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feat: add RD-Agent-Quant scenario (#838)

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* fix model input shape bug and costeer_model bug

* fix a bug

* fix a bug in docker result extraction

* a system-level optimization

* add a filter of stdout

* update

* add stdout to model

* model training_hyperparameters update

* quant scenario

* update some quant settings

* llm choose action

* Thompson Sampling Bandit for action choosing

* refine both scens

* add trace messages for quant scen

* fix some bugs

* fix some bugs

* update

* update

* update

* fix

* fix

* fix

* update for merge

* fix ci

* fix some bugs

* fix ci

* fix ci

* fix ci

* fix ci

* refactor

* default qlib4rdagent local env downloading

* fix ci

* fix ci

* fix a bug

* fix ci

* fix: align all prompts on template (#908)

* use template to render all prompts

* fix CI

---------

Co-authored-by: Xu Yang <xuyang1@microsoft.com>

* add fin_quant in cli

* fix a bug

* fix ci

* fix some bugs

* refactor

* remove the columns in hypothesis if no value generated in this column

* fix a bug

* fix ci

* fix conda env

* add qlib gitignore

* remove existed qlib folder & install torch in qlib conda

* fix workspace ui in feedback

* align model config in coder and runner in docker or conda

* fix CI

* fix CI

---------

Co-authored-by: Xu Yang <peteryang@vip.qq.com>
Co-authored-by: Xu Yang <xuyang1@microsoft.com>
This commit is contained in:
Yuante Li
2025-05-29 16:16:51 +08:00
committed by GitHub
parent b0e88c7375
commit d1019cb568
64 changed files with 2427 additions and 1025 deletions
Download Patch File Download Diff File Expand all files Collapse all files
rdagent/components/coder/factor_coder/evolving_strategy.py
+16 -45
View File
@@ -1,11 +1,8 @@
from __future__ import annotations
import json
from pathlib import Path
from typing import Dict
from jinja2 import Environment, StrictUndefined
from rdagent.components.coder.CoSTEER.evaluators import CoSTEERSingleFeedback
from rdagent.components.coder.CoSTEER.evolving_strategy import (
MultiProcessEvolvingStrategy,
@@ -17,11 +14,9 @@ from rdagent.components.coder.CoSTEER.knowledge_management import (
from rdagent.components.coder.factor_coder.config import FACTOR_COSTEER_SETTINGS
from rdagent.components.coder.factor_coder.factor import FactorFBWorkspace, FactorTask
from rdagent.core.experiment import FBWorkspace
from rdagent.core.prompts import Prompts
from rdagent.oai.llm_conf import LLM_SETTINGS
from rdagent.oai.llm_utils import APIBackend
implement_prompts = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
from rdagent.utils.agent.tpl import T
class FactorMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
@@ -36,24 +31,14 @@ class FactorMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
queried_former_failed_knowledge_to_render: list,
queried_similar_error_knowledge_to_render: list,
) -> str:
error_summary_system_prompt = (
Environment(undefined=StrictUndefined)
.from_string(implement_prompts["evolving_strategy_error_summary_v2_system"])
.render(
scenario=self.scen.get_scenario_all_desc(target_task),
factor_information_str=target_task.get_task_information(),
code_and_feedback=queried_former_failed_knowledge_to_render[-1].get_implementation_and_feedback_str(),
)
.strip("\n")
error_summary_system_prompt = T(".prompts:evolving_strategy_error_summary_v2_system").r(
scenario=self.scen.get_scenario_all_desc(target_task),
factor_information_str=target_task.get_task_information(),
code_and_feedback=queried_former_failed_knowledge_to_render[-1].get_implementation_and_feedback_str(),
)
for _ in range(10): # max attempt to reduce the length of error_summary_user_prompt
error_summary_user_prompt = (
Environment(undefined=StrictUndefined)
.from_string(implement_prompts["evolving_strategy_error_summary_v2_user"])
.render(
queried_similar_error_knowledge=queried_similar_error_knowledge_to_render,
)
.strip("\n")
error_summary_user_prompt = T(".prompts:evolving_strategy_error_summary_v2_user").r(
queried_similar_error_knowledge=queried_similar_error_knowledge_to_render,
)
if (
APIBackend().build_messages_and_calculate_token(
@@ -106,16 +91,9 @@ class FactorMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
latest_attempt_to_latest_successful_execution = queried_knowledge.task_to_former_failed_traces[
target_factor_task_information
][1]
system_prompt = (
Environment(undefined=StrictUndefined)
.from_string(
implement_prompts["evolving_strategy_factor_implementation_v1_system"],
)
.render(
scenario=self.scen.get_scenario_all_desc(target_task, filtered_tag="feature"),
queried_former_failed_knowledge=queried_former_failed_knowledge_to_render,
)
system_prompt = T(".prompts:evolving_strategy_factor_implementation_v1_system").r(
scenario=self.scen.get_scenario_all_desc(target_task, filtered_tag="feature"),
queried_former_failed_knowledge=queried_former_failed_knowledge_to_render,
)
queried_similar_successful_knowledge_to_render = queried_similar_successful_knowledge
queried_similar_error_knowledge_to_render = queried_similar_error_knowledge
@@ -136,19 +114,12 @@ class FactorMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
else:
error_summary_critics = None
# 构建user_prompt。开始写代码
user_prompt = (
Environment(undefined=StrictUndefined)
.from_string(
implement_prompts["evolving_strategy_factor_implementation_v2_user"],
)
.render(
factor_information_str=target_factor_task_information,
queried_similar_successful_knowledge=queried_similar_successful_knowledge_to_render,
queried_similar_error_knowledge=queried_similar_error_knowledge_to_render,
error_summary_critics=error_summary_critics,
latest_attempt_to_latest_successful_execution=latest_attempt_to_latest_successful_execution,
)
.strip("\n")
user_prompt = T(".prompts:evolving_strategy_factor_implementation_v2_user").r(
factor_information_str=target_factor_task_information,
queried_similar_successful_knowledge=queried_similar_successful_knowledge_to_render,
queried_similar_error_knowledge=queried_similar_error_knowledge_to_render,
error_summary_critics=error_summary_critics,
latest_attempt_to_latest_successful_execution=latest_attempt_to_latest_successful_execution,
)
if (
APIBackend().build_messages_and_calculate_token(user_prompt=user_prompt, system_prompt=system_prompt)