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---------

Co-authored-by: Yuliang Liu <34134635+Yuliang-Liu@users.noreply.github.com>
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2023-06-01 09:57:03 +08:00
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# coding=utf-8
# Copyright 2022 x-plug 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.
""" MplugOwl 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__)
MPLUG_OWL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"MAGAer13/mplug-owl-llama-7b": "https://huggingface.co/MAGAer13/mplug-owl-llama-7b/resolve/main/config.json",
# See all MplugOwl models at https://huggingface.co/models?filter=mplug_owl
}
class MplugOwlVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MplugOwlVisionModel`]. It is used to instantiate
a
mPLUG-Owl vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration defaults will yield a similar configuration to that of the mPLUG-Owl
[x-plug/x_plug-llama-7b](https://huggingface.co/x-plug/x_plug-llama-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 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).
```"""
model_type = "mplug_owl_vision_model"
def __init__(
self,
hidden_size=1024,
intermediate_size=4096,
projection_dim=768,
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-6,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=1.0,
use_flash_attn=False,
**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
self.use_flash_attn = use_flash_attn
@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 MplugOwlConfig
if config_dict.get("model_type") == "mplug-owl":
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 MplugOwlVisualAbstractorConfig(PretrainedConfig):
model_type = "mplug_owl_visual_abstract"
def __init__(
self,
hidden_size=1024, #
num_hidden_layers=6, #
num_attention_heads=16, #
intermediate_size=4096, #
attention_probs_dropout_prob=0.1, #
initializer_range=0.02,
layer_norm_eps=1e-6, #
encoder_hidden_size=1024, #
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
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 visual_abstractor config dict if we are loading from MplugOwlConfig
if config_dict.get("model_type") == "mplug-owl":
config_dict = config_dict["abstractor_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 MplugOwlConfig(PretrainedConfig):
r"""
[`MplugOwlConfig`] is the configuration class to store the configuration of a [`MplugOwlForConditionalGeneration`].
It is used to instantiate a mPLUG-Owl 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 mPLUG-Owl [x-plug/x_plug-llama-7b](https://huggingface.co/x-plug/x_plug-llama-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 [`MplugOwlVisionConfig`].
visual_abstractor_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`MplugOwlVisualAbstractorConfig`].
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 (
... MplugOwlVisionConfig,
... MplugOwlVisualAbstractorConfig,
... OPTConfig,
... MplugOwlConfig,
... MplugOwlForConditionalGeneration,
... )
>>> # Initializing a MplugOwlConfig with x-plug/x_plug-llama-7b style configuration
>>> configuration = MplugOwlConfig()
>>> # Initializing a MplugOwlForConditionalGeneration (with random weights) from the x-plug/x_plug-llama-7b style configuration
>>> model = MplugOwlForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> # We can also initialize a MplugOwlConfig from a MplugOwlVisionConfig, MplugOwlVisualAbstractorConfig and any PretrainedConfig
>>> # Initializing mPLUG-Owl vision, mPLUG-Owl Q-Former and language model configurations
>>> vision_config = MplugOwlVisionConfig()
>>> visual_abstractor_config = MplugOwlVisualAbstractorConfig()
>>> text_config = OPTConfig()
>>> config = MplugOwlConfig.from_text_vision_configs(vision_config, visual_abstractor_config, text_config)
```"""
model_type = "mplug-owl"
is_composition = True
def __init__(
self, vision_config=None, visual_abstractor_config=None, text_config=None, num_query_tokens=64, **kwargs
):
super().__init__(**kwargs)
if vision_config is None:
vision_config = MplugOwlVisionConfig().to_dict()
logger.info("vision_config is None.")
if visual_abstractor_config is None:
visual_abstractor_config = {}
logger.info("abstractor_config is None. ")
if text_config is None:
# we use LLAMA 7b by default
from transformers.llama.configuration_llama import LlamaConfig
text_config = LlamaConfig(pad_token_id=2).to_dict()
logger.info("text_config is None.")
self.vision_config = MplugOwlVisionConfig(**vision_config)
self.visual_abstractor_config = MplugOwlVisualAbstractorConfig(**visual_abstractor_config)
# self.visual_abstractor_config.layer_norm_eps = 1e-6
text_model_type = text_config["model_type"] if "model_type" in text_config else "llama"
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.visual_abstractor_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
for attr in dir(self.text_config):
if not hasattr(self, attr):
setattr(self, attr, getattr(self.text_config, attr))
@classmethod
def from_vision_visual_abstractor_text_configs(
cls,
vision_config: MplugOwlVisionConfig,
visual_abstractor_config: MplugOwlVisualAbstractorConfig,
text_config: PretrainedConfig,
**kwargs,
):
r"""
Instantiate a [`MplugOwlConfig`] (or a derived class) from a mPLUG-Owl vision model, Q-Former and language
model configurations.
Returns:
[`MplugOwlConfig`]: An instance of a configuration object
"""
return cls(
vision_config=vision_config.to_dict(),
visual_abstractor_config=visual_abstractor_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["visual_abstractor_config"] = self.visual_abstractor_config.to_dict()
output["text_config"] = self.text_config.to_dict()
output["model_type"] = self.__class__.model_type
return output