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

* 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>
2026-08-01 09:27:43 +00:00 · 2025-05-29 16:16:51 +08:00
parent b0e88c7375
commit d1019cb568
64 changed files with 2427 additions and 1025 deletions
@@ -0,0 +1,13 @@
+from pydantic_settings import SettingsConfigDict
+
+from rdagent.components.coder.CoSTEER.config import CoSTEERSettings
+
+
+class ModelCoSTEERSettings(CoSTEERSettings):
+    model_config = SettingsConfigDict(env_prefix="MODEL_CoSTEER_")
+
+    env_type: str = "conda"  # or "docker"
+    """Environment to run model code in coder and runner: 'conda' for local conda env, 'docker' for Docker container"""
+
+
+MODEL_COSTEER_SETTINGS = ModelCoSTEERSettings()
@@ -1,18 +1,14 @@
 import json
-from pathlib import Path
 from typing import Dict, Tuple

 import numpy as np
-from jinja2 import Environment, StrictUndefined

 from rdagent.components.coder.CoSTEER.evaluators import CoSTEEREvaluator
 from rdagent.components.coder.model_coder.model import ModelFBWorkspace, ModelTask
 from rdagent.core.experiment import Task, Workspace
-from rdagent.core.prompts import Prompts
 from rdagent.oai.llm_conf import LLM_SETTINGS
 from rdagent.oai.llm_utils import APIBackend
-
-evaluate_prompts = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
+from rdagent.utils.agent.tpl import T


 # This shape evaluator is also used in data_science
@@ -70,32 +66,21 @@ class ModelCodeEvaluator(CoSTEEREvaluator):
        model_task_information = target_task.get_task_information()
        code = implementation.all_codes

-        system_prompt = (
-            Environment(undefined=StrictUndefined)
-            .from_string(evaluate_prompts["evaluator_code_feedback"]["system"])
-            .render(
-                scenario=(
-                    self.scen.get_scenario_all_desc(target_task, filtered_tag=target_task.model_type)
-                    if self.scen is not None
-                    else "No scenario description."
-                )
+        system_prompt = T(".prompts:evaluator_code_feedback.system").r(
+            scenario=(
+                self.scen.get_scenario_all_desc(target_task, filtered_tag=target_task.model_type)
+                if self.scen is not None
+                else "No scenario description."
            )
        )
-
        execution_feedback_to_render = model_execution_feedback
        for _ in range(10):  # 10 times to split the content is enough
-            user_prompt = (
-                Environment(undefined=StrictUndefined)
-                .from_string(
-                    evaluate_prompts["evaluator_code_feedback"]["user"],
-                )
-                .render(
-                    model_information=model_task_information,
-                    code=code,
-                    model_execution_feedback=execution_feedback_to_render,
-                    model_value_feedback=model_value_feedback,
-                    gt_code=gt_implementation.all_codes if gt_implementation else None,
-                )
+            user_prompt = T(".prompts:evaluator_code_feedback.user").r(
+                model_information=model_task_information,
+                code=code,
+                model_execution_feedback=execution_feedback_to_render,
+                model_value_feedback=model_value_feedback,
+                gt_code=gt_implementation.all_codes if gt_implementation else None,
            )
            if (
                APIBackend().build_messages_and_calculate_token(
@@ -133,34 +118,25 @@ class ModelFinalEvaluator(CoSTEEREvaluator):
        if gt_implementation is not None:
            assert isinstance(gt_implementation, ModelFBWorkspace)

-        system_prompt = (
-            Environment(undefined=StrictUndefined)
-            .from_string(evaluate_prompts["evaluator_final_feedback"]["system"])
-            .render(
-                scenario=(
-                    self.scen.get_scenario_all_desc(target_task, filtered_tag=target_task.model_type)
-                    if self.scen is not None
-                    else "No scenario description."
-                )
+        system_prompt = T(".prompts:evaluator_final_feedback.system").r(
+            scenario=(
+                self.scen.get_scenario_all_desc(target_task, filtered_tag=target_task.model_type)
+                if self.scen is not None
+                else "No scenario description."
            )
        )

