Implement model (and some factor) coder with evolving (#52)

* store code into FBImplementation * fix path related bugs * fix a bug * fix factor related small bugs * re-submit all model related code * new code to model coder * finish the model evolving code --------- Co-authored-by: xuyang1 <xuyang1@microsoft.com>
2026-08-04 18:57:44 +00:00 · 2024-07-10 15:45:43 +08:00
parent 63f7bf23da
commit 22e1aa3330
24 changed files with 1243 additions and 291 deletions
@@ -0,0 +1,86 @@
+import pickle
+from pathlib import Path
+
+from rdagent.components.coder.model_coder.conf import MODEL_IMPL_SETTINGS
+from rdagent.components.coder.model_coder.CoSTEER.evaluators import (
+    ModelCoderMultiEvaluator,
+)
+from rdagent.components.coder.model_coder.CoSTEER.evolvable_subjects import (
+    ModelEvolvingItem,
+)
+from rdagent.components.coder.model_coder.CoSTEER.evolving_strategy import (
+    ModelCoderEvolvingStrategy,
+)
+from rdagent.components.coder.model_coder.CoSTEER.knowledge_management import (
+    ModelKnowledgeBase,
+    ModelRAGStrategy,
+)
+from rdagent.components.coder.model_coder.model import ModelExperiment
+from rdagent.core.evolving_agent import RAGEvoAgent
+from rdagent.core.task_generator import TaskGenerator
+
+
+class ModelCoSTEER(TaskGenerator[ModelExperiment]):
+    def __init__(
+        self,
+        *args,
+        with_knowledge: bool = True,
+        with_feedback: bool = True,
+        knowledge_self_gen: bool = True,
+        **kwargs,
+    ) -> None:
+        super().__init__(*args, **kwargs)
+        self.max_loop = MODEL_IMPL_SETTINGS.max_loop
+        self.knowledge_base_path = (
+            Path(MODEL_IMPL_SETTINGS.knowledge_base_path)
+            if MODEL_IMPL_SETTINGS.knowledge_base_path is not None
+            else None
+        )
+        self.new_knowledge_base_path = (
+            Path(MODEL_IMPL_SETTINGS.new_knowledge_base_path)
+            if MODEL_IMPL_SETTINGS.new_knowledge_base_path is not None
+            else None
+        )
+        self.with_knowledge = with_knowledge
+        self.with_feedback = with_feedback
+        self.knowledge_self_gen = knowledge_self_gen
+        self.evolving_strategy = ModelCoderEvolvingStrategy(scen=self.scen)
+        self.model_evaluator = ModelCoderMultiEvaluator(scen=self.scen)
+
+    def load_or_init_knowledge_base(self, former_knowledge_base_path: Path = None, component_init_list: list = []):
+        if former_knowledge_base_path is not None and former_knowledge_base_path.exists():
+            model_knowledge_base = pickle.load(open(former_knowledge_base_path, "rb"))
+            if not isinstance(model_knowledge_base, ModelKnowledgeBase):
+                raise ValueError("The former knowledge base is not compatible with the current version")
+        else:
+            model_knowledge_base = ModelKnowledgeBase()
+
+        return model_knowledge_base
+
+    def generate(self, exp: ModelExperiment) -> ModelExperiment:
+        # init knowledge base
+        model_knowledge_base = self.load_or_init_knowledge_base(
+            former_knowledge_base_path=self.knowledge_base_path,
+            component_init_list=[],
+        )
+        # init rag method
+        self.rag = ModelRAGStrategy(model_knowledge_base)
+
+        # init intermediate items
+        model_experiment = ModelEvolvingItem(sub_tasks=exp.sub_tasks)
+
+        self.evolve_agent = RAGEvoAgent(max_loop=self.max_loop, evolving_strategy=self.evolving_strategy, rag=self.rag)
+
+        model_experiment = self.evolve_agent.multistep_evolve(
+            model_experiment,
+            self.model_evaluator,
+            with_knowledge=self.with_knowledge,
+            with_feedback=self.with_feedback,
+            knowledge_self_gen=self.knowledge_self_gen,
+        )
+
+        # save new knowledge base
+        if self.new_knowledge_base_path is not None:
+            pickle.dump(model_knowledge_base, open(self.new_knowledge_base_path, "wb"))
+        self.knowledge_base = model_knowledge_base
+        return model_experiment