siglip-so400m-14-384-flash-attn2 / configuration_siglip.py

Leyo HF staff

transformers-like-implementation (#1)

83cef5d verified 4 months ago

No virus

13.9 kB

	# coding=utf-8
	# Copyright 2024 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.
	""" Siglip model configuration"""

	import os
	from typing import Union

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


	logger = logging.get_logger(__name__)

	SIGLIP_PRETRAINED_CONFIG_ARCHIVE_MAP = {
	"google/siglip-base-patch16-224": "https://huggingface.co/google/siglip-base-patch16-224/resolve/main/config.json",
	}


	class SiglipTextConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`SiglipTextModel`]. It is used to instantiate a
	Siglip 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 Siglip
	[google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) 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 32000):
	Vocabulary size of the Siglip text model. Defines the number of different tokens that can be represented by
	the `inputs_ids` passed when calling [`SiglipModel`].
	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.
	max_position_embeddings (`int`, optional, defaults to 64):
	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 `"gelu_pytorch_tanh"`):
	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-06):
	The epsilon used by the layer normalization layers.
	attention_dropout (`float`, optional, defaults to 0.0):
	The dropout ratio for the attention probabilities.
	pad_token_id (`int`, optional, defaults to 1):
	The id of the padding token in the vocabulary.
	bos_token_id (`int`, optional, defaults to 49406):
	The id of the beginning-of-sequence token in the vocabulary.
	eos_token_id (`int`, optional, defaults to 49407):
	The id of the end-of-sequence token in the vocabulary.

	Example:

	```python
	>>> from transformers import SiglipTextConfig, SiglipTextModel

	>>> # Initializing a SiglipTextConfig with google/siglip-base-patch16-224 style configuration
	>>> configuration = SiglipTextConfig()

	>>> # Initializing a SiglipTextModel (with random weights) from the google/siglip-base-patch16-224 style configuration
	>>> model = SiglipTextModel(configuration)

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

	model_type = "siglip_text_model"

	def __init__(
	self,
	vocab_size=32000,
	hidden_size=768,
	intermediate_size=3072,
	num_hidden_layers=12,
	num_attention_heads=12,
	max_position_embeddings=64,
	hidden_act="gelu_pytorch_tanh",
	layer_norm_eps=1e-6,
	attention_dropout=0.0,
	# This differs from `CLIPTokenizer`'s default and from openai/siglip
	# See https://github.com/huggingface/transformers/pull/24773#issuecomment-1632287538
	pad_token_id=1,
	bos_token_id=49406,
	eos_token_id=49407,
	_flash_attn_2_enabled=True,
	**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.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.attention_dropout = attention_dropout
	self._flash_attn_2_enabled = _flash_attn_2_enabled

	@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 SiglipConfig
	if config_dict.get("model_type") == "siglip":
	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 SiglipVisionConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a
	Siglip 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 Siglip
	[google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) 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 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.
	num_channels (`int`, optional, defaults to 3):
	Number of channels in the input images.
	image_size (`int`, optional, defaults to 224):
	The size (resolution) of each image.
	patch_size (`int`, optional, defaults to 16):
	The size (resolution) of each patch.
	hidden_act (`str` or `function`, optional, defaults to `"gelu_pytorch_tanh"`):
	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-06):
	The epsilon used by the layer normalization layers.
	attention_dropout (`float`, optional, defaults to 0.0):
	The dropout ratio for the attention probabilities.

	Example:

	```python
	>>> from transformers import SiglipVisionConfig, SiglipVisionModel

	>>> # Initializing a SiglipVisionConfig with google/siglip-base-patch16-224 style configuration
	>>> configuration = SiglipVisionConfig()

	>>> # Initializing a SiglipVisionModel (with random weights) from the google/siglip-base-patch16-224 style configuration
	>>> model = SiglipVisionModel(configuration)

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

	model_type = "siglip_vision_model"

	def __init__(
	self,
	hidden_size=768,
	intermediate_size=3072,
	num_hidden_layers=12,
	num_attention_heads=12,
	num_channels=3,
	image_size=224,
	patch_size=16,
	hidden_act="gelu_pytorch_tanh",
	layer_norm_eps=1e-6,
	attention_dropout=0.0,
	_flash_attn_2_enabled=True,
	**kwargs,
	):
	super().__init__(**kwargs)

	self.hidden_size = hidden_size
	self.intermediate_size = intermediate_size
	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.attention_dropout = attention_dropout
	self.layer_norm_eps = layer_norm_eps
	self.hidden_act = hidden_act
	self._flash_attn_2_enabled = _flash_attn_2_enabled

	@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 SiglipConfig
	if config_dict.get("model_type") == "siglip":
	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 SiglipConfig(PretrainedConfig):
	r"""
	[`SiglipConfig`] is the configuration class to store the configuration of a [`SiglipModel`]. It is used to
	instantiate a Siglip 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 Siglip
	[google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture.

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

	Args:
	text_config (`dict`, optional):
	Dictionary of configuration options used to initialize [`SiglipTextConfig`].
	vision_config (`dict`, optional):
	Dictionary of configuration options used to initialize [`SiglipVisionConfig`].
	kwargs (optional):
	Dictionary of keyword arguments.

	Example:

	```python
	>>> from transformers import SiglipConfig, SiglipModel

	>>> # Initializing a SiglipConfig with google/siglip-base-patch16-224 style configuration
	>>> configuration = SiglipConfig()

	>>> # Initializing a SiglipModel (with random weights) from the google/siglip-base-patch16-224 style configuration
	>>> model = SiglipModel(configuration)

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

	>>> # We can also initialize a SiglipConfig from a SiglipTextConfig and a SiglipVisionConfig
	>>> from transformers import SiglipTextConfig, SiglipVisionConfig

	>>> # Initializing a SiglipText and SiglipVision configuration
	>>> config_text = SiglipTextConfig()
	>>> config_vision = SiglipVisionConfig()

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

	model_type = "siglip"

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

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

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

	self.text_config = SiglipTextConfig(**text_config)
	self.vision_config = SiglipVisionConfig(**vision_config)

	self.initializer_factor = 1.0

	@classmethod
	def from_text_vision_configs(cls, text_config: SiglipTextConfig, vision_config: SiglipVisionConfig, **kwargs):
	r"""
	Instantiate a [`SiglipConfig`] (or a derived class) from siglip text model configuration and siglip vision
	model configuration.

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

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