`, optional, defaults to 768):
	Dimensionality of the encoder layers and the pooler layer.
	intermediate_size (`int`, optional, defaults to 3072):
	Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
	num_hidden_layers (`int`, optional, defaults to 12):
	Number of hidden layers in the Transformer encoder.
	num_attention_heads (`int`, optional, defaults to 12):
	Number of attention heads for each attention layer in the Transformer encoder.
	image_size (`int`, optional, defaults to 224):
	The size (resolution) of each image.
	patch_size (`int`, optional, defaults to 32):
	The size (resolution) of each patch.
	hidden_act (`str` or `function`, optional, defaults to `"quick_gelu"`):
	The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
	`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
	layer_norm_eps (`float`, optional, defaults to 1e-5):
	The epsilon used by the layer normalization layers.
	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 1):
	A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
	testing).

	Example:

	```python
	>>> from transformers import CLIPVisionConfig, CLIPVisionModel

	>>> # Initializing a CLIPVisionConfig with openai/clip-vit-base-patch32 style configuration
	>>> configuration = CLIPVisionConfig()

	>>> # Initializing a CLIPVisionModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
	>>> model = CLIPVisionModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```"""

	model_type = "clip_vision_model"

	def __init__(
	self,
	hidden_size=768,
	intermediate_size=3072,
	projection_dim=512,
	num_hidden_layers=12,
	num_attention_heads=12,
	num_channels=3,
	image_size=224,
	patch_size=32,
	hidden_act="gelu",
	layer_norm_eps=1e-5,
	attention_dropout=0.0,
	initializer_range=0.02,
	initializer_factor=1.0,
	q_bias=True,
	k_bias=True,
	v_bias=True,
	post_layernorm=False,
	use_rms_norm=True,
	**kwargs,
	):
	super().__init__(**kwargs)

	self.hidden_size = hidden_size
	self.intermediate_size = intermediate_size
	self.projection_dim = projection_dim
	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.q_bias=q_bias
	self.k_bias=k_bias
	self.v_bias=v_bias
	self.post_layernorm = post_layernorm
	self.attention_dropout = attention_dropout
	self.layer_norm_eps = layer_norm_eps
	self.hidden_act = hidden_act
	self.use_rms_norm = use_rms_norm

	@classmethod
	def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
	config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

	# get the vision config dict if we are loading from CLIPConfig
	if config_dict.get("model_type") == "clip":
	config_dict = config_dict["vision_config"]

	if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
	logger.warning(
	f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
	f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
	)

	return cls.from_dict(config_dict, **kwargs)


	class EvaCLIPConfig(PretrainedConfig):
	r"""
	[`CLIPConfig`] is the configuration class to store the configuration of a [`CLIPModel`]. It is used to instantiate
	a CLIP model according to the specified arguments, defining the text model and vision model configs. Instantiating
	a configuration with the defaults will yield a similar configuration to that of the CLIP
	[openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) architecture.

	Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
	documentation from [`PretrainedConfig`] for more information.

	Args:
	text_config (`dict`, optional):
	Dictionary of configuration options used to initialize [`CLIPTextConfig`].
	vision_config (`dict`, optional):
	Dictionary of configuration options used to initialize [`CLIPVisionConfig`].
	projection_dim (`int`, optional, defaults to 512):
	Dimentionality of text and vision projection layers.
	logit_scale_init_value (`float`, optional, defaults to 2.6592):
	The inital value of the logit_scale paramter. Default is used as per the original CLIP implementation.
	kwargs (optional):
	Dictionary of keyword arguments.

	Example:

	```python
	>>> from transformers import CLIPConfig, CLIPModel

	>>> # Initializing a CLIPConfig with openai/clip-vit-base-patch32 style configuration
	>>> configuration = CLIPConfig()

	>>> # Initializing a CLIPModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
	>>> model = CLIPModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config

	>>> # We can also initialize a CLIPConfig from a CLIPTextConfig and a CLIPVisionConfig
	>>> from transformers import CLIPTextConfig, CLIPVisionConfig

	>>> # Initializing a CLIPText and CLIPVision configuration
	>>> config_text = CLIPTextConfig()
	>>> config_vision = CLIPVisionConfig()

	>>> config = CLIPConfig.from_text_vision_configs(config_text, config_vision)
	```"""

	model_type = "clip"
	is_composition = True

	def __init__(
	self, text_config=None, vision_config=None, projection_dim=512, logit_scale_init_value=2.6592, **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 = kwargs.pop("text_config_dict", None)
	vision_config_dict = kwargs.pop("vision_config_dict", None)

	super().__init__(**kwargs)

	# Instead of simply assigning `[text\|vision]_config_dict` to `[text\|vision]_config`, we use the values in
	# `[text\|vision]_config_dict` to update the values in `[text\|vision]_config`. The values should be same in most
	# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
	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 = EvaCLIPTextConfig(**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 `text_config` but with different values. "
	f'The value `text_config_dict["{key}"]` will be used 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 initialize `CLIPTextConfig`. The "
	f'value `text_config["{key}"]` will be overriden.'
	)
	logger.warning(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 = EvaCLIPVisionConfig(**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 `vision_config` but with different "
	f'values. The value `vision_config_dict["{key}"]` will 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 initialize `CLIPVisionConfig`. "
	f'The value `vision_config["{key}"]` will be overriden.'
	)
	logger.warning(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 `CLIPTextConfig` with default values.")

	if vision_config is None:
	vision_config = {}
	logger.info("`vision_config` is `None`. initializing the `CLIPVisionConfig` with default values.")

	self.text_config = EvaCLIPTextConfig(**text_config)
	self.vision_config = EvaCLIPVisionConfig(**vision_config)

	self.projection_dim = projection_dim
	self.logit_scale_init_value = logit_scale_init_value
	self.initializer_factor = 1.0

	@classmethod
	def from_text_vision_configs(cls, text_config: EvaCLIPTextConfig, vision_config: EvaCLIPVisionConfig, **kwargs):
	r"""
	Instantiate a [`CLIPConfig`] (or a derived class) from clip text model configuration and clip vision model
	configuration.

	Returns:
	[`CLIPConfig`]: An instance of a configuration object
	"""

	return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)

	def to_dict(self):
	"""
	Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].

	Returns:
	`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
	"""
	output = copy.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