from dataclasses import dataclass, field from typing import Optional from result_parser import yes_or_no, find_option_number, anomaly_detection, trajectory_prediction, trajectory_classification result_parsers = { "poi_category_recognition": find_option_number, "poi_identification": yes_or_no, "urban_region_function_recognition": find_option_number, "administrative_region_determination": find_option_number, "point_trajectory": find_option_number, "point_region": find_option_number, "trajectory_region": find_option_number, "trajectory_identification": yes_or_no, "trajectory_trajectory": find_option_number, "direction_determination": find_option_number, "trajectory_anomaly_detection": anomaly_detection, "trajectory_classification": trajectory_classification, "trajectory_prediction": trajectory_prediction } max_tokens = { "poi_category_recognition": 15, "poi_identification": 15, "urban_region_function_recognition": 15, "administrative_region_determination": 15, "point_trajectory": 15, "point_region": 15, "trajectory_region": 15, "trajectory_identification": 15, "trajectory_trajectory": 15, "direction_determination": 15, "trajectory_anomaly_detection": 15, "trajectory_classification": 15, "trajectory_prediction": 50 } dataset_files = { "poi_category_recognition": ["../datasets/basic/knowledge_comprehension/poi_category_recognition.jsonl"], "poi_identification": ["../datasets/basic/knowledge_comprehension/poi_identification.jsonl"], "urban_region_function_recognition": ["../datasets/basic/knowledge_comprehension/urban_region_function_recognition.jsonl"], "administrative_region_determination": ["../datasets/basic/knowledge_comprehension/administrative_region_determination.jsonl"], "point_trajectory": ["../datasets/basic/spatiotemporal_reasoning/point_trajectory.jsonl"], "point_region": ["../datasets/basic/spatiotemporal_reasoning/point_region_2regions.jsonl", "../datasets/basic/spatiotemporal_reasoning/point_region_3regions.jsonl", "../datasets/basic/spatiotemporal_reasoning/point_region_4regions.jsonl", "../datasets/basic/spatiotemporal_reasoning/point_region_5regions.jsonl"], "trajectory_region": ["../datasets/basic/spatiotemporal_reasoning/trajectory_region_length2.jsonl", "../datasets/basic/spatiotemporal_reasoning/trajectory_region_length4.jsonl", "../datasets/basic/spatiotemporal_reasoning/trajectory_region_length6.jsonl", "../datasets/basic/spatiotemporal_reasoning/trajectory_region_length8.jsonl", "../datasets/basic/spatiotemporal_reasoning/trajectory_region_length10.jsonl"], "trajectory_identification": ["../datasets/basic/spatiotemporal_reasoning/trajectory_identification_downsampling.jsonl", "../datasets/basic/spatiotemporal_reasoning/trajectory_identification_staggered_sampling.jsonl", "../datasets/basic/spatiotemporal_reasoning/trajectory_identification_spatial_offset.jsonl", "../datasets/basic/spatiotemporal_reasoning/trajectory_identification_temporal_offset.jsonl"], "trajectory_trajectory": ["../datasets/basic/accurate_calculation/trajectory_trajectory.jsonl"], "direction_determination": ["../datasets/basic/accurate_calculation/direction_determination.jsonl"], "trajectory_anomaly_detection": ["../datasets/basic/downstream_applications/trajectory_anomaly_detection_abnormal.jsonl", "../datasets/basic/downstream_applications/trajectory_anomaly_detection_normal.jsonl"], "trajectory_classification": ["../datasets/basic/downstream_applications/trajectory_classification.jsonl"], "trajectory_prediction": ["../datasets/basic/downstream_applications/trajectory_prediction.jsonl"] } icl_files = { "poi_identification": "../datasets/icl/poi_identification.jsonl", "trajectory_region": "../datasets/icl/trajectory_region.jsonl", "trajectory_trajectory": "../datasets/icl/trajectory_trajectory.jsonl", "direction_determination": "../datasets/icl/direction_determination.jsonl", "trajectory_anomaly_detection": "../datasets/icl/trajectory_anomaly_detection.jsonl", "trajectory_prediction": "../datasets/icl/trajectory_prediction.jsonl" } cot_files = { "urban_region_function_recognition": "../datasets/cot/urban_region_function_recognition.jsonl", "trajectory_region": "../datasets/cot/trajectory_region.jsonl", "trajectory_trajectory": "../datasets/cot/trajectory_trajectory.jsonl", "trajectory_classification": "../datasets/cot/trajectory_classification.jsonl" } sft_files = { "administrative_region_determination": { "train": "../datasets/sft/administrative_region_determination_train.jsonl", "valid": "../datasets/sft/administrative_region_determination_valid.jsonl" }, "direction_determination": { "train": "../datasets/sft/direction_determination_train.jsonl", "valid": "../datasets/sft/direction_determination_valid.jsonl" }, "trajectory_anomaly_detection":{ "train": "../datasets/sft/trajectory_anomaly_detection_train.jsonl", "valid": "../datasets/sft/trajectory_anomaly_detection_valid.jsonl" }, "trajectory_prediction": { "train": "../datasets/sft/trajectory_prediction_train.jsonl", "valid": "../datasets/sft/trajectory_prediction_valid.jsonl" }, "trajectory_region": { "train": "../datasets/sft/trajectory_region_train.jsonl", "valid": "../datasets/sft/trajectory_region_valid.jsonl" }, "trajectory_trajectory": { "train": "../datasets/sft/trajectory_trajectory_train.jsonl", "valid": "../datasets/sft/trajectory_trajectory_valid.jsonl" } } @dataclass class ScriptArguments: """ These arguments vary depending on how many GPUs you have, what their capacity and features are, and what size model you want to train. """ per_device_train_batch_size: Optional[int] = field(default=4) per_device_eval_batch_size: Optional[int] = field(default=1) gradient_accumulation_steps: Optional[int] = field(default=4) learning_rate: Optional[float] = field(default=2e-4) max_grad_norm: Optional[float] = field(default=0.3) weight_decay: Optional[int] = field(default=0.001) lora_alpha: Optional[int] = field(default=16) lora_dropout: Optional[float] = field(default=0.1) lora_r: Optional[int] = field(default=8) max_seq_length: Optional[int] = field(default=2048) model_name: Optional[str] = field( default=None, metadata={ "help": "The model that you want to train from the Hugging Face hub. E.g. gpt2, gpt2-xl, bert, etc." } ) dataset_name: Optional[str] = field( default="stingning/ultrachat", metadata={"help": "The preference dataset to use."}, ) fp16: Optional[bool] = field( default=False, metadata={"help": "Enables fp16 training."}, ) bf16: Optional[bool] = field( default=False, metadata={"help": "Enables bf16 training."}, ) packing: Optional[bool] = field( default=True, metadata={"help": "Use packing dataset creating."}, ) gradient_checkpointing: Optional[bool] = field( default=True, metadata={"help": "Enables gradient checkpointing."}, ) use_flash_attention_2: Optional[bool] = field( default=False, metadata={"help": "Enables Flash Attention 2."}, ) optim: Optional[str] = field( default="paged_adamw_32bit", metadata={"help": "The optimizer to use."}, ) lr_scheduler_type: str = field( default="constant", metadata={"help": "Learning rate schedule. Constant a bit better than cosine, and has advantage for analysis"}, ) max_steps: int = field(default=1000, metadata={"help": "How many optimizer update steps to take"}) warmup_ratio: float = field(default=0.03, metadata={"help": "Fraction of steps to do a warmup for"}) save_steps: int = field(default=100, metadata={"help": "Save checkpoint every X updates steps."}) logging_steps: int = field(default=10, metadata={"help": "Log every X updates steps."}) output_dir: str = field( default="./results", metadata={"help": "The output directory where the model predictions and checkpoints will be written."}, )