from dataclasses import dataclass, field from transformers import TrainingArguments from typing import List @dataclass class ModelArguments: model_name: str = field(metadata={"help": "huggingface model name or path"}) model_type: str = field(default=None, metadata={"help": "model type, typically includes in config file, but sometimes needs mannually add"}) processor_name: str = field(default=None, metadata={"help": "processor_name, huggingface model name or path"}) model_backbone: str = field(default=None, metadata={"help": "HF model type"}) checkpoint_path: str = field(default=None, metadata={"help": "a local model path, could be a LoRA version"}) pooling: str = field(default='last', metadata={"help": "pooling method for encoder"}) normalize: bool = field(default=False, metadata={"help": "normalize query and passage representations"}) temperature: float = field(default=0.02, metadata={"help": "temperature for softmax"}) lora: bool = field(default=False, metadata={"help": "do parameter-efficient fine-tuning with lora"}) lora_r: int = field(default=16, metadata={"help": "lora r"}) lora_alpha: int = field(default=64, metadata={"help": "lora alpha"}) lora_dropout: float = field(default=0.1, metadata={"help": "lora dropout"}) lora_target_modules: str = field(default="qkv_proj,o_proj,gate_up_proj,down_proj,k_proj,q_proj,out_proj,v_proj", metadata={"help": "lora target modules"}) num_crops: int = field(default=16, metadata={"help": "number of crops used in image encoder"}) uigraph_use: bool = field(default=False, metadata={"help": "Enable ui graph for token selection"}) uigraph_diff: int = field(default=1, metadata={"help": "Pixel difference used for constructing ui graph for token selection"}) uigraph_rand: bool = field(default=False, metadata={"help": "Enable random graph construction for token selection"}) uimask_ratio: float = field(default=0.5, metadata={"help": "Specify the percentage of patch tokens to skip per component for token selection"}) uimask_rand: bool = field(default=False, metadata={"help": "Enable random token selection instead of uniform selection"}) lm_skip_layer: str = field(default='[1,28,0]', metadata={"help": "Specify the layers of the language model to skip for token selection"}) vis_skip_layer: str = field(default='[1,32,0]', metadata={"help": "Specify the layers of the vision model to skip for token selection"}) # 视觉压缩方式: token_pooling / visionzip / none vision_compression: str = field( default="token_pooling", metadata={ "help": "视觉 token 压缩方式: 'token_pooling' (2x2 pooling 实现) | " "'visionzip' (VisionZip 实现) | 'none' (使用原始 Qwen2-VL，不做额外压缩)" }, ) @dataclass class DataArguments: dataset_config: str = field(default=None, metadata={"help": "yaml file with dataset configuration"}) data_basedir: str = field(default=None, metadata={"help": "Expect an absolute path to the base directory of all datasets. If set, it will be prepended to each dataset path"}) dataset_name: str = field(default=None, metadata={"help": "huggingface dataset name"}) subset_name: List[str] = field(default=None, metadata={"help": "Useful for datasets with subsets"}) dataset_split: str = field(default='train', metadata={"help": "dataset split"}) num_sample_per_subset: int = field(default=None, metadata={"help": "number of training samples per subset"}) image_dir: str = field(default=None, metadata={"help": "Image directory path"}) encode_output_path: str = field(default=None, metadata={"help": "encode output path"}) max_len: int = field(default=None, metadata={"help": "The maximum total input sequence length after tokenization. Use with caution, since it may truncate text prompts due to large image lengths."},) embedding_type: str = field(default="", metadata={"help": "embedding type"}) image_resolution: str = field(default=None, metadata={"help": "for models i.e. LLaVA-next and Qwen, resize images first, none means using original image resolution. This is only works when `--resize_use_processor false`."}) resize_use_processor: bool = field(default=True, metadata={"help": "Resize visual inputs insides processor, e.g. Qwen2VLImageProcessor, instead of by our code."}) resize_min_pixels: int = field(default=28*28*4, metadata={"help": "The min pixels of the image to resize the image. This is only works when `--resize_use_processor true`."}) resize_max_pixels: int = field(default=28*28*1280, metadata={"help": "The max pixels of the image to resize the image. This is only works when `--resize_use_processor true`."}) image_decay_factor: float = field(default=None, metadata={"help": "The image decay factor for resizing temporal images"}) num_hardneg: int = field(default=0, metadata={"help": "hard negative number"}) @dataclass class TrainingArguments(TrainingArguments): image_encoder_freeze: bool = field(default=False, metadata={"help": "huggingface model name"}) output_dir: str = field(default=None, metadata={"help": "directory for saving trained models"}) resume_from: str = field(default="none", metadata={"help": "`auto` will detect if any previous checkpoints should be resumed. or specify specific step of the checkpoint."}) project_name: str = field(default=None, metadata={"help": "project name"}) logging_steps: int = field(default=1, metadata={"help": "logging steps"}) num_train_epochs: int = field(default=1, metadata={"help": "number of training epochs"}) grad_cache: bool = field(default=False, metadata={"help": "Use gradient cache update"}) gc_q_chunk_size: int = field(default=2, metadata={"help": "query side subset size"}) gc_p_chunk_size: int = field(default=2, metadata={"help": "target side subset size"}) interleave_stopping_strategy: str = field(default="all_exhausted", metadata={"help": "all_exhausted or first_exhausted"}) interleave_batch_size: float = field(default=0, metadata={"help": "Specify mini-batch size to interleave data from multi-sources, 0/None means random sampling by examples, 1 means full batch."}) @dataclass class MTEBArguments: device: str = field(default="cuda", metadata={"help": "use cuda for single GPU inference, if multiple GPUs are available it will use DP automatically"}) batch_size_per_device: int = field(default=16, metadata={"help": ""}) max_length: int = field(default=512, metadata={"help": ""}) eval_output_dir: str = field(default=None, metadata={"help": "directory for saving trained models"}) task_types: List[str] = field(default=None, metadata={"help": ""}) tasks: List[str] = field(default=None, metadata={"help": ""}) prompt_family: List[str] = field(default=None, metadata={"help": ""})