wehub-resource-sync
495 lines
18 KiB
Python
495 lines
18 KiB
Python
"""SearchQA rollout — single-turn QA agent + batch execution.
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The QA agent receives a skill document, question, and context passages,
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then produces an answer in <answer>...</answer> tags.
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Public API
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----------
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- :func:`process_one` — run + evaluate one QA item
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- :func:`run_batch` — parallel execution of a list of items
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"""
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from __future__ import annotations
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import json
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import os
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import time
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from collections import Counter
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from concurrent.futures import FIRST_COMPLETED, ThreadPoolExecutor, wait
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from skillopt.envs.searchqa.evaluator import evaluate
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from skillopt.model import chat_target, is_target_exec_backend
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from skillopt.model.codex_harness import prepare_workspace, render_skill_md, run_target_exec
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from skillopt.prompts import load_prompt
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# ── Prompt templates ─────────────────────────────────────────────────────────
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_MAX_CONTEXT_CHARS = 6000
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def _raise_on_systemic_failure(results: list[dict]) -> None:
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"""Abort when all rollout rows failed before any agent response."""
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if not results or not all(row.get("agent_ok") is False for row in results):
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return
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reasons = Counter(str(row.get("fail_reason") or "unknown error") for row in results)
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common_reason, count = reasons.most_common(1)[0]
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raise RuntimeError(
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f"SearchQA rollout failed for all {len(results)} items before an agent "
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f"response ({count}x): {common_reason}"
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)
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def _truncate_context(context: str, max_chars: int = _MAX_CONTEXT_CHARS) -> str:
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"""Truncate context at [DOC] boundaries to stay within budget."""
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if len(context) <= max_chars:
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return context
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docs = context.split("[DOC]")
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result = ""
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for doc in docs:
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candidate = result + "[DOC]" + doc if result else doc
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if len(candidate) > max_chars:
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break
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result = candidate
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if not result:
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result = context[:max_chars] + "\n...[truncated]"
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return result
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def _build_system(skill_content: str) -> str:
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if skill_content.strip():
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skill_section = f"## Skill\n{skill_content.strip()}\n\n"
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else:
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skill_section = ""
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return load_prompt("rollout_system", env="searchqa").format(skill_section=skill_section)
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def _build_user(
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question: str,
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context: str,
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*,
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diagnostic_mode: bool = False,
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diagnostic_instruction: str = "",
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diagnostic_trace_context: str = "",
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) -> str:
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context = _truncate_context(context)
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parts = [
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f"## Context\n{context}",
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f"## Question\n{question}",
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]
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if diagnostic_trace_context.strip():
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parts.append(
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"## Previous Codex Trace Snapshot\n"
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"This is a partial transcript from an earlier attempt. Use it as your current reasoning context.\n\n"
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f"{diagnostic_trace_context.strip()}"
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)
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if diagnostic_mode and diagnostic_instruction.strip():
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parts.append(f"## Training Readout\n{diagnostic_instruction.strip()}")
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return "\n\n".join(parts)
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def _build_codex_skill(skill_content: str) -> str:
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return render_skill_md(
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skill_content,
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description="Dynamic ReflACT skill for solving the current SearchQA example.",
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preamble=(
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"Use this skill when solving the current SearchQA task.\n"
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"Read the provided context carefully, ground the answer in that context,\n"
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"and return the final answer inside <answer>...</answer>."
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),
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)
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def _run_codex_once(
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*,
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pred_dir: str,
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skill_content: str,
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question: str,
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context: str,
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model: str,
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timeout: int,
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diagnostic_mode: bool = False,
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diagnostic_instruction: str = "",
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diagnostic_trace_context: str = "",
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previous_response: str = "",
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) -> tuple[str, str, str, str]:
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user = _build_user(
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question,
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context,
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diagnostic_mode=diagnostic_mode,
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diagnostic_instruction=diagnostic_instruction,
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diagnostic_trace_context=diagnostic_trace_context,
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)
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task_parts = [user]
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if previous_response:
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task_parts.append(
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"## Previous Attempt\n"
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f"{previous_response}\n\n"
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"Review it against the same context and question. If needed, correct it."
