CharlesYang030
244e346b83
- Skill optimization framework with training loop analogy - 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen) - WebUI for browser-based training control - Pluggable architecture for extending benchmarks and backends
283 lines
11 KiB
Python
283 lines
11 KiB
Python
"""LiveMathematicianBench environment adapter for ReflACT."""
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from __future__ import annotations
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import json
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import os
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from skillopt.gradient.deep_probe import generate_deep_probe_instruction
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from skillopt.datasets.base import BatchSpec
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from skillopt.gradient.reflect import run_minibatch_reflect
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from skillopt.envs.base import EnvAdapter
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from skillopt.envs.livemathematicianbench.dataloader import LiveMathematicianBenchDataLoader
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from skillopt.envs.livemathematicianbench.rollout import run_batch
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from skillopt.model import get_student_backend
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class LiveMathematicianBenchAdapter(EnvAdapter):
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"""LiveMathematicianBench adapter."""
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def build_reference_text(self, item: dict) -> str:
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parts: list[str] = []
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theorem = str(item.get("theorem") or "").strip()
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sketch = str(item.get("sketch") or "").strip()
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if theorem:
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parts.append(f"## Reference Theorem\n{theorem}")
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if sketch:
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parts.append(f"## Reference Sketch\n{sketch}")
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return "\n\n".join(parts)
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def get_reference_metadata(self, item: dict) -> dict:
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fields: list[str] = []
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previews: list[str] = []
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theorem = str(item.get("theorem") or "").strip()
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sketch = str(item.get("sketch") or "").strip()
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if theorem:
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fields.append("theorem")
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previews.append(f"[theorem]\n{theorem[:220]}")
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if sketch:
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fields.append("sketch")
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previews.append(f"[sketch]\n{sketch[:220]}")
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return {
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"fields": fields,
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"preview": "\n\n".join(previews)[:500],
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}
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def __init__(
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self,
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split_dir: str = "",
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data_path: str = "",
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split_mode: str = "ratio",
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split_ratio: str = "2:1:7",
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split_seed: int = 42,
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split_output_dir: str = "",
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max_turns: int = 1,
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exec_timeout: int = 600,
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workers: int = 64,
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analyst_workers: int = 16,
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failure_only: bool = False,
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minibatch_size: int = 8,
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edit_budget: int = 4,
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seed: int = 42,
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limit: int = 0,
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shuffle_choices: bool = True,
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use_theorem: bool = False,
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use_sketch: bool = False,
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use_deep_reflect: bool = False,
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deep_reflect_failures: int = 4,
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deep_reflect_successes: int = 2,
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) -> None:
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self.max_turns = max_turns
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self.exec_timeout = exec_timeout
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self.workers = workers
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self.analyst_workers = analyst_workers
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self.failure_only = failure_only
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self.minibatch_size = minibatch_size
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self.edit_budget = edit_budget
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self.use_theorem = use_theorem
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self.use_sketch = use_sketch
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self.use_deep_reflect = use_deep_reflect
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self.deep_reflect_failures = deep_reflect_failures
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self.deep_reflect_successes = deep_reflect_successes
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self.dataloader = LiveMathematicianBenchDataLoader(
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split_dir=split_dir,
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data_path=data_path,
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split_mode=split_mode,
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split_ratio=split_ratio,
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split_seed=split_seed,
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split_output_dir=split_output_dir,
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seed=seed,
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limit=limit,
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shuffle_choices=shuffle_choices,
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)
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def setup(self, cfg: dict) -> None:
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super().setup(cfg)
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self.dataloader.setup(cfg)
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def get_dataloader(self):
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return self.dataloader
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def build_env_from_batch(self, batch: BatchSpec, **kwargs):
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return list(batch.payload or [])
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def build_train_env(self, batch_size: int, seed: int, **kwargs):
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batch = self.dataloader.build_train_batch(batch_size=batch_size, seed=seed, **kwargs)
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return self.build_env_from_batch(batch, **kwargs)
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def build_eval_env(self, env_num: int, split: str, seed: int, **kwargs):
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batch = self.dataloader.build_eval_batch(env_num=env_num, split=split, seed=seed, **kwargs)
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return self.build_env_from_batch(batch, **kwargs)
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def rollout(
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self,
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env_manager,
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skill_content: str,
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out_dir: str,
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**kwargs,
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) -> list[dict]:
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items: list[dict] = env_manager
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return run_batch(
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items=items,
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out_root=out_dir,
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skill_content=skill_content,
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max_turns=self.max_turns,
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exec_timeout=self.exec_timeout,
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workers=self.workers,
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use_theorem=self.use_theorem,
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use_sketch=self.use_sketch,
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diagnostic_mode=kwargs.get("diagnostic_mode", False),
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diagnostic_instruction=kwargs.get("diagnostic_instruction", ""),
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diagnostic_trace_context_by_id=kwargs.get("diagnostic_trace_context_by_id"),
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task_timeout=self.exec_timeout,
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)
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def reflect(
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self,
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results: list[dict],
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skill_content: str,
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out_dir: str,
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**kwargs,
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) -> list[dict | None]:
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prediction_dir = kwargs.get("prediction_dir", os.path.join(out_dir, "predictions"))
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patches_dir = kwargs.get("patches_dir", os.path.join(out_dir, "patches"))
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random_seed = kwargs.get("random_seed")
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step_buffer_context = kwargs.get("step_buffer_context", "")
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meta_skill_context = kwargs.get("meta_skill_context", "")
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return run_minibatch_reflect(
