SkillOpt v0.1.0: initial release
- 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
This commit is contained in:
CharlesYang030
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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 concurrent.futures import FIRST_COMPLETED, ThreadPoolExecutor, wait
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from skillopt.envs.docvqa.evaluator import evaluate
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from skillopt.model import chat_student_messages, get_student_backend, is_student_exec_backend
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from skillopt.model.codex_harness import prepare_workspace, render_skill_md, run_student_exec
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from skillopt.prompts import load_prompt
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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="docvqa").format(skill_section=skill_section)
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def _image_to_data_uri(path: str) -> str:
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import base64
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import mimetypes
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mime = mimetypes.guess_type(path)[0] or "image/png"
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with open(path, "rb") as f:
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encoded = base64.b64encode(f.read()).decode("ascii")
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return f"data:{mime};base64,{encoded}"
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def _build_messages(
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item: dict,
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skill_content: str,
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image_detail: str,
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*,
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diagnostic_mode: bool = False,
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diagnostic_instruction: str = "",
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) -> tuple[list[dict], str, str]:
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system = _build_system(skill_content)
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user_text = item["question"] + "\n\nReturn the final answer inside <answer>...</answer>."
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if diagnostic_mode and diagnostic_instruction.strip():
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user_text += f"\n\n## Training Readout\n{diagnostic_instruction.strip()}"
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image_url = {"url": _image_to_data_uri(item["image_path"])}
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if image_detail and image_detail != "auto":
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image_url["detail"] = image_detail
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messages = [
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{"role": "system", "content": system},
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{
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"role": "user",
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"content": [
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{"type": "text", "text": user_text},
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{"type": "image_url", "image_url": image_url},
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],
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},
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]
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return messages, system, user_text
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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 DocVQA document-image question.",
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preamble=(
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"Use this skill when answering the current DocVQA question.\n"
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"Inspect the attached document image carefully 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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item: dict,
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skill_content: str,
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model: str,
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timeout: int,
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image_detail: str,
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diagnostic_mode: bool = False,
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diagnostic_instruction: str = "",
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previous_response: str = "",
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) -> tuple[str, str, str, str]:
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_ = image_detail
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_messages, _system, user_text = _build_messages(
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item,
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skill_content,
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image_detail,
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diagnostic_mode=diagnostic_mode,
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diagnostic_instruction=diagnostic_instruction,
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)
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task_parts = [user_text]
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image_abs = os.path.abspath(item["image_path"])
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task_parts.append(
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"## Document Image\n"
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"The document image is available in this workspace via `ATTACHMENTS.md`.\n"
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f"Original image path: `{image_abs}`\n"
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"Open or inspect that image before answering; do not answer from memory."
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)
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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 the same document image carefully and correct the answer if needed."
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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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images=[item["image_path"]],
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)
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prompt = (
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"Use the `skillopt-student` skill available in this workspace.\n"
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"Read `task.md`, inspect the attached document image, and answer the DocVQA 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_student_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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images=[item["image_path"]],
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)
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return final_message or raw, raw, skill_md, task_text
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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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*,
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max_turns: int = 1,
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exec_timeout: int = 120,
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image_detail: str = "auto",
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diagnostic_mode: bool = False,
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diagnostic_instruction: str = "",
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) -> dict:
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item_id = str(item["id"])
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result = {
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"id": item_id,
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"question": item["question"],
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"task_type": item.get("subtask") or item.get("task_type") or "docvqa",
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"task_description": item["question"],
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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": "",
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"agent_ok": False,
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"n_turns": 0,
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"image_paths": item.get("image_paths", []),
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"gold_answer": item.get("answers", []),
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}
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try:
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response = ""
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system_prompt = ""
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user_text = ""
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conversation: list[dict] = []
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if is_student_exec_backend():
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from skillopt.model import azure_openai as _llm
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conversation = [
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{
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"role": "user",
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"content": item["question"] + "\n\n" + f"[image] {os.path.basename(item['image_path'])}",
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}
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]
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for turn in range(max_turns):
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response, _raw, system_prompt, user_text = _run_codex_once(
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pred_dir=os.path.join(out_root, "predictions", item_id),
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item=item,
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skill_content=skill_content,
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model=_llm.STUDENT_DEPLOYMENT,
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timeout=exec_timeout,
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image_detail=image_detail,
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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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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 "<answer>" in response.lower():
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break
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else:
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messages, system_prompt, user_text = _build_messages(
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item,
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skill_content,
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image_detail,
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diagnostic_mode=diagnostic_mode,
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diagnostic_instruction=diagnostic_instruction,
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)
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conversation = [
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{
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"role": "user",
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"content": user_text + "\n\n" + f"[image] {os.path.basename(item['image_path'])}",
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}
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]
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for turn in range(max_turns):
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if turn == 0:
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resp_text, _ = chat_student_messages(
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messages=messages,
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max_completion_tokens=768,
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retries=5,
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stage="rollout",
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timeout=exec_timeout,
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)
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else:
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refinement_messages = [
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messages[0],
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messages[1],
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{"role": "assistant", "content": response},
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{"role": "user", "content": "Review the same image carefully and answer again. Keep the final answer inside <answer>...</answer>."},
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]
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resp_text, _ = chat_student_messages(
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messages=refinement_messages,
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max_completion_tokens=512,
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retries=5,
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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 "<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) - 1
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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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with open(os.path.join(pred_dir, "student_system_prompt.txt"), "w", encoding="utf-8") as f:
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f.write(system_prompt)
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with open(os.path.join(pred_dir, "student_user_prompt.txt"), "w", encoding="utf-8") as f:
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f.write(user_text)
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eval_result = evaluate(response, item.get("answers", []))
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result["predicted_answer"] = eval_result["predicted_answer"]
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result["hard"] = int(eval_result["anls"] >= 0.999)
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result["soft"] = eval_result["anls"]
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if result["soft"] <= 0.0:
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result["fail_reason"] = f"predicted '{eval_result['predicted_answer']}' but expected one of {item.get('answers', [])}"
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eval_detail = (
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"[EVALUATION RESULT]\n"
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f"Question: {item['question']}\n"
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f"Predicted answer: {eval_result['predicted_answer']!r}\n"
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f"Gold answers: {item.get('answers', [])!r}\n"
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f"ANLS: {eval_result['anls']:.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", encoding="utf-8") 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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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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*,
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max_turns: int = 1,
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exec_timeout: int = 120,
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workers: int = 16,
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image_detail: str = "auto",
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diagnostic_mode: bool = False,
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diagnostic_instruction: str = "",
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task_timeout: int = 600,
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) -> list[dict]:
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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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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, encoding="utf-8") as f:
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for line in f:
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try:
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row = json.loads(line)
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except Exception:
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continue
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done_ids.add(str(row["id"]))
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existing.append(row)
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pending = [item for item in items if str(item["id"]) not in done_ids]
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if not pending:
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return 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_type": item.get("subtask") or item.get("task_type") or "docvqa",
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"task_description": item.get("question", ""),
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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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"image_paths": item.get("image_paths", []),
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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=max_turns,
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exec_timeout=exec_timeout,
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image_detail=image_detail,
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diagnostic_mode=diagnostic_mode,
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diagnostic_instruction=diagnostic_instruction,
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)
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total = len(existing) + len(pending)
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completed = len(existing)
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correct = 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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with open(results_path, "a", encoding="utf-8") 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, item): item for item 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 += 1
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acc = correct / 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 / 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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return results
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