mirror of
https://github.com/NicolasBohn/NexQuant.git
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Xinjie Shen
b2fb104108
* add needed dependency * add extract_model_and_implement pipeline * add merge_file_to_model_dict_to_model_dict * implement `rdagent\app\model_implementation\eval.py` * Running benchmark * refine import --------- Co-authored-by: Young <afe.young@gmail.com>
35 lines
1.1 KiB
Python
35 lines
1.1 KiB
Python
from pathlib import Path
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DIRNAME = Path(__file__).absolute().resolve().parent
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from rdagent.components.task_implementation.model_implementation.benchmark.eval import ModelImpValEval
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from rdagent.components.task_implementation.model_implementation.one_shot import ModelTaskGen
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from rdagent.components.task_implementation.model_implementation.task import ModelImpLoader, ModelTaskLoderJson
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bench_folder = DIRNAME.parent.parent / "components" / "task_implementation" / "model_implementation" / "benchmark"
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mtl = ModelTaskLoderJson(str(bench_folder / "model_dict.json"))
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task_l = mtl.load()
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task_l = [t for t in task_l if t.key == "A-DGN"] # FIXME: other models does not work well
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mtg = ModelTaskGen()
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impl_l = mtg.generate(task_l)
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# TODO: Align it with the benchmark framework after @wenjun's refine the evaluation part.
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# Currently, we just handcraft a workflow for fast evaluation.
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mil = ModelImpLoader(bench_folder / "gt_code")
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mie = ModelImpValEval()
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# Evaluation:
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eval_l = []
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for impl in impl_l:
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print(impl.target_task)
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gt_impl = mil.load(impl.target_task)
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eval_l.append(mie.evaluate(gt_impl, impl))
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print(eval_l)
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