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# coding=utf-8 | |
# Copyright 2023 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. | |
""" BLIP-2 model configuration""" | |
import os | |
from typing import Union | |
from ...configuration_utils import PretrainedConfig | |
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES | |
from ...utils import logging | |
from ..auto import CONFIG_MAPPING | |
logger = logging.get_logger(__name__) | |
BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
"salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json", | |
} | |
class Blip2VisionConfig(PretrainedConfig): | |
r""" | |
This is the configuration class to store the configuration of a [`Blip2VisionModel`]. It is used to instantiate a | |
BLIP-2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a | |
configuration defaults will yield a similar configuration to that of the BLIP-2 | |
[Salesforce/blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b) 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 1408): | |
Dimensionality of the encoder layers and the pooler layer. | |
intermediate_size (`int`, *optional*, defaults to 6144): | |
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. | |
num_hidden_layers (`int`, *optional*, defaults to 39): | |
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 `"gelu"`): | |
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, | |
`"relu"`, `"selu"` and `"gelu_new"` ``"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. | |
qkv_bias (`bool`, *optional*, defaults to `True`): | |
Whether to add a bias to the queries and values in the self-attention layers. | |
Example: | |
```python | |
>>> from transformers import Blip2VisionConfig, Blip2VisionModel | |
>>> # Initializing a Blip2VisionConfig with Salesforce/blip2-opt-2.7b style configuration | |
>>> configuration = Blip2VisionConfig() | |
>>> # Initializing a Blip2VisionModel (with random weights) from the Salesforce/blip2-opt-2.7b style configuration | |
>>> model = Blip2VisionModel(configuration) | |
>>> # Accessing the model configuration | |
>>> configuration = model.config | |
```""" | |
model_type = "blip_2_vision_model" | |
def __init__( | |
self, | |
hidden_size=1408, | |
intermediate_size=6144, | |
num_hidden_layers=39, | |
num_attention_heads=16, | |
image_size=224, | |
patch_size=14, | |
hidden_act="gelu", | |
layer_norm_eps=1e-6, | |
attention_dropout=0.0, | |
initializer_range=1e-10, | |
qkv_bias=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.patch_size = patch_size | |
self.image_size = image_size | |
self.initializer_range = initializer_range | |
self.attention_dropout = attention_dropout | |
self.layer_norm_eps = layer_norm_eps | |
self.hidden_act = hidden_act | |
self.qkv_bias = qkv_bias | |
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 Blip2Config | |
if config_dict.get("model_type") == "blip-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 Blip2QFormerConfig(PretrainedConfig): | |
r""" | |
This is the configuration class to store the configuration of a [`Blip2QFormerModel`]. It is used to instantiate a | |
BLIP-2 Querying Transformer (Q-Former) 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 BLIP-2 | |
[Salesforce/blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b) architecture. Configuration objects | |
inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from | |
[`PretrainedConfig`] for more information. | |
Note that [`Blip2QFormerModel`] is very similar to [`BertLMHeadModel`] with interleaved cross-attention. | |
Args: | |
vocab_size (`int`, *optional*, defaults to 30522): | |
Vocabulary size of the Q-Former model. Defines the number of different tokens that can be represented by | |
the `inputs_ids` passed when calling the model. | |
hidden_size (`int`, *optional*, defaults to 768): | |
Dimensionality of the encoder layers and the pooler layer. | |
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. | |
intermediate_size (`int`, *optional*, defaults to 3072): | |
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. | |
hidden_act (`str` or `Callable`, *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. | |
hidden_dropout_prob (`float`, *optional*, defaults to 0.1): | |
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | |
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): | |
The dropout ratio for the attention probabilities. | |
max_position_embeddings (`int`, *optional*, defaults to 512): | |
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). | |
initializer_range (`float`, *optional*, defaults to 0.02): | |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
layer_norm_eps (`float`, *optional*, defaults to 1e-12): | |
The epsilon used by the layer normalization layers. | |
position_embedding_type (`str`, *optional*, defaults to `"absolute"`): | |
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For | |
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to | |
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). | |
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models | |
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). | |
cross_attention_frequency (`int`, *optional*, defaults to 2): | |
The frequency of adding cross-attention to the Transformer layers. | |
encoder_hidden_size (`int`, *optional*, defaults to 1408): | |
The hidden size of the hidden states for cross-attention. | |
Examples: | |
```python | |
>>> from transformers import Blip2QFormerConfig, Blip2QFormerModel | |
>>> # Initializing a BLIP-2 Salesforce/blip2-opt-2.7b style configuration | |
>>> configuration = Blip2QFormerConfig() | |
>>> # Initializing a model (with random weights) from the Salesforce/blip2-opt-2.7b style configuration | |
