embodied_explainer / robohusky /configuration_husky.py
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# coding=utf-8
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""" Husky model configuration"""
import copy
import os
from typing import Union
from transformers.configuration_utils import PretrainedConfig
from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from transformers.utils import logging
from transformers.models.auto import CONFIG_MAPPING
logger = logging.get_logger(__name__)
HUSKY_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"wofmanaf/husky-7b": "https://huggingface.co/wofmanaf/husky-7b/resolve/main/config.json",
}
class HuskyVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`HuskyVisionModel`]. It is used to
instantiate a Husky vision encoder according to the specified arguments, defining the model architecture.
Instantiating a configuration defaults will yield a similar configuration to that of the Husky 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.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
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).
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries and values in the self-attention layers.
"""
model_type = "husky_vision_model"
def __init__(
self,
hidden_size=1408,
intermediate_size=6144,
projection_dim=512,
num_hidden_layers=39,
num_attention_heads=16,
num_channels=3,
image_size=224,
patch_size=14,
hidden_act="gelu",
layer_norm_eps=0.00001,
dropout=0.0,
attention_dropout=0.0,
initializer_range=1e-10,
initializer_factor=1.0,
qkv_bias=True,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.projection_dim = projection_dim
self.dropout = dropout
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
self.qkv_bias = qkv_bias
@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 HuskyConfig
if config_dict.get("model_type") == "husky":
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 HuskyQFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`HuskyQFormerModel`]. It is used to
instantiate a Husky 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 Husky [Salesforce/instruct-blip-flan-t5](https://huggingface.co/Salesforce/instruct-blip-flan-t5)
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 [`HuskyQFormerModel`] 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).
classifier_dropout (`float`, *optional*):
The dropout ratio for the classification head.
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.
"""
model_type = "husky_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",
classifier_dropout=None,
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.classifier_dropout = classifier_dropout
self.cross_attention_frequency = cross_attention_frequency
self.encoder_hidden_size = encoder_hidden_size
@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 qformer config dict if we are loading from HuskyConfig
if config_dict.get("model_type") == "husky":
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 HuskyConfig(PretrainedConfig):
r"""
[`HuskyConfig`] is the configuration class to store the configuration of a
[`HuskyForConditionalGeneration`]. It is used to instantiate a Husky 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 Husky
[Salesforce/instruct-blip-flan-t5](https://huggingface.co/Salesforce/instruct-blip-flan-t5) 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 [`HuskyVisionConfig`].
qformer_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`HuskyQFormerConfig`].
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.
"""
model_type = "husky"
is_composition = True
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 HuskyVisionConfig with default values.")
if qformer_config is None:
qformer_config = {}
logger.info("qformer_config is None. Initializing the HuskyQFormerConfig 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 = HuskyVisionConfig(**vision_config)
self.qformer_config = HuskyQFormerConfig(**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
@classmethod
def from_vision_qformer_text_configs(
cls,
vision_config: HuskyVisionConfig,
qformer_config: HuskyQFormerConfig,
text_config: PretrainedConfig,
**kwargs,
):
r"""
Instantiate a [`HuskyConfig`] (or a derived class) from a Husky vision model, Q-Former and
language model configurations.
Returns:
[`HuskyConfig`]: 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,
)
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["vision_config"] = self.vision_config.to_dict()
output["qformer_config"] = self.qformer_config.to_dict()
output["text_config"] = self.text_config.to_dict()
output["model_type"] = self.__class__.model_type
return output
if __name__ == '__main__':
config = HuskyConfig.from_pretrain