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)
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task_text = "\n\n".join(task_parts)
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skill_md = _build_codex_skill(skill_content)
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work_dir = os.path.join(pred_dir, "codex_exec")
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prepare_workspace(
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work_dir=work_dir,
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skill_md=skill_md,
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task_text=task_text,
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)
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prompt = (
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"Use the `skillopt-target` skill available in this workspace.\n"
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"Read `task.md` and answer the SearchQA question.\n"
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"Return the final answer inside <answer>...</answer>."
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)
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final_message, raw = run_target_exec(
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work_dir=work_dir,
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prompt=prompt,
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model=model,
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timeout=timeout,
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)
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return final_message or raw, raw, skill_md, task_text
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# ── Single-item execution ───────────────────────────────────────────────────
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def process_one(
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item: dict,
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out_root: str,
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skill_content: str,
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max_turns: int = 1,
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diagnostic_mode: bool = False,
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diagnostic_instruction: str = "",
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diagnostic_trace_context: str = "",
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exec_timeout: int = 120,
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max_completion_tokens: int = 16384,
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) -> dict:
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"""Process a single QA item: run agent + evaluate.
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Parameters
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----------
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item : dict
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Must have keys: ``id``, ``question``, ``context``, ``answers``.
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out_root : str
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Output directory (predictions saved under ``predictions/<id>/``).
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skill_content : str
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Current skill document text.
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max_turns : int
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Max reasoning turns (1 = single-turn QA).
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Returns
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-------
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dict
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Result with ``hard`` (EM as int), ``soft`` (F1), etc.
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"""
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item_id = str(item["id"])
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question = item["question"]
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context = item.get("context", "")
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gold_answers = item.get("answers", [])
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result = {
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"id": item_id,
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"question": question,
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"em": 0.0,
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"f1": 0.0,
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"sub_em": 0.0,
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"hard": 0,
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"soft": 0.0,
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"predicted_answer": "",
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"gold_answers": gold_answers,
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"response": "",
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"fail_reason": "",
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"agent_ok": False,
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"n_turns": 0,
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}
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try:
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pred_dir = os.path.join(out_root, "predictions", item_id)
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os.makedirs(pred_dir, exist_ok=True)
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if is_target_exec_backend():
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from skillopt.model import azure_openai as _llm
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conversation: list[dict] = []
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response = ""
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system = ""
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user = ""
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for turn in range(max_turns):
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response, raw, system, user = _run_codex_once(
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pred_dir=pred_dir,
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skill_content=skill_content,
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question=question,
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context=context,
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model=_llm.TARGET_DEPLOYMENT,
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timeout=exec_timeout,
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diagnostic_mode=diagnostic_mode if turn == 0 else False,
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diagnostic_instruction=diagnostic_instruction if turn == 0 else "",
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diagnostic_trace_context=diagnostic_trace_context if turn == 0 else "",
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previous_response=response if turn > 0 else "",
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)
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conversation.append({"type": "message", "turn": turn + 1, "content": response})
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if turn > 0 and "<answer>" in response.lower():
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break
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result["response"] = response
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result["agent_ok"] = True
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result["n_turns"] = len(conversation)
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with open(os.path.join(pred_dir, "target_system_prompt.txt"), "w") as f:
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f.write(system)
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with open(os.path.join(pred_dir, "target_user_prompt.txt"), "w") as f:
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f.write(user)
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with open(os.path.join(pred_dir, "conversation.json"), "w") as f:
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json.dump(conversation, f, ensure_ascii=False, indent=2)
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eval_result = evaluate(response, gold_answers)
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result["em"] = eval_result["em"]
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result["f1"] = eval_result["f1"]
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result["sub_em"] = eval_result["sub_em"]
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result["predicted_answer"] = eval_result["predicted_answer"]
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result["hard"] = int(eval_result["em"])
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result["soft"] = eval_result["f1"]
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if eval_result["em"] < 1.0:
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result["fail_reason"] = (
