mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 07:57:44 +00:00
Xu Yang
1d9b4cd2ec
* use CoSTEER as component name * rename factorimplementation to avoid confusion * rename modelimplementation * align benchmark and evolving evaluators * add scenario to evaluator init function * rename all factorimplementationknowledge in CoSTEER * remove all scenario related information in component * remove useless code --------- Co-authored-by: xuyang1 <xuyang1@microsoft.com>
90 lines
2.7 KiB
Python
90 lines
2.7 KiB
Python
import json
|
|
from pathlib import Path
|
|
from typing import Dict
|
|
|
|
from jinja2 import Environment, StrictUndefined
|
|
|
|
from rdagent.components.coder.factor_coder.CoSTEER.evolvable_subjects import (
|
|
FactorEvolvingItem,
|
|
)
|
|
from rdagent.components.coder.factor_coder.utils import get_data_folder_intro
|
|
from rdagent.core.conf import RD_AGENT_SETTINGS
|
|
from rdagent.core.log import RDAgentLog
|
|
from rdagent.core.prompts import Prompts
|
|
from rdagent.core.scenario import Scenario
|
|
from rdagent.oai.llm_utils import APIBackend
|
|
|
|
scheduler_prompts = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
|
|
|
|
|
|
def RandomSelect(to_be_finished_task_index, implementation_factors_per_round):
|
|
import random
|
|
|
|
to_be_finished_task_index = random.sample(
|
|
to_be_finished_task_index,
|
|
implementation_factors_per_round,
|
|
)
|
|
|
|
RDAgentLog().info(f"The random selection is: {to_be_finished_task_index}")
|
|
return to_be_finished_task_index
|
|
|
|
|
|
def LLMSelect(
|
|
to_be_finished_task_index,
|
|
implementation_factors_per_round,
|
|
evo: FactorEvolvingItem,
|
|
former_trace: Dict,
|
|
scen: Scenario,
|
|
):
|
|
tasks = []
|
|
for i in to_be_finished_task_index:
|
|
# find corresponding former trace for each task
|
|
target_factor_task_information = evo.sub_tasks[i].get_factor_information()
|
|
if target_factor_task_information in former_trace:
|
|
tasks.append((i, evo.sub_tasks[i], former_trace[target_factor_task_information]))
|
|
|
|
system_prompt = (
|
|
Environment(undefined=StrictUndefined)
|
|
.from_string(
|
|
scheduler_prompts["select_implementable_factor_system"],
|
|
)
|
|
.render(
|
|
scenario=scen.get_scenario_all_desc(),
|
|
)
|
|
)
|
|
|
|
while True:
|
|
user_prompt = (
|
|
Environment(undefined=StrictUndefined)
|
|
.from_string(
|
|
scheduler_prompts["select_implementable_factor_user"],
|
|
)
|
|
.render(
|
|
factor_num=implementation_factors_per_round,
|
|
sub_tasks=tasks,
|
|
)
|
|
)
|
|
if (
|
|
APIBackend().build_messages_and_calculate_token(
|
|
user_prompt=user_prompt,
|
|
system_prompt=system_prompt,
|
|
)
|
|
< RD_AGENT_SETTINGS.chat_token_limit
|
|
):
|
|
break
|
|
|
|
response = APIBackend().build_messages_and_create_chat_completion(
|
|
user_prompt=user_prompt,
|
|
system_prompt=system_prompt,
|
|
json_mode=True,
|
|
)
|
|
try:
|
|
selection = json.loads(response)["selected_factor"]
|
|
if not isinstance(selection, list):
|
|
return to_be_finished_task_index
|
|
selection_index = [x for x in selection if isinstance(x, int)]
|
|
except:
|
|
return to_be_finished_task_index
|
|
|
|
return selection_index
|