mirror of
https://gitee.com/ascend/ModelLink.git
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248 lines
8.2 KiB
Python
248 lines
8.2 KiB
Python
# coding=utf-8
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# Copyright (c) 2024, HUAWEI CORPORATION. All rights reserved.
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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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"""Sample Generate LLAMA"""
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import os
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import sys
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import time
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import logging
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from torch import distributed as dist
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from transformers import AutoTokenizer
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import modellink
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from megatron.initialize import initialize_megatron
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from megatron import get_args
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from megatron.model import GPTModel
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from megatron.arguments import core_transformer_config_from_args
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from modellink.tasks.evaluation.utils import add_text_generate_args
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from modellink.tasks.evaluation.eval_api.chat import Chat
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from modellink.tasks.evaluation.eval_impl.boolq_eval import BoolqEval
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from modellink.tasks.evaluation.eval_impl.gsm8k_eval import Gsm8kEval
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from modellink.tasks.evaluation.eval_impl.mmlu_eval import MmluEval
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from modellink.tasks.evaluation.eval_impl.ceval_exam import CEvalExam
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from modellink.tasks.evaluation.eval_impl.bbh_eval import BBHEval
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from modellink.tasks.evaluation.eval_impl.agi_eval import AGIEvalExam
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from modellink.tasks.evaluation.eval_impl.human_eval import HumanEval
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
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logging.getLogger().setLevel(logging.INFO)
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logger = logging.getLogger(__name__)
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def model_provider(pre_process=True, post_process=True):
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config = core_transformer_config_from_args(get_args())
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"""Build the model."""
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init_model = GPTModel(
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config,
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parallel_output=False,
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pre_process=pre_process,
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post_process=post_process
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)
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return init_model
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def get_result(result):
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if result:
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final_results = []
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if isinstance(result[0], list):
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for idx, res in enumerate(result[0]):
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final_result = [res]
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if result[1][idx][0][tokenizer.encode("Yes")[-1]] >= result[1][idx][0][tokenizer.encode("No")[-1]]:
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final_result.append('T')
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else:
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final_result.append('F')
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final_results.append(final_result)
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else:
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final_result = [result[0]]
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if result[1][0][tokenizer.encode("Yes")[-1]] >= result[1][0][tokenizer.encode("No")[-1]]:
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final_result.append('T')
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else:
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final_result.append('F')
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final_results.append(final_result)
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else:
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final_results = None
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return final_results
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class LLMChat(Chat):
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def __init__(self, llm_args):
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self.args = llm_args
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def chat(self, instruction, history):
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instruction_temp = [template.format(instruction=ins) for ins in instruction]
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result = model.generate(
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instruction_temp,
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do_sample=False,
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max_new_tokens=max_new_tokens,
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stream=False,
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return_output_log_probs=True
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)
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return get_result(result), dist.get_rank()
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def beam_search_chat(self, instruction, history):
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instruction_temp = template.format(instruction=instruction)
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result = model.generate(
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instruction_temp,
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do_sample=False,
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max_new_tokens=max_new_tokens,
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stream=False,
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num_beams=4,
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top_k=50,
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top_p=0.95,
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length_penalty=0.7
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)
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return [result], dist.get_rank()
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def mmlu(eval_args, agent):
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data_path = None
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for path in eval_args.task_data_path:
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if 'mmlu' in path:
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data_path = path
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try:
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if data_path:
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mmlu_eval = MmluEval(test_dir=data_path, batch_size=eval_args.evaluation_batch_size)
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answer, score_df = mmlu_eval.eval(chat=agent)
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logger.info(score_df)
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except Exception as e:
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logger.info(e)
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def gsm8k(eval_args, agent):
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data_path = None
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for path in eval_args.task_data_path:
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if 'gsm8k' in path:
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data_path = path
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try:
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if data_path:
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gsm8k_eval = Gsm8kEval(test_dir=data_path, batch_size=eval_args.evaluation_batch_size)
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answer, score_df = gsm8k_eval.eval(chat=agent)
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logger.info(score_df)
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except Exception as e:
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logger.info(e)
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def boolq(eval_args, agent):
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data_path = None
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for path in eval_args.task_data_path:
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if 'boolq' in path:
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data_path = path
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try:
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if data_path:
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boolq_eval = BoolqEval(test_dir=data_path, batch_size=eval_args.evaluation_batch_size)
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answer, score_df = boolq_eval.eval(chat=agent)
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logger.info(score_df)
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except Exception as e:
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logger.info(e)
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def ceval(eval_args, agent):
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data_path = None
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for path in eval_args.task_data_path:
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if 'ceval' in path:
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data_path = path
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try:
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if data_path:
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ceval_exam = CEvalExam(test_dir=data_path, batch_size=eval_args.evaluation_batch_size)
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answer, score_df = ceval_exam.eval(chat=agent)
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logger.info(score_df)
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except Exception as e:
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logger.info(e)
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def human_eval(eval_args, agent):
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data_path = None
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for path in eval_args.task_data_path:
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if 'human_eval' in path:
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data_path = path
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try:
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if data_path:
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human_eval_exam = HumanEval(test_dir=data_path)
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answer, score_df = human_eval_exam.eval(chat=agent)
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logger.info(score_df)
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except Exception as e:
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logger.info(e)
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def agi_eval(eval_args, agent):
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data_path = None
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for path in eval_args.task_data_path:
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if 'agieval' in path:
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data_path = path
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try:
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if data_path:
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agieval_exam = AGIEvalExam(test_dir=data_path, batch_size=eval_args.evaluation_batch_size)
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answer, score_df = agieval_exam.eval(chat=agent)
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logger.info(score_df)
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except Exception as e:
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logger.info(e)
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def bbh_eval(eval_args, agent):
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data_path = None
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for path in eval_args.task_data_path:
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if 'bbh' in path:
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data_path = path
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try:
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if data_path:
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bbh = BBHEval(test_dir=data_path, batch_size=eval_args.evaluation_batch_size)
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answer, score_df = bbh.eval(chat=agent)
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logger.info(score_df)
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except Exception as e:
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logger.info(e)
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if __name__ == "__main__":
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initialize_megatron(extra_args_provider=add_text_generate_args,
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args_defaults={'no_load_rng': True,
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'no_load_optim': True})
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args = get_args()
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model = GPTModel.from_pretrained(
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model_provider=model_provider,
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pretrained_model_name_or_path=args.load
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)
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tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name_or_path, trust_remote_code=True)
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max_new_tokens = args.max_new_tokens
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template = "{instruction}"
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if 'mmlu' in args.task:
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a = time.time()
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mmlu(args, LLMChat(args))
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logger.info(f'MMLU Running Time:, {time.time() - a}')
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if 'gsm8k' in args.task:
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a = time.time()
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gsm8k(args, LLMChat(args))
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logger.info(f'GSM8k Running Time: {time.time() - a}')
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if 'boolq' in args.task:
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a = time.time()
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boolq(args, LLMChat(args))
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logger.info(f'Boolq Running Time: {time.time() - a}')
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if 'ceval' in args.task:
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a = time.time()
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ceval(args, LLMChat(args))
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logger.info(f'Ceval Running Time: {time.time() - a}')
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if 'bbh' in args.task:
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a = time.time()
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bbh_eval(args, LLMChat(args))
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logger.info(f'bbh Running Time: {time.time() - a}')
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if 'agieval' in args.task:
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a = time.time()
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agi_eval(args, LLMChat(args))
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logger.info(f'agi_eval Running Time: {time.time() - a}')
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if 'human_eval' in args.task:
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a = time.time()
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human_eval(args, LLMChat(args))
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logger.info(f'Human_eval Running Time: {time.time() - a}')
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