        execution_feedback_to_render = model_execution_feedback

        for _ in range(10):  # 10 times to split the content is enough
-            user_prompt = (
-                Environment(undefined=StrictUndefined)
-                .from_string(
-                    evaluate_prompts["evaluator_final_feedback"]["user"],
-                )
-                .render(
-                    model_information=target_task.get_task_information(),
-                    model_execution_feedback=execution_feedback_to_render,
-                    model_shape_feedback=model_shape_feedback,
-                    model_code_feedback=model_code_feedback,
-                    model_value_feedback=model_value_feedback,
-                )
+            user_prompt = T(".prompts:evaluator_final_feedback.user").r(
+                model_information=target_task.get_task_information(),
+                model_execution_feedback=execution_feedback_to_render,
+                model_shape_feedback=model_shape_feedback,
+                model_code_feedback=model_code_feedback,
+                model_value_feedback=model_value_feedback,
            )
+
            if (
                APIBackend().build_messages_and_calculate_token(
                    user_prompt=user_prompt,
@@ -1,9 +1,6 @@
 import json
-from pathlib import Path
 from typing import Dict

-from jinja2 import Environment, StrictUndefined
-
 from rdagent.components.coder.CoSTEER.config import CoSTEER_SETTINGS
 from rdagent.components.coder.CoSTEER.evaluators import CoSTEERSingleFeedback
 from rdagent.components.coder.CoSTEER.evolving_strategy import (
@@ -14,16 +11,13 @@ from rdagent.components.coder.CoSTEER.knowledge_management import (
    CoSTEERQueriedKnowledgeV2,
 )
 from rdagent.components.coder.model_coder.model import (
-    ModelExperiment,
    ModelFBWorkspace,
    ModelTask,
 )
 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
-
-coder_prompts = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
+from rdagent.utils.agent.tpl import T


 class ModelMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
@@ -52,31 +46,18 @@ class ModelMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
            if isinstance(queried_knowledge, CoSTEERQueriedKnowledgeV2)
            else queried_former_failed_knowledge
        )
-        system_prompt = (
-            Environment(undefined=StrictUndefined)
-            .from_string(
-                coder_prompts["evolving_strategy_model_coder"]["system"],
-            )
-            .render(
-                scenario=self.scen.get_scenario_all_desc(filtered_tag=target_task.model_type),
-                queried_former_failed_knowledge=queried_former_failed_knowledge_to_render,
-                current_code=workspace.file_dict.get("model.py"),
-            )
+        system_prompt = T(".prompts:evolving_strategy_model_coder.system").r(
+            scenario=self.scen.get_scenario_all_desc(filtered_tag="model"),
+            queried_former_failed_knowledge=queried_former_failed_knowledge_to_render,
+            current_code=workspace.file_dict.get("model.py"),
        )

        queried_similar_successful_knowledge_to_render = queried_similar_successful_knowledge
        for _ in range(10):  # max attempt to reduce the length of user_prompt
-            user_prompt = (
-                Environment(undefined=StrictUndefined)
-                .from_string(
-                    coder_prompts["evolving_strategy_model_coder"]["user"],
-                )
-                .render(
-                    model_information_str=model_information_str,
-                    queried_similar_successful_knowledge=queried_similar_successful_knowledge_to_render,
-                    queried_former_failed_knowledge=queried_former_failed_knowledge_to_render,
-                )
-                .strip("\n")
+            user_prompt = T(".prompts:evolving_strategy_model_coder.user").r(
+                model_information_str=model_information_str,
+                queried_similar_successful_knowledge=queried_similar_successful_knowledge_to_render,
+                queried_former_failed_knowledge=queried_former_failed_knowledge_to_render,
            )
            if (
                APIBackend().build_messages_and_calculate_token(
@@ -5,10 +5,11 @@ from pathlib import Path
 from typing import Dict, Optional

 from rdagent.components.coder.CoSTEER.task import CoSTEERTask
+from rdagent.components.coder.model_coder.conf import MODEL_COSTEER_SETTINGS
 from rdagent.core.experiment import Experiment, FBWorkspace
 from rdagent.core.utils import cache_with_pickle
 from rdagent.oai.llm_utils import md5_hash
-from rdagent.utils.env import KGDockerEnv, QTDockerEnv
+from rdagent.utils.env import KGDockerEnv, QlibCondaConf, QlibCondaEnv, QTDockerEnv


