configuration_llava.py

# Copyright 2023 Stability AI 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 import PretrainedConfig, CLIPVisionConfig
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 LlavaMlpConfig(PretrainedConfig):
    model_type = "llava_mlp"

    def __init__(
        self,
        num_hidden_layers=2,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.num_hidden_layers = num_hidden_layers

    @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
        )

        # get the qformer config dict if we are loading from InstructBlipConfig
        if config_dict.get("model_type") == "llava":
            config_dict = config_dict["mlp_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 LlavaConfig(PretrainedConfig):
    model_type = "llava"
    is_composition = True

    def __init__(
        self,
        vision_config=None,
        mlp_config=None,
        text_config=None,
        vision_select_layer=-2,
        vision_select_feature="patch",
        **kwargs,
    ):
        super().__init__(**kwargs)

        if vision_config is None:
            vision_config = {}
            logger.info(
                "vision_config is None. initializing the CLIPVisionConfig with default values."
            )

        if mlp_config is None:
            mlp_config = {}
            logger.info(
                "mlp_config is None. Initializing the LlavaMlpConfig 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 = CLIPVisionConfig(**vision_config)
        self.mlp_config = LlavaMlpConfig(**mlp_config)
        text_model_type = text_config["model_type"]
        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.use_decoder_only_language_model = (
            self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
        )
        self.vision_select_layer = vision_select_layer
        assert vision_select_feature in [
            "cls_patch",
            "patch",
        ], f"Unexpected select feature: {vision_select_feature}"
        self.vision_select_feature = vision_select_feature
        self.initializer_factor = 1.0
        self.initializer_range = 0.02

    @classmethod
    def from_vision_mlp_text_configs(
        cls,
        vision_config: CLIPVisionConfig,
        mlp_config: LlavaMlpConfig,
        text_config: PretrainedConfig,
        **kwargs,
    ):
        return cls(
            vision_config=vision_config.to_dict(),
            mlp_config=mlp_config.to_dict(),
            text_config=text_config.to_dict(),
            **kwargs,
        )