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f"EM=0: predicted '{eval_result['predicted_answer']}' "
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f"but expected {gold_answers}"
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)
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eval_detail = (
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f"[EVALUATION RESULT]\n"
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f"Question: {question}\n"
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f"Predicted answer: {eval_result['predicted_answer']!r}\n"
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f"Gold answers: {gold_answers!r}\n"
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f"Exact Match: {eval_result['em']}\n"
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f"F1: {eval_result['f1']:.4f}"
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)
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conversation.append({"role": "system", "content": eval_detail})
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with open(os.path.join(pred_dir, "conversation.json"), "w") as f:
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json.dump(conversation, f, ensure_ascii=False, indent=2)
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return result
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system = _build_system(skill_content)
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user = _build_user(
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question,
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context,
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diagnostic_mode=diagnostic_mode,
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diagnostic_instruction=diagnostic_instruction,
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diagnostic_trace_context=diagnostic_trace_context,
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)
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conversation: list[dict] = []
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response = ""
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for turn in range(max_turns):
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if turn == 0:
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resp_text, _ = chat_target(
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system=system, user=user,
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max_completion_tokens=max_completion_tokens,
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retries=5, stage="rollout",
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timeout=exec_timeout,
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)
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else:
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refinement = (
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f"Your previous answer was:\n{response}\n\n"
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f"Review it against the context and question. "
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f"If correct, repeat it. If wrong, provide a corrected answer.\n"
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f"Use <answer>...</answer> tags for your final answer."
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)
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resp_text, _ = chat_target(
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system=system, user=refinement,
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max_completion_tokens=max_completion_tokens,
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retries=5, stage="rollout",
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timeout=exec_timeout,
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)
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response = resp_text
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conversation.append({"type": "message", "turn": turn + 1, "content": resp_text})
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if turn > 0 and "<answer>" in resp_text.lower():
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break
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result["response"] = response
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result["agent_ok"] = True
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result["n_turns"] = len(conversation)
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# Save conversation
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with open(os.path.join(pred_dir, "target_system_prompt.txt"), "w") as f:
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f.write(system)
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with open(os.path.join(pred_dir, "target_user_prompt.txt"), "w") as f:
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f.write(user)
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with open(os.path.join(pred_dir, "conversation.json"), "w") as f:
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json.dump(conversation, f, ensure_ascii=False, indent=2)
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# Evaluate
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eval_result = evaluate(response, gold_answers)
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result["em"] = eval_result["em"]
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result["f1"] = eval_result["f1"]
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result["sub_em"] = eval_result["sub_em"]
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result["predicted_answer"] = eval_result["predicted_answer"]
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result["hard"] = int(eval_result["em"])
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result["soft"] = eval_result["f1"]
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if eval_result["em"] < 1.0:
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result["fail_reason"] = (
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f"EM=0: predicted '{eval_result['predicted_answer']}' "
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f"but expected {gold_answers}"
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)
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# Append eval details to conversation for the analyst
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eval_detail = (
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f"[EVALUATION RESULT]\n"
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f"Question: {question}\n"
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f"Predicted answer: {eval_result['predicted_answer']!r}\n"
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f"Gold answers: {gold_answers!r}\n"
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f"Exact Match: {eval_result['em']}\n"
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f"F1: {eval_result['f1']:.4f}"
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)
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conversation.append({
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"role": "system",
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"content": eval_detail,
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})
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# Re-save enriched conversation
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with open(os.path.join(pred_dir, "conversation.json"), "w") as f:
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json.dump(conversation, f, ensure_ascii=False, indent=2)
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except Exception as e: # noqa: BLE001
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result["fail_reason"] = f"error: {e}"
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return result
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# ── Batch execution ──────────────────────────────────────────────────────────
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def run_batch(
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items: list[dict],
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out_root: str,
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skill_content: str,
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max_turns: int = 1,
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exec_timeout: int = 120,
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workers: int = 64,
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max_completion_tokens: int = 16384,
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diagnostic_mode: bool = False,
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diagnostic_instruction: str = "",
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diagnostic_trace_context_by_id: dict[str, str] | None = None,
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task_timeout: int = 600,
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) -> list[dict]:
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"""Run QA agent on all items with ThreadPoolExecutor. Resume-aware."""
