mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 16:07:46 +00:00
Xu Yang
768229427d
* simplify RDAgent conf * add unified cacher(untested) * fix small bugs * fix a bug * fix a small bug in runner * use hash_key = None to skip cache * fix CI * in factor execution, ignore cache when raise exception * add file locker to avoid mp calling * fix CI * use function __module__ name as folder in cache
89 lines
2.7 KiB
Python
89 lines
2.7 KiB
Python
import json
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from pathlib import Path
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from typing import Dict
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from jinja2 import Environment, StrictUndefined
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from rdagent.components.coder.factor_coder.CoSTEER.evolvable_subjects import (
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FactorEvolvingItem,
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)
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from rdagent.core.prompts import Prompts
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from rdagent.core.scenario import Scenario
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from rdagent.log import rdagent_logger as logger
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from rdagent.oai.llm_conf import LLM_SETTINGS
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from rdagent.oai.llm_utils import APIBackend
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scheduler_prompts = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
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def RandomSelect(to_be_finished_task_index, implementation_factors_per_round):
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import random
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to_be_finished_task_index = random.sample(
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to_be_finished_task_index,
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implementation_factors_per_round,
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)
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logger.info(f"The random selection is: {to_be_finished_task_index}")
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return to_be_finished_task_index
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def LLMSelect(
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to_be_finished_task_index,
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implementation_factors_per_round,
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evo: FactorEvolvingItem,
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former_trace: Dict,
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scen: Scenario,
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):
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tasks = []
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for i in to_be_finished_task_index:
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# find corresponding former trace for each task
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target_factor_task_information = evo.sub_tasks[i].get_task_information()
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if target_factor_task_information in former_trace:
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tasks.append((i, evo.sub_tasks[i], former_trace[target_factor_task_information][0]))
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system_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(
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scheduler_prompts["select_implementable_factor_system"],
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)
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.render(
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scenario=scen.get_scenario_all_desc(),
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)
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)
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for _ in range(10): # max attempt to reduce the length of user_prompt
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user_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(
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scheduler_prompts["select_implementable_factor_user"],
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)
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.render(
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factor_num=implementation_factors_per_round,
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sub_tasks=tasks,
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)
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)
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if (
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APIBackend().build_messages_and_calculate_token(
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user_prompt=user_prompt,
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system_prompt=system_prompt,
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)
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< LLM_SETTINGS.chat_token_limit
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):
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break
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response = APIBackend().build_messages_and_create_chat_completion(
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user_prompt=user_prompt,
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system_prompt=system_prompt,
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json_mode=True,
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)
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try:
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selection = json.loads(response)["selected_factor"]
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if not isinstance(selection, list):
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return to_be_finished_task_index
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selection_index = [x for x in selection if isinstance(x, int)]
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except:
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return to_be_finished_task_index
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return selection_index
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