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from optimization_pipeline import OptimizationPipeline |
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from utils.config import load_yaml, modify_input_for_ranker, validate_generation_config, override_config |
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import argparse |
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import os |
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from estimator.estimator_llm import LLMEstimator |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--generation_config_path', default='config/config_diff/config_generation.yml', type=str, help='Configuration file path') |
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parser.add_argument('--ranker_config_path', default='config/config_diff/config_ranking.yml', type=str, help='Configuration file path') |
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parser.add_argument('--task_description', |
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default='', |
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required=False, type=str, help='Describing the task') |
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parser.add_argument('--prompt', |
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default='', |
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required=False, type=str, help='Prompt to use as initial.') |
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parser.add_argument('--load_dump', default='dump', required=False, type=str, help='In case of loading from checkpoint') |
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parser.add_argument('--output_dump', default='dump', required=False, type=str, help='Output to save checkpoints') |
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parser.add_argument('--num_ranker_steps', default=20, type=int, help='Number of iterations') |
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parser.add_argument('--num_generation_steps', default=20, type=int, help='Number of iterations') |
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opt = parser.parse_args() |
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ranker_config_params = override_config(opt.ranker_config_path) |
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generation_config_params = override_config(opt.generation_config_path) |
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validate_generation_config(ranker_config_params, generation_config_params) |
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if opt.task_description == '': |
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task_description = input("Describe the task: ") |
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else: |
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task_description = opt.task_description |
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if opt.prompt == '': |
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initial_prompt = input("Initial prompt: ") |
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else: |
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initial_prompt = opt.prompt |
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ranker_pipeline = OptimizationPipeline(ranker_config_params, output_path=os.path.join(opt.output_dump, 'ranker')) |
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if opt.load_dump != '': |
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ranker_pipeline.load_state(os.path.join(opt.load_dump, 'ranker')) |
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ranker_pipeline.predictor.init_chain(ranker_config_params.dataset.label_schema) |
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if (ranker_pipeline.cur_prompt is None) or (ranker_pipeline.task_description is None): |
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ranker_mod_prompt, ranker_mod_task_desc = modify_input_for_ranker(ranker_config_params, task_description, |
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initial_prompt) |
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ranker_pipeline.cur_prompt = ranker_mod_prompt |
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ranker_pipeline.task_description = ranker_mod_task_desc |
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best_prompt = ranker_pipeline.run_pipeline(opt.num_ranker_steps) |
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generation_config_params.eval.function_params = ranker_config_params.predictor.config |
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generation_config_params.eval.function_params.instruction = best_prompt['prompt'] |
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generation_config_params.eval.function_params.label_schema = ranker_config_params.dataset.label_schema |
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generation_pipeline = OptimizationPipeline(generation_config_params, task_description, initial_prompt, |
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output_path=os.path.join(opt.output_dump, 'generator')) |
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if opt.load_dump != '': |
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generation_pipeline.load_state(os.path.join(opt.load_dump, 'generator')) |
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best_generation_prompt = generation_pipeline.run_pipeline(opt.num_generation_steps) |
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print('\033[92m' + 'Calibrated prompt score:', str(best_generation_prompt['score']) + '\033[0m') |
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print('\033[92m' + 'Calibrated prompt:', best_generation_prompt['prompt'] + '\033[0m') |
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