>>> model = Blip2QFormerModel(configuration) | |
>>> # Accessing the model configuration | |
>>> configuration = model.config | |
```""" | |
model_type = "blip_2_qformer" | |
def __init__( | |
self, | |
vocab_size=30522, | |
hidden_size=768, | |
num_hidden_layers=12, | |
num_attention_heads=12, | |
intermediate_size=3072, | |
hidden_act="gelu", | |
hidden_dropout_prob=0.1, | |
attention_probs_dropout_prob=0.1, | |
max_position_embeddings=512, | |
initializer_range=0.02, | |
layer_norm_eps=1e-12, | |
pad_token_id=0, | |
position_embedding_type="absolute", | |
cross_attention_frequency=2, | |
encoder_hidden_size=1408, | |
**kwargs, | |
): | |
super().__init__(pad_token_id=pad_token_id, **kwargs) | |
self.vocab_size = vocab_size | |
self.hidden_size = hidden_size | |
self.num_hidden_layers = num_hidden_layers | |
self.num_attention_heads = num_attention_heads | |
self.hidden_act = hidden_act | |
self.intermediate_size = intermediate_size | |
self.hidden_dropout_prob = hidden_dropout_prob | |
self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
self.max_position_embeddings = max_position_embeddings | |
self.initializer_range = initializer_range | |
self.layer_norm_eps = layer_norm_eps | |
self.position_embedding_type = position_embedding_type | |
self.cross_attention_frequency = cross_attention_frequency | |
self.encoder_hidden_size = encoder_hidden_size | |
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 qformer config dict if we are loading from Blip2Config | |
if config_dict.get("model_type") == "blip-2": | |
config_dict = config_dict["qformer_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 Blip2Config(PretrainedConfig): | |
r""" | |
[`Blip2Config`] is the configuration class to store the configuration of a [`Blip2ForConditionalGeneration`]. It is | |
used to instantiate a BLIP-2 model according to the specified arguments, defining the vision model, Q-Former model | |
and language model configs. Instantiating a configuration with the defaults will yield a similar configuration to | |
that of the BLIP-2 [Salesforce/blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b) architecture. | |
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
documentation from [`PretrainedConfig`] for more information. | |
Args: | |
vision_config (`dict`, *optional*): | |
Dictionary of configuration options used to initialize [`Blip2VisionConfig`]. | |
qformer_config (`dict`, *optional*): | |
Dictionary of configuration options used to initialize [`Blip2QFormerConfig`]. | |
text_config (`dict`, *optional*): | |
Dictionary of configuration options used to initialize any [`PretrainedConfig`]. | |
num_query_tokens (`int`, *optional*, defaults to 32): | |
The number of query tokens passed through the Transformer. | |
kwargs (*optional*): | |
Dictionary of keyword arguments. | |
Example: | |
```python | |
>>> from transformers import ( | |
... Blip2VisionConfig, | |
... Blip2QFormerConfig, | |
... OPTConfig, | |
... Blip2Config, | |
... Blip2ForConditionalGeneration, | |
... ) | |
>>> # Initializing a Blip2Config with Salesforce/blip2-opt-2.7b style configuration | |
>>> configuration = Blip2Config() | |
>>> # Initializing a Blip2ForConditionalGeneration (with random weights) from the Salesforce/blip2-opt-2.7b style configuration | |
>>> model = Blip2ForConditionalGeneration(configuration) | |
>>> # Accessing the model configuration | |
>>> configuration = model.config | |
>>> # We can also initialize a Blip2Config from a Blip2VisionConfig, Blip2QFormerConfig and any PretrainedConfig | |
>>> # Initializing BLIP-2 vision, BLIP-2 Q-Former and language model configurations | |
>>> vision_config = Blip2VisionConfig() | |
>>> qformer_config = Blip2QFormerConfig() | |
>>> text_config = OPTConfig() | |
>>> config = Blip2Config.from_text_vision_configs(vision_config, qformer_config, text_config) | |
```""" | |
model_type = "blip-2" | |
def __init__(self, vision_config=None, qformer_config=None, text_config=None, num_query_tokens=32, **kwargs): | |
super().__init__(**kwargs) | |
if vision_config is None: | |
vision_config = {} | |
logger.info("vision_config is None. initializing the Blip2VisionConfig with default values.") | |
if qformer_config is None: | |
qformer_config = {} | |
logger.info("qformer_config is None. Initializing the Blip2QFormerConfig with default values.") | |
if text_config is None: | |
text_config = {} | |
logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`).") | |
self.vision_config = Blip2VisionConfig(**vision_config) | |
self.qformer_config = Blip2QFormerConfig(**qformer_config) | |
text_model_type = text_config["model_type"] if "model_type" in text_config else "opt" | |
self.text_config = CONFIG_MAPPING[text_model_type](**text_config) | |
self.tie_word_embeddings = self.text_config.tie_word_embeddings | |
self.is_encoder_decoder = self.text_config.is_encoder_decoder | |
self.num_query_tokens = num_query_tokens | |
self.qformer_config.encoder_hidden_size = self.vision_config.hidden_size | |
self.use_decoder_only_language_model = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES | |
self.initializer_factor = 1.0 | |
self.initializer_range = 0.02 | |
def from_vision_qformer_text_configs( | |
cls, | |
vision_config: Blip2VisionConfig, | |
qformer_config: Blip2QFormerConfig, | |
text_config: PretrainedConfig, | |
**kwargs, | |
): | |
r""" | |
Instantiate a [`Blip2Config`] (or a derived class) from a BLIP-2 vision model, Q-Former and language model | |
configurations. | |
Returns: | |
[`Blip2Config`]: An instance of a configuration object | |
""" | |
return cls( | |
vision_config=vision_config.to_dict(), | |
qformer_config=qformer_config.to_dict(), | |
text_config=text_config.to_dict(), | |
**kwargs, | |
) | |