| | 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."}, |
| | ) |
| |
|
