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# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
import copy
from transformers import LlamaConfig, Qwen2Config
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
from .configuration_intern_vit import InternVisionConfig
logger = logging.get_logger(__name__)
class InternVLChatConfig(PretrainedConfig):
model_type = "internvl_chat"
is_composition = True
def __init__(
self,
vision_config=None,
llm_config=None,
use_backbone_lora=0,
use_llm_lora=0,
select_layer=-1,
force_image_size=None,
downsample_ratio=0.5,
template=None,
dynamic_image_size=False,
use_thumbnail=False,
ps_version="v1",
min_dynamic_patch=1,
max_dynamic_patch=6,
**kwargs,
):
super().__init__(**kwargs)
if vision_config is None:
vision_config = {}
logger.info("vision_config is None. Initializing the InternVisionConfig with default values.")
if llm_config is None:
llm_config = {"architectures": ["Qwen2ForCausalLM"]}
logger.info("llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).")
self.vision_config = InternVisionConfig(**vision_config)
if llm_config["architectures"][0] == "LlamaForCausalLM":
self.llm_config = LlamaConfig(**llm_config)
elif llm_config["architectures"][0] == "Qwen2ForCausalLM":
self.llm_config = Qwen2Config(**llm_config)
else:
raise ValueError("Unsupported architecture: {}".format(llm_config["architectures"][0]))
self.use_backbone_lora = use_backbone_lora
self.use_llm_lora = use_llm_lora
self.select_layer = select_layer
self.force_image_size = force_image_size
self.downsample_ratio = downsample_ratio
self.template = template
self.dynamic_image_size = dynamic_image_size
self.use_thumbnail = use_thumbnail
self.ps_version = ps_version # pixel shuffle version
self.min_dynamic_patch = min_dynamic_patch
self.max_dynamic_patch = max_dynamic_patch
logger.info(f"vision_select_layer: {self.select_layer}")
logger.info(f"ps_version: {self.ps_version}")
logger.info(f"min_dynamic_patch: {self.min_dynamic_patch}")
logger.info(f"max_dynamic_patch: {self.max_dynamic_patch}")
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["llm_config"] = self.llm_config.to_dict()
output["model_type"] = self.__class__.model_type
output["use_backbone_lora"] = self.use_backbone_lora
output["use_llm_lora"] = self.use_llm_lora
output["select_layer"] = self.select_layer
output["force_image_size"] = self.force_image_size
output["downsample_ratio"] = self.downsample_ratio
output["template"] = self.template
output["dynamic_image_size"] = self.dynamic_image_size
output["use_thumbnail"] = self.use_thumbnail
output["ps_version"] = self.ps_version
output["min_dynamic_patch"] = self.min_dynamic_patch
output["max_dynamic_patch"] = self.max_dynamic_patch
return output
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