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import os |
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import json |
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import argparse |
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import torch |
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import random |
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import glog |
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from lm_eval import evaluator |
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from eval_utils import LMEvalAdaptor |
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from .tokenization_bitnet import BitnetTokenizer |
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from .modeling_bitnet import BitnetForCausalLM |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--seed', default=0, type=int) |
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parser.add_argument('--hf_path', default='1bitLLM/bitnet_b1_58-3B', type=str) |
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parser.add_argument('--batch_size', type=int, default=1, help='batch size') |
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parser.add_argument("--tasks", type=str) |
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parser.add_argument("--output_path", default=None, type=str) |
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parser.add_argument('--num_fewshot', type=int, default=0) |
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parser.add_argument('--ctx_size', default=2048, type=int) |
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def main(args): |
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model_str = args.hf_path |
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model = BitnetForCausalLM.from_pretrained( |
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args.hf_path, |
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device_map='auto', |
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low_cpu_mem_usage=True, |
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use_flash_attention_2=True, |
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torch_dtype=torch.float16, |
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).half() |
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tokenizer = BitnetTokenizer.from_pretrained(args.hf_path, use_fast=False) |
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glog.info('loaded model!') |
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task_names = args.tasks.split(",") |
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lm_eval_model = LMEvalAdaptor(model_str, model, tokenizer, args.batch_size, args.ctx_size) |
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results = evaluator.simple_evaluate( |
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model=lm_eval_model, |
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tasks=task_names, |
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batch_size=args.batch_size, |
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no_cache=True, |
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num_fewshot=args.num_fewshot, |
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) |
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print(evaluator.make_table(results)) |
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if args.output_path is not None: |
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os.makedirs(os.path.dirname(args.output_path), exist_ok=True) |
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results["config"]["model"] = args.hf_path |
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with open(args.output_path, "w") as f: |
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json.dump(results, f, indent=2) |
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if __name__ == '__main__': |
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torch.set_grad_enabled(False) |
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args = parser.parse_args() |
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random.seed(args.seed) |
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torch.random.manual_seed(args.seed) |
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main(args) |
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