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results=results,
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skill_content=skill_content,
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prediction_dir=prediction_dir,
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patches_dir=patches_dir,
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workers=self.analyst_workers,
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failure_only=self.failure_only,
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minibatch_size=self.minibatch_size,
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edit_budget=self.edit_budget,
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random_seed=random_seed,
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error_system=self.get_error_minibatch_prompt(),
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success_system=self.get_success_minibatch_prompt(),
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step_buffer_context=step_buffer_context,
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meta_skill_context=meta_skill_context,
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update_mode=getattr(self, "_cfg", {}).get("skill_update_mode", "patch"),
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)
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def deep_reflect(
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self,
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results: list[dict],
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skill_content: str,
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out_dir: str,
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**kwargs,
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) -> list[dict | None]:
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if not self.use_deep_reflect:
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return []
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env_manager = kwargs.get("env_manager")
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prediction_dir = kwargs.get("prediction_dir", os.path.join(out_dir, "predictions"))
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random_seed = kwargs.get("random_seed")
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step_buffer_context = kwargs.get("step_buffer_context", "")
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meta_skill_context = kwargs.get("meta_skill_context", "")
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codex_backend = get_student_backend() == "codex_exec"
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selected_items = self.select_representative_items(
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results,
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env_manager if isinstance(env_manager, list) else None,
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n_failures=self.deep_reflect_failures,
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n_successes=self.deep_reflect_successes,
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seed=random_seed,
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)
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if not selected_items:
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return []
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selected_ids = {str(item["id"]) for item in selected_items}
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selected_results = [row for row in results if str(row.get("id")) in selected_ids]
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selected_examples = self.attach_reference_context(selected_results, selected_items)
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if codex_backend:
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selected_examples = self.attach_codex_probe_context(selected_examples, prediction_dir)
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selected_metadata = []
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theorem_count = 0
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sketch_count = 0
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for item in selected_items:
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meta = self.get_reference_metadata(item)
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if "theorem" in meta["fields"]:
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theorem_count += 1
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if "sketch" in meta["fields"]:
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sketch_count += 1
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selected_metadata.append({
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"id": str(item["id"]),
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"task_type": str(item.get("theorem_type", ["math_mcq"])[0] if item.get("theorem_type") else "math_mcq"),
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"reference_fields": meta["fields"],
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"reference_preview": meta["preview"],
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})
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deep_dir = os.path.join(out_dir, "deep_reflect")
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rollout_dir = os.path.join(deep_dir, "rollout")
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patches_dir = os.path.join(deep_dir, "patches")
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os.makedirs(deep_dir, exist_ok=True)
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print(
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f" [2b/6 DEEP REFLECT setup] selected={len(selected_items)} "
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f"reference_fields=theorem({theorem_count}/{len(selected_items)}),"
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f"sketch({sketch_count}/{len(selected_items)})"
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)
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probe = generate_deep_probe_instruction(
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skill_content=skill_content,
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items=selected_examples,
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prediction_dir=prediction_dir,
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system_prompt=self.get_codex_deep_probe_prompt() if codex_backend else self.get_deep_probe_prompt(),
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step_buffer_context=step_buffer_context,
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meta_skill_context=meta_skill_context,
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)
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if not probe:
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return []
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diagnostic_trace_context_by_id = None
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if codex_backend:
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selected_items, diagnostic_trace_context_by_id, probe = self.resolve_codex_probe_target(
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selected_items=selected_items,
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selected_examples=selected_examples,
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prediction_dir=prediction_dir,
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probe=probe,
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)
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probe_record = {
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**probe,
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"reference_summary": {
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"selected_count": len(selected_items),
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"field_counts": {
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"theorem": theorem_count,
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"sketch": sketch_count,
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},
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},
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"selected_examples": selected_metadata,
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}
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with open(os.path.join(deep_dir, "probe.json"), "w", encoding="utf-8") as f:
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json.dump(probe_record, f, ensure_ascii=False, indent=2)
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deep_results = run_batch(
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items=selected_items,
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out_root=rollout_dir,
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skill_content=skill_content,
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max_turns=self.max_turns,
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workers=min(self.workers, max(len(selected_items), 1)),
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use_theorem=self.use_theorem,
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use_sketch=self.use_sketch,
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diagnostic_mode=True,
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diagnostic_instruction=probe["probe_instruction"],
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diagnostic_trace_context_by_id=diagnostic_trace_context_by_id,
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task_timeout=self.exec_timeout,
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)
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deep_results = self.attach_reference_context(deep_results, selected_items)
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return run_minibatch_reflect(
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results=deep_results,
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skill_content=skill_content,
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prediction_dir=os.path.join(rollout_dir, "predictions"),
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patches_dir=patches_dir,
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workers=self.analyst_workers,
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failure_only=self.failure_only,
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minibatch_size=self.minibatch_size,
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edit_budget=self.edit_budget,
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random_seed=random_seed,
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error_system=self.get_error_minibatch_prompt(),
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success_system=self.get_success_minibatch_prompt(),
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step_buffer_context=step_buffer_context,
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meta_skill_context=meta_skill_context,
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update_mode=getattr(self, "_cfg", {}).get("skill_update_mode", "patch"),
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)
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def get_task_types(self) -> list[str]:
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return self.dataloader.get_task_types()
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