configuration_chexagent.py

# coding=utf-8
# Copyright 2023 The CheXagent Authors 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.

import os
from typing import Union

from transformers.configuration_utils import PretrainedConfig
from transformers.models.auto import CONFIG_MAPPING
from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from transformers.utils import logging

logger = logging.get_logger(__name__)


class CheXagentVisionConfig(PretrainedConfig):
    model_type = "chexagent_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

    @classmethod
    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)

        if config_dict.get("model_type") == "chexagent":
            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 CheXagentQFormerConfig(PretrainedConfig):
    model_type = "chexagent_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

    @classmethod
    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)

        if config_dict.get("model_type") == "chexagent":
            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 CheXagentConfig(PretrainedConfig):
    model_type = "chexagent"

    def __init__(
            self, vision_config=None, qformer_config=None, text_config=None, num_query_tokens=128,
            num_max_images=2, **kwargs
    ):
        super().__init__(**kwargs)

        if vision_config is None:
            vision_config = {}

        if qformer_config is None:
            qformer_config = {}

        if text_config is None:
            text_config = {}

        self.vision_config = CheXagentVisionConfig(**vision_config)
        self.qformer_config = CheXagentQFormerConfig(**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
        self.num_max_images = num_max_images

    @classmethod
    def from_vision_qformer_text_configs(
            cls,
            vision_config: CheXagentVisionConfig,
            qformer_config: CheXagentQFormerConfig,
            text_config: PretrainedConfig,
            **kwargs,
    ):
        return cls(
            vision_config=vision_config.to_dict(),
            qformer_config=qformer_config.to_dict(),
            text_config=text_config.to_dict(),
            **kwargs,
        )