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task_timeout = max(int(task_timeout), int(exec_timeout) + 60)
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results_path = os.path.join(out_root, "results.jsonl")
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os.makedirs(out_root, exist_ok=True)
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# Resume: load already-done
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done_ids: set[str] = set()
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existing: list[dict] = []
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if os.path.exists(results_path):
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with open(results_path) as f:
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for line in f:
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try:
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r = json.loads(line)
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done_ids.add(str(r["id"]))
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existing.append(r)
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except Exception:
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pass
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pending = [it for it in items if str(it["id"]) not in done_ids]
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if not pending:
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_raise_on_systemic_failure(existing)
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return existing
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total = len(existing) + len(pending)
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completed = len(existing)
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correct_count = sum(1 for r in existing if r.get("hard", 0))
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if existing:
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print(f" [rollout] resuming: {completed}/{total} already done", flush=True)
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results = list(existing)
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def _timeout_result(item: dict) -> dict:
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return {
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"id": str(item["id"]),
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"question": item.get("question", ""),
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"task_description": item.get("question", ""),
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"task_type": item.get("task_type") or "searchqa",
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"hard": 0,
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"soft": 0.0,
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"predicted_answer": "",
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"response": "",
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"fail_reason": f"task-timeout-{task_timeout}s",
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"agent_ok": False,
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"n_turns": 0,
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"gold_answer": item.get("answers", []),
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"phase": "timeout",
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}
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def _error_result(item: dict, exc: Exception) -> dict:
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row = _timeout_result(item)
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row["phase"] = "error"
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row["fail_reason"] = f"unexpected: {type(exc).__name__}: {exc}"
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return row
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started_at: dict[str, float] = {}
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def _run_one(item: dict) -> dict:
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started_at[str(item["id"])] = time.time()
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return process_one(
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item,
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out_root,
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skill_content,
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max_turns,
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diagnostic_mode,
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diagnostic_instruction,
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(diagnostic_trace_context_by_id or {}).get(str(item["id"]), ""),
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exec_timeout,
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max_completion_tokens,
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)
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with open(results_path, "a") as outf:
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ex = ThreadPoolExecutor(max_workers=workers)
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try:
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futs = {ex.submit(_run_one, it): it for it in pending}
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pending_futs = set(futs)
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while pending_futs:
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done, _ = wait(pending_futs, timeout=5, return_when=FIRST_COMPLETED)
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now = time.time()
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timed_out = [
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fut for fut in pending_futs - done
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if str(futs[fut]["id"]) in started_at
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and now - started_at[str(futs[fut]["id"])] >= task_timeout
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]
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for fut in done:
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pending_futs.remove(fut)
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item = futs[fut]
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try:
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res = fut.result()
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except Exception as exc: # noqa: BLE001
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res = _error_result(item, exc)
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results.append(res)
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completed += 1
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if res.get("hard", 0):
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correct_count += 1
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acc = correct_count / completed if completed else 0
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print(
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f" [rollout] {completed}/{total} "
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f"(acc={acc:.3f}) id={res['id']} "
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f"hard={res.get('hard', '?')}",
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flush=True,
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)
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outf.write(json.dumps(res, ensure_ascii=False) + "\n")
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outf.flush()
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for fut in timed_out:
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pending_futs.remove(fut)
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fut.cancel()
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res = _timeout_result(futs[fut])
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results.append(res)
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completed += 1
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acc = correct_count / completed if completed else 0
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print(
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f" [rollout] {completed}/{total} "
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f"(acc={acc:.3f}) id={res['id']} TIMEOUT",
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flush=True,
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)
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outf.write(json.dumps(res, ensure_ascii=False) + "\n")
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outf.flush()
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finally:
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ex.shutdown(wait=False, cancel_futures=True)
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_raise_on_systemic_failure(results)
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return results
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