mirror of
https://github.com/deepseek-ai/DeepSeek-Coder
synced 2025-01-22 18:45:34 +00:00
43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
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import os
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import numpy as np
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import pandas as pd
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import torch
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import torch.nn.functional as F
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import json
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import torch.distributed as dist
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import subprocess
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import sys
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from accelerate import Accelerator
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from accelerate import DistributedDataParallelKwargs
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from pathlib import Path
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from argparse import ArgumentParser
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from humaneval import HumanEval as evaltor
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from transformers import AutoTokenizer, AutoModelForCausalLM
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if __name__ == '__main__':
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kwargs_handlers = [DistributedDataParallelKwargs(find_unused_parameters=True)]
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accelerator = Accelerator(mixed_precision="bf16", kwargs_handlers=kwargs_handlers)
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parser = ArgumentParser()
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parser.add_argument("--logdir", type=str, default="")
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parser.add_argument("--language", type=str, default="")
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parser.add_argument("--dataroot", type=str, default="")
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args = parser.parse_args()
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logdir = args.logdir
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language = args.language
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if logdir == "":
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logdir = "tmp/"
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tokenizer = dict(
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cls=AutoTokenizer,
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model_path=logdir,)
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dataroot = args.dataroot
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evaluator = evaltor(data_root=dataroot, max_seq_len=4096, tokenizer_cfg=tokenizer, log_dir=logdir, n_sample=1, batch_size=1, language=language, max_gen_len=500)
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model = AutoModelForCausalLM.from_pretrained(logdir, device_map=accelerator.device, trust_remote_code=True, torch_dtype=torch.bfloat16)
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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evaluator.eval_model(model, accelerator)
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