japanese-clip/japanese_clip/cloob/configuration_cloob.py

# coding=utf-8
# Copyright 2022 rinna Co., Ltd.
#
# 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
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""" CLOOB model configuration"""
import logging
import copy
import os
from typing import Union

from transformers import AutoConfig, PretrainedConfig


logger = logging.getLogger(__name__)


class CLOOBTextConfig(PretrainedConfig):
    model_type = "cloob_text_model"

    def __init__(
        self,
        vocab_size=49408,
        hidden_size=512,
        intermediate_size=2048,
        num_hidden_layers=12,
        num_attention_heads=8,
        max_position_embeddings=77,
        hidden_act="quick_gelu",
        layer_norm_eps=0.00001,
        dropout=0.0,
        attention_dropout=0.0,
        initializer_range=0.02,
        initializer_factor=1.0,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        **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.dropout = dropout
        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.initializer_range = initializer_range
        self.initializer_factor = initializer_factor
        self.attention_dropout = attention_dropout

    @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 text config dict if we are loading from CLIPConfig
        if config_dict.get("model_type") == "clip":
            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 CLOOBVisionConfig(PretrainedConfig):
    model_type = "cloob_vision_model"

    def __init__(
        self,
        hidden_size=768,
        intermediate_size=3072,
        num_hidden_layers=12,
        num_attention_heads=12,
        image_size=224,
        patch_size=32,
        hidden_act="quick_gelu",
        layer_norm_eps=0.00001,
        dropout=0.0,
        attention_dropout=0.0,
        initializer_range=0.02,
        initializer_factor=1.0,
        **kwargs
    ):
        super().__init__(**kwargs)

        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.dropout = dropout
        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.initializer_factor = initializer_factor
        self.attention_dropout = attention_dropout
        self.layer_norm_eps = layer_norm_eps
        self.hidden_act = hidden_act

    @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 CLIPConfig
        if config_dict.get("model_type") == "clip":
            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 CLOOBConfig(PretrainedConfig):
    model_type = "cloob"
    is_composition = True

    def __init__(
        self,
        text_config=None,
        vision_config=None,
        projection_dim=512,
        init_inv_tau=30.0,
        scale_hopfield=15.0,
        **kwargs
    ):
        super().__init__(text_config=text_config, vision_config=vision_config, **kwargs)

        if vision_config is None:
            raise ValueError("`vision_config` can not be `None`.")

        if text_config is None:
            raise ValueError("`text_config` can not be `None`.")

        vision_model_type = vision_config.pop("model_type")
        text_model_type = text_config.pop("model_type")

        if vision_model_type == "cloob_vision_model":
            self.vision_config = CLOOBVisionConfig(**vision_config)
        else:
            self.vision_config = AutoConfig.for_model(
                vision_model_type, **vision_config
            )

        if text_model_type == "cloob_text_model":
            self.text_config = CLOOBTextConfig(**text_config)
        else:
            self.text_config = AutoConfig.for_model(
                text_model_type, **text_config
            )

        self.projection_dim = projection_dim
        self.initializer_factor = 1.0
        self.init_inv_tau = init_inv_tau
        self.scale_hopfield = scale_hopfield


    @classmethod
    def from_text_vision_configs(cls, text_config: CLOOBTextConfig, vision_config: CLOOBVisionConfig, **kwargs):
        r"""
        Instantiate a [`CLIPConfig`] (or a derived class) from clip text model configuration and clip vision model
        configuration.

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

        return cls(text_config_dict=text_config.to_dict(), vision_config_dict=vision_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["text_config"] = self.text_config.to_dict()
        output["vision_config"] = self.vision_config.to_dict()
        output["model_type"] = self.__class__.model_type
        return output