298b64217e
* add auto triggered acc test * use llama 7b instead * fix env * debug download * fix download prefix * add cut dirs * fix env of model path * fix dataset download * full job * source xpu env vars * use matrix to trigger model run * reset batch=1 * remove redirect * remove some trigger * add task matrix * add precision list * test llama-7b-chat * use /mnt/disk1 to store model and datasets * remove installation test * correct downloading path * fix HF vars * add bigdl-llm env vars * rename file * fix hf_home * fix script path * rename as harness evalution * rerun
82 lines
4.7 KiB
Python
82 lines
4.7 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# this code is copied from llama2 example test, and added performance test
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import argparse
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import os
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import subprocess
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task_cmd = "--num_fewshot {} --tasks {}"
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task_map = {
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"hellaswag": task_cmd.format(10, "hellaswag"),
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"arc": task_cmd.format(25, "arc_challenge"),
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"truthfulqa": task_cmd.format(0, "truthfulqa_mc"),
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"mmlu": task_cmd.format(5, "hendrycksTest-abstract_algebra,hendrycksTest-anatomy,hendrycksTest-astronomy,hendrycksTest-business_ethics,hendrycksTest-clinical_knowledge,hendrycksTest-college_biology,hendrycksTest-college_chemistry,hendrycksTest-college_computer_science,hendrycksTest-college_mathematics,hendrycksTest-college_medicine,hendrycksTest-college_physics,hendrycksTest-computer_security,hendrycksTest-conceptual_physics,hendrycksTest-econometrics,hendrycksTest-electrical_engineering,hendrycksTest-elementary_mathematics,hendrycksTest-formal_logic,hendrycksTest-global_facts,hendrycksTest-high_school_biology,hendrycksTest-high_school_chemistry,hendrycksTest-high_school_computer_science,hendrycksTest-high_school_european_history,hendrycksTest-high_school_geography,hendrycksTest-high_school_government_and_politics,hendrycksTest-high_school_macroeconomics,hendrycksTest-high_school_mathematics,hendrycksTest-high_school_microeconomics,hendrycksTest-high_school_physics,hendrycksTest-high_school_psychology,hendrycksTest-high_school_statistics,hendrycksTest-high_school_us_history,hendrycksTest-high_school_world_history,hendrycksTest-human_aging,hendrycksTest-human_sexuality,hendrycksTest-international_law,hendrycksTest-jurisprudence,hendrycksTest-logical_fallacies,hendrycksTest-machine_learning,hendrycksTest-management,hendrycksTest-marketing,hendrycksTest-medical_genetics,hendrycksTest-miscellaneous,hendrycksTest-moral_disputes,hendrycksTest-moral_scenarios,hendrycksTest-nutrition,hendrycksTest-philosophy,hendrycksTest-prehistory,hendrycksTest-professional_accounting,hendrycksTest-professional_law,hendrycksTest-professional_medicine,hendrycksTest-professional_psychology,hendrycksTest-public_relations,hendrycksTest-security_studies,hendrycksTest-sociology,hendrycksTest-us_foreign_policy,hendrycksTest-virology,hendrycksTest-world_religions")
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}
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prec_to_arg = {
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"bigdl-llm": {
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"int4": "load_in_low_bit=sym_int4",
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"nf4": "load_in_low_bit=nf4",
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"nf3": "load_in_low_bit=nf3",
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"fp8": "load_in_low_bit=fp8",
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"fp4": "load_in_low_bit=fp4",
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"bf16": "dtype=bfloat16",
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"fp16": "dtype=float16",
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},
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"hf-causal": {
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"nf4": "bnb_type=nf4",
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"bf16": "dtype=bfloat16",
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"fp16": "dtype=float16",
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}
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}
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", required=True, type=str)
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parser.add_argument("--pretrained", required=True, type=str)
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parser.add_argument("--precision", required=True, nargs='+', type=str)
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parser.add_argument("--device", required=True, type=str)
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parser.add_argument("--batch", default=1, type=int)
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parser.add_argument("--tasks", required=True, nargs='+', type=str)
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parser.add_argument("--output_dir", type=str)
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args = parser.parse_args()
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print(args.model)
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print(args.tasks)
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basic_cmd = "python lm-evaluation-harness/main.py --model {} --model_args pretrained={},{} --no_cache --device {} --batch_size {} {} --output_path {} "
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os.makedirs(args.output_dir, exist_ok=True)
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index = 1
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total = len(args.precision) * len(args.tasks)
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for prec in args.precision:
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prec_arg = prec_to_arg[args.model][prec]
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for task in args.tasks:
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output_path = f"{args.model}_{prec}_{args.device}_{task}"
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task_arg = task_map[task]
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cmd_exec = basic_cmd.format(args.model, args.pretrained, prec_arg, args.device, args.batch,
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task_arg, f"{args.output_dir}/{output_path}")
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print(f"Running job {index}/{total}:\n{cmd_exec}")
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index += 1
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with open(f"{args.output_dir}/log_{output_path}.txt", "w") as f:
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return_code = subprocess.call(cmd_exec, shell=True)
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if return_code == 0:
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print("Successful")
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else:
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print("Failed")
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main()
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