File size: 2,508 Bytes
2449566 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | 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]
seeds=[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]
seeds=[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'
}
dtype = torch.bfloat16 if extras.bf16 else torch.float16
with torch.cuda.amp.autocast(dtype=dtype):
results = evaluator.evaluate_multiple_benchmarks(
benchmark_configs=benchmark_configs,
batch_size=args.val_batch_size,
max_seq_length=256, max_new_tokens=512
)
with open(args.output_dir + "/eval.json", "w", encoding="utf-8") as f:
json.dump(results, f, ensure_ascii=False, indent=4)
if __name__ == "__main__":
main() |