3213a65d96
* Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * Update Readme.md * remove submodule * add mPLUG MiniGPT4 * Update Readme.md * Update Readme.md * Update Readme.md --------- Co-authored-by: Yuliang Liu <34134635+Yuliang-Liu@users.noreply.github.com>
299 lines
13 KiB
Python
299 lines
13 KiB
Python
# 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
|