kosmos-2-patch14-224 / configuration_kosmos2.py

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	# coding=utf-8
	# Copyright 2023 Microsoft Research and The HuggingFace Inc. team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	""" KOSMOS-2 model configuration"""

	import copy
	import os
	from typing import Union

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


	logger = logging.get_logger(__name__)

	BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
	"microsoft/kosmos-2-patch14-224": (
	"https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/config.json"
	),
	# See all KOSMOS-2 models at https://huggingface.co/models?filter=kosmos-2
	}


	class Kosmos2TextConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`Kosmos2TextModel`]. It is used to instantiate a KOSMOS-2 text decoder
	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 decoder of the KOSMOS-2
	[microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) 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 65037):
	Vocabulary size of the Kosmos2 model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`Kosmos2Model`].
	embed_dim (`int`, optional, defaults to 2048):
	Dimensionality of the layers and the pooler layer.
	layers (`int`, optional, defaults to 24):
	Number of hidden layers in the Transformer encoder.
	attention_heads (`int`, optional, defaults to 32):
	Number of attention heads for each attention layer in the Transformer encoder.
	ffn_dim (`int`, optional, defaults to 8192):
	Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
	activation_function (`str` or `function`, optional, defaults to `"gelu"`):
	The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
	`"relu"`, `"silu"` and `"gelu_new"` are supported.
	dropout (`float`, optional, defaults to 0.1):
	The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
	attention_dropout (`float`, optional, defaults to 0.1):
	The dropout ratio for the attention probabilities.
	activation_dropout (`float`, optional, defaults to 0.0):
	The dropout ratio for activations inside the fully connected layer.
	max_position_embeddings (`int`, optional, defaults to 2048):
	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).
	layerdrop (`float`, optional, defaults to 0.0):
	The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
	for more details.
	layer_norm_eps (`float`, optional, defaults to 1e-5):
	The epsilon used by the layer normalization layers.
	scale_embedding (`bool`, optional, defaults to `True`):
	Scale embeddings by diving by sqrt(embed_dim).
	use_cache (`bool`, optional, defaults to `True`):
	Whether or not the model should return the last key/values attentions (not used by all models).

	Example:

	```python
	>>> from transformers import Kosmos2TextConfig, Kosmos2TextModel

	>>> # Initializing a Kosmos2TextConfig microsoft/kosmos-2-patch14-224 style configuration
	>>> configuration = Kosmos2TextConfig()

	>>> # Initializing a Kosmos2TextModel (with random weights) from the microsoft/kosmos-2-patch14-224 style configuration
	>>> model = Kosmos2TextModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```"""
	model_type = "kosmos_2_text_model"
	keys_to_ignore_at_inference = ["past_key_values"]
	attribute_map = {"num_attention_heads": "attention_heads", "hidden_size": "embed_dim"}

	def __init__(
	self,
	vocab_size=65037,
	max_position_embeddings=2048,
	embed_dim=2048,
	layers=24,
	ffn_dim=8192,
	attention_heads=32,
	activation_function="gelu",
	dropout=0.1,
	attention_dropout=0.1,
	activation_dropout=0.0,
	layerdrop=0.0,
	layer_norm_eps=1e-5,
	scale_embedding=True,
	use_cache=True,
	pad_token_id=1,
	bos_token_id=0,
	eos_token_id=2,
	**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.max_position_embeddings = max_position_embeddings
	self.embed_dim = embed_dim
	self.layers = layers
	self.ffn_dim = ffn_dim
	self.attention_heads = attention_heads
	self.activation_function = activation_function
	self.dropout = dropout
	self.attention_dropout = attention_dropout
	self.activation_dropout = activation_dropout
	self.layerdrop = layerdrop
	self.layer_norm_eps = layer_norm_eps
	self.scale_embedding = scale_embedding
	self.use_cache = use_cache

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

	config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

	# get the text config dict if we are loading from Kosmos2Config
	if config_dict.get("model_type") == "kosmos-2":
	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 Kosmos2VisionConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`Kosmos2VisionModel`]. It is used to instantiate a
	KOSMOS-2 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 KOSMOS-2
	[microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) 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 1024):
	Dimensionality of the encoder layers and the pooler layer.
	intermediate_size (`int`, optional, defaults to 4096):
	Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
	num_hidden_layers (`int`, optional, defaults to 24):
	Number of hidden layers in the Transformer encoder.
	num_attention_heads (`int`, optional, defaults to 16):
	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 14):
	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 Kosmos2VisionConfig, Kosmos2VisionModel

	>>> # Initializing a Kosmos2VisionConfig with microsoft/kosmos-2-patch14-224 style configuration
	>>> configuration = Kosmos2VisionConfig()

	>>> # Initializing a Kosmos2VisionModel (with random weights) from the microsoft/kosmos-2-patch14-224 style configuration
	>>> model = Kosmos2VisionModel(configuration)

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

	model_type = "kosmos_2_vision_model"

	def __init__(
	self,
	hidden_size=1024,
	intermediate_size=4096,
	projection_dim=512,
	num_hidden_layers=24,
	num_attention_heads=16,
	num_channels=3,
	image_size=224,
	patch_size=14,
	hidden_act="quick_gelu",
	layer_norm_eps=1e-5,
	attention_dropout=0.0,
	initializer_range=0.02,
	initializer_factor=1.0,
	**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.attention_dropout = attention_dropout
	self.layer_norm_eps = layer_norm_eps
	self.hidden_act = hidden_act

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

	config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

	# get the vision config dict if we are loading from Kosmos2Config
	if config_dict.get("model_type") == "kosmos-2":
	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 Kosmos2Config(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`Kosmos2Model`]. It is used to instantiate a KOSMOS-2
	model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
	defaults will yield a similar configuration to that of the KOSMOS-2
	[microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) architecture.

	Args:
	text_config (`dict`, optional):
	Dictionary of configuration options used to initialize [`Kosmos2TextConfig`].
	vision_config (`dict`, optional):
	Dictionary of configuration options used to initialize [`Kosmos2VisionConfig`].
	latent_query_num (`int`, optional, defaults to 64):
	The number of latent query tokens that represent the image features used in the text decoder component.
	kwargs (optional):
	Dictionary of keyword arguments.

	Example:

	```python
	>>> from transformers import Kosmos2Config, Kosmos2Model

	>>> # Initializing a Kosmos-2 kosmos-2-patch14-224 style configuration
	>>> configuration = Kosmos2Config()

	>>> # Initializing a model (with random weights) from the kosmos-2-patch14-224 style configuration
	>>> model = Kosmos2Model(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```"""
	model_type = "kosmos-2"
	is_composition = True

	def __init__(
	self,
	text_config=None,
	vision_config=None,
	latent_query_num=64,
	**kwargs,
	):
	super().__init__(**kwargs)

	if text_config is None:
	text_config = {}
	logger.info("`text_config` is `None`. Initializing the `Kosmos2TextConfig` with default values.")

	if vision_config is None:
	vision_config = {}
	logger.info("`vision_config` is `None`. Initializing the `Kosmos2VisionConfig` with default values.")

	self.text_config = Kosmos2TextConfig(**text_config)
	self.vision_config = Kosmos2VisionConfig(**vision_config)

	self.latent_query_num = latent_query_num

	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