| import argparse
|
| from arguments import Arguments
|
| from evaluator import Evaluator
|
| from transformers import AutoModelForCausalLM, HfArgumentParser
|
|
|
| import torch
|
| import json
|
| import numpy as np
|
| import random
|
|
|
| def set_seed(seed):
|
| random.seed(seed)
|
| np.random.seed(seed)
|
| torch.manual_seed(seed)
|
|
|
| def main():
|
| hf_parser = HfArgumentParser(Arguments)
|
| args, remaining = hf_parser.parse_args_into_dataclasses(return_remaining_strings=True)
|
|
|
| extra_parser = argparse.ArgumentParser(add_help=False)
|
| extra_parser.add_argument("--seed", type=int, default=42, help="Random seed")
|
| extra_parser.add_argument("--model_path", type=str, default=None)
|
| extra_parser.add_argument("--lora_path", type=str, default=None)
|
| extra_parser.add_argument("--tokenizer", type=str, default=None)
|
| extra_parser.add_argument("--bf16", action="store_true")
|
|
|
| extras = extra_parser.parse_args(remaining)
|
|
|
| set_seed(extras.seed)
|
|
|
| if extras.lora_path is not None:
|
| evaluator = Evaluator(
|
| tokenizer_path=extras.tokenizer,
|
| model_path=extras.model_path,
|
| distilled_lora=extras.lora_path,
|
| device=args.student_device,
|
| seeds=[10, 20, 30, 40, 50]
|
| )
|
| else:
|
| evaluator = Evaluator(
|
| tokenizer_path=extras.tokenizer,
|
| model_path=extras.model_path,
|
| device=args.student_device,
|
| seeds=[10, 20, 30, 40, 50]
|
| )
|
|
|
| evaluator.model.config.output_hidden_states=False
|
| evaluator.model.config.output_attentions=False
|
|
|
| benchmark_configs = {'sni': './data/sinst/11_/valid.jsonl',
|
| 'dolly': './data/dolly/valid.jsonl',
|
| 'self_instruct': './data/self-inst/valid.jsonl',
|
| 'vicuna': './data/vicuna/valid.jsonl'
|
| }
|
|
|
|
|
|
|
|
|
| for key in benchmark_configs:
|
| evaluator.generate_and_save_outputs(
|
| dataset_path=benchmark_configs[key],
|
| output_file=args.output_dir + f"/generated_{key}.jsonl",
|
| batch_size=args.val_batch_size,
|
| max_seq_length=256, max_new_tokens=512,
|
| temperature=0.9, top_p=1.0
|
| )
|
|
|
| if __name__ == "__main__":
|
| main() |
