Cuzyoung
115 lines
3.8 KiB
Python
115 lines
3.8 KiB
Python
from __future__ import annotations
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import json
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import os
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from typing import Any, Callable
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from reflact.gradient.deep_probe import generate_deep_probe_instruction
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from reflact.gradient.reflect import run_minibatch_reflect
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def run_no_reference_deep_reflect(
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adapter: Any,
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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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*,
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env_manager: Any = None,
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prediction_dir: str | None = None,
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random_seed: int | None = None,
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step_buffer_context: str = "",
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output_requirements: list[str] | None = None,
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metadata_builder: Callable[[dict], dict] | None = None,
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) -> list[dict | None]:
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"""Run teacher-designed diagnostic probing without hidden references."""
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if not getattr(adapter, "use_deep_reflect", False):
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return []
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if not isinstance(env_manager, list):
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return []
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prediction_dir = prediction_dir or os.path.join(out_dir, "predictions")
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selected_items = adapter.select_representative_items(
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results,
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env_manager,
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n_failures=getattr(adapter, "deep_reflect_failures", 4),
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n_successes=getattr(adapter, "deep_reflect_successes", 2),
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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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if metadata_builder is None:
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selected_metadata = [
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{
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"id": str(item.get("id")),
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"task_type": str(item.get("task_type") or item.get("topic") or "unknown"),
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"question_preview": str(item.get("question") or "")[:200],
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}
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for item in selected_items
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]
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else:
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selected_metadata = [metadata_builder(item) for item in selected_items]
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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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"mode=no_reference_probe"
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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_results,
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prediction_dir=prediction_dir,
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system_prompt=adapter.get_deep_probe_prompt(),
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step_buffer_context=step_buffer_context,
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output_requirements=output_requirements,
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)
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if not probe:
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return []
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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(
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{
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**probe,
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"reference_summary": {
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"mode": "no_reference_probe",
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"selected_count": len(selected_items),
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},
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"selected_examples": selected_metadata,
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},
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f,
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ensure_ascii=False,
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indent=2,
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)
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deep_results = adapter.rollout(
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selected_items,
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skill_content,
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rollout_dir,
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diagnostic_mode=True,
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diagnostic_instruction=probe["probe_instruction"],
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)
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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=getattr(adapter, "analyst_workers", 8),
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failure_only=getattr(adapter, "failure_only", False),
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minibatch_size=getattr(adapter, "minibatch_size", 8),
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edit_budget=getattr(adapter, "edit_budget", 4),
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random_seed=random_seed,
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error_system=adapter.get_error_minibatch_prompt(),
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success_system=adapter.get_success_minibatch_prompt(),
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step_buffer_context=step_buffer_context,
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update_mode=getattr(getattr(adapter, "_cfg", {}), "get", lambda *_: "patch")(
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"skill_update_mode",
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"patch",
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),
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)
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