 class ModelTask(CoSTEERTask):
@@ -19,6 +20,7 @@ class ModelTask(CoSTEERTask):
        architecture: str,
        *args,
        hyperparameters: Dict[str, str],
+        training_hyperparameters: Dict[str, str],
        formulation: str = None,
        variables: Dict[str, str] = None,
        model_type: Optional[str] = None,
@@ -28,6 +30,7 @@ class ModelTask(CoSTEERTask):
        self.architecture: str = architecture
        self.variables: str = variables
        self.hyperparameters: str = hyperparameters
+        self.training_hyperparameters: str = training_hyperparameters
        self.model_type: str = (
            model_type  # Tabular for tabular model, TimesSeries for time series model, Graph for graph model, XGBoost for XGBoost model
        )
@@ -41,6 +44,17 @@ description: {self.description}
        task_desc += f"architecture: {self.architecture}\n"
        task_desc += f"variables: {self.variables}\n" if self.variables else ""
        task_desc += f"hyperparameters: {self.hyperparameters}\n"
+        task_desc += f"training_hyperparameters: {self.training_hyperparameters}\n"
+        task_desc += f"model_type: {self.model_type}\n"
+        return task_desc
+
+    def get_task_brief_information(self):
+        task_desc = f"""name: {self.name}
+description: {self.description}
+"""
+        task_desc += f"architecture: {self.architecture}\n"
+        task_desc += f"hyperparameters: {self.hyperparameters}\n"
+        task_desc += f"training_hyperparameters: {self.training_hyperparameters}\n"
        task_desc += f"model_type: {self.model_type}\n"
        return task_desc

@@ -99,7 +113,15 @@ class ModelFBWorkspace(FBWorkspace):
    ):
        self.before_execute()
        try:
-            qtde = QTDockerEnv() if self.target_task.version == 1 else KGDockerEnv()
+            if self.target_task.version == 1:
+                if MODEL_COSTEER_SETTINGS.env_type == "docker":
+                    qtde = QTDockerEnv()
+                elif MODEL_COSTEER_SETTINGS.env_type == "conda":
+                    qtde = QlibCondaEnv(conf=QlibCondaConf())
+                else:
+                    raise ValueError(f"Unknown env_type: {MODEL_COSTEER_SETTINGS.env_type}")
+            else:
+                qtde = KGDockerEnv()
            qtde.prepare()

            if self.target_task.version == 1:
@@ -1,12 +1,10 @@
 import re
 from pathlib import Path

-from jinja2 import Environment, StrictUndefined
-
 from rdagent.components.coder.model_coder.model import ModelExperiment, ModelFBWorkspace
 from rdagent.core.developer import Developer
-from rdagent.core.prompts import Prompts
 from rdagent.oai.llm_utils import APIBackend
+from rdagent.utils.agent.tpl import T

 DIRNAME = Path(__file__).absolute().resolve().parent

@@ -17,18 +15,14 @@ class ModelCodeWriter(Developer[ModelExperiment]):
        for t in exp.sub_tasks:
            mti = ModelFBWorkspace(t)
            mti.prepare()
-            pr = Prompts(file_path=DIRNAME / "prompt.yaml")

-            user_prompt_tpl = Environment(undefined=StrictUndefined).from_string(pr["code_implement_user"])
-            sys_prompt_tpl = Environment(undefined=StrictUndefined).from_string(pr["code_implement_sys"])
-
-            user_prompt = user_prompt_tpl.render(
+            user_prompt = T(".prompts:code_implement_user").r(
                name=t.name,
                description=t.description,
                formulation=t.formulation,
                variables=t.variables,
            )
-            system_prompt = sys_prompt_tpl.render()
+            system_prompt = T(".prompts:code_implement_sys").r()

            resp = APIBackend().build_messages_and_create_chat_completion(user_prompt, system_prompt)

@@ -2,19 +2,16 @@ from __future__ import annotations

 import json
 import re
-from pathlib import Path

-from rdagent.components.coder.model_coder.model import ModelExperiment, ModelTask
+from rdagent.components.coder.model_coder.model import ModelTask
 from rdagent.components.document_reader.document_reader import (
    load_and_process_pdfs_by_langchain,
 )
 from rdagent.components.loader.task_loader import ModelTaskLoader
-from rdagent.core.prompts import Prompts
 from rdagent.log import rdagent_logger as logger
 from rdagent.oai.llm_utils import APIBackend
 from rdagent.scenarios.qlib.experiment.model_experiment import QlibModelExperiment
-
-document_process_prompts = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
+from rdagent.utils.agent.tpl import T


 def extract_model_from_doc(doc_content: str) -> dict:
@@ -32,7 +29,7 @@ def extract_model_from_doc(doc_content: str) -> dict:
        {model_name: dict{description, formulation, variables}}
    """
    session = APIBackend().build_chat_session(
-        session_system_prompt=document_process_prompts["extract_model_formulation_system"],
+        session_system_prompt=T(".prompts:extract_model_formulation_system").r(),
    )
    current_user_prompt = doc_content