Cuzyoung
cff7ff6846
- Fix teacher/student in deep_reflect, meta_reflect, sealqa, babyvision, mathverse, mmrb, swebench envs and prompt templates - Remove .gradio/certificate.pem from tracked files - Add .gradio/ to .gitignore Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
484 lines
19 KiB
Python
484 lines
19 KiB
Python
"""BabyVision rollout — multimodal visual QA with image input."""
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from __future__ import annotations
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import base64
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import json
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import mimetypes
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import os
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from skillopt.envs.babyvision.evaluator import evaluate_item, evaluation_mode, extract_boxed_answer
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from skillopt.model import chat_target_messages, get_target_backend, 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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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="babyvision").format(skill_section=skill_section)
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def _format_choices(choices: list[dict]) -> str:
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return "\n".join(f"{choice['label']}. {choice['text']}" for choice in choices)
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def _build_user_text(
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item: dict,
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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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parts = []
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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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parts.append(f"## Question\n{item['question']}")
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if item["ans_type"] == "choice":
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parts.append(f"## Choices\n{_format_choices(item['choices'])}")
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parts.append("Answer using the single correct option label in \\boxed{...}.")
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else:
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parts.append("Answer with a short phrase in \\boxed{...}.")
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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 _image_to_data_uri(path: str) -> str:
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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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diagnostic_trace_context: 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 = _build_user_text(
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item,
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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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image_url = {
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"url": _image_to_data_uri(item["image_path"]),
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}
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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 BabyVision visual reasoning question.",
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preamble=(
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"Use this skill when answering the current visual reasoning question.\n"
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"Inspect the attached image carefully and return the final answer in \\boxed{...}."
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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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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_text = _build_user_text(
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item,
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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_text]
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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 image and question carefully. If needed, correct the answer."
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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-target` skill available in this workspace.\n"
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"Read `task.md`, inspect the attached image, and answer the question.\n"
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"Return the final answer in \\boxed{...}."
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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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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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image_detail: str = "auto",
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judge_model: str = "gpt-5.4",
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judge_max_completion_tokens: int = 256,
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judge_retries: int = 5,
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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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) -> 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("subtype") or item.get("task_type") or "babyvision",
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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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"predicted_label": "",
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"predicted_text": "",
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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_path": item["image_path"],
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"ans_type": item["ans_type"],
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"evaluation_mode": evaluation_mode(),
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"judge_model": judge_model,
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}
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if item["ans_type"] == "choice":
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result["correct_label"] = item["correct_choice"]["label"]
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result["correct_text"] = item["correct_choice"]["text"]
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else:
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result["gold_answers"] = item["blank_answers"]
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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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response = ""
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conversation: list[dict] = [
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{"role": "user", "content": f"{item['question']}\n\n[image] {os.path.basename(item['image_path'])}"}
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]
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system_prompt = ""
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user_text = ""
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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=pred_dir,
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item=item,
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skill_content=skill_content,
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model=_llm.TARGET_DEPLOYMENT,
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timeout=120,
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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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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 extract_boxed_answer(response) is not None:
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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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with open(os.path.join(pred_dir, "target_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, "target_user_prompt.txt"), "w", encoding="utf-8") as f:
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f.write(user_text)
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eval_result = evaluate_item(
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item=item,
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prediction_text=response,
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judge_model=judge_model,
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max_completion_tokens=judge_max_completion_tokens,
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retries=judge_retries,
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)
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result["evaluation_mode"] = eval_result["evaluation_mode"]
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result["judge_raw"] = eval_result["judge_raw"]
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result["judge_reason"] = eval_result["judge_reason"]
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result["matched_gold"] = eval_result["matched_gold"]
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if item["ans_type"] == "choice":
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result["predicted_label"] = eval_result["predicted_label"]
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result["predicted_text"] = eval_result["predicted_text"]
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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 not result["hard"]:
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result["fail_reason"] = (
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f"judge=0: predicted '{eval_result['predicted_label'] or eval_result['predicted_answer']}' "
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f"but expected '{eval_result['correct_label']}' ({eval_result['judge_reason']})"
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)
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eval_detail = (
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f"[EVALUATION RESULT]\n"
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f"Question: {item['question']}\n"
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f"Predicted label: {eval_result['predicted_label']!r}\n"
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f"Predicted text: {eval_result['predicted_text']!r}\n"
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f"Correct label: {eval_result['correct_label']!r}\n"
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f"Correct text: {eval_result['correct_text']!r}\n"
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f"Judge correct: {eval_result['em']}\n"
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f"Judge reason: {eval_result['judge_reason']}"
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)
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else:
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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 not result["hard"]:
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result["fail_reason"] = (
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f"judge=0: predicted '{eval_result['predicted_answer']}' "
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f"but expected {item['blank_answers']} ({eval_result['judge_reason']})"
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)
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eval_detail = (
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f"[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['blank_answers']!r}\n"
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f"Judge correct: {eval_result['em']}\n"
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f"Judge reason: {eval_result['judge_reason']}\n"
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f"String F1: {eval_result.get('string_f1', 0.0):.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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return result
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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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diagnostic_trace_context=diagnostic_trace_context,
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)
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response = ""
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conversation: list[dict] = [
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{"role": "user", "content": f"{user_text}\n\n[image] {os.path.basename(item['image_path'])}"}
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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_target_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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)
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else:
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refinement_text = (
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f"Your previous answer was:\n{response}\n\n"
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"Review the same image and question carefully. "
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"If needed, correct your answer. Output the final answer in \\boxed{...}."
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)
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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": refinement_text},
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]
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resp_text, _ = chat_target_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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)
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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 extract_boxed_answer(resp_text) is not None:
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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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with open(os.path.join(pred_dir, "target_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, "target_user_prompt.txt"), "w", encoding="utf-8") as f:
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f.write(user_text)
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eval_result = evaluate_item(
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item=item,
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prediction_text=response,
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judge_model=judge_model,
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max_completion_tokens=judge_max_completion_tokens,
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retries=judge_retries,
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)
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result["evaluation_mode"] = eval_result["evaluation_mode"]
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result["judge_raw"] = eval_result["judge_raw"]
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result["judge_reason"] = eval_result["judge_reason"]
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result["matched_gold"] = eval_result["matched_gold"]
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if item["ans_type"] == "choice":
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result["predicted_label"] = eval_result["predicted_label"]
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result["predicted_text"] = eval_result["predicted_text"]
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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 not result["hard"]:
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result["fail_reason"] = (
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f"judge=0: predicted '{eval_result['predicted_label'] or eval_result['predicted_answer']}' "
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f"but expected '{eval_result['correct_label']}' ({eval_result['judge_reason']})"
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)
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eval_detail = (
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f"[EVALUATION RESULT]\n"
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f"Question: {item['question']}\n"
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f"Predicted label: {eval_result['predicted_label']!r}\n"
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f"Predicted text: {eval_result['predicted_text']!r}\n"
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f"Correct label: {eval_result['correct_label']!r}\n"
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f"Correct text: {eval_result['correct_text']!r}\n"
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f"Judge correct: {eval_result['em']}\n"
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f"Judge reason: {eval_result['judge_reason']}"
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)
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else:
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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 not result["hard"]:
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result["fail_reason"] = (
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f"judge=0: predicted '{eval_result['predicted_answer']}' "
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f"but expected {item['blank_answers']} ({eval_result['judge_reason']})"
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)
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eval_detail = (
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f"[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['blank_answers']!r}\n"
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f"Judge correct: {eval_result['em']}\n"
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f"Judge reason: {eval_result['judge_reason']}\n"
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f"String F1: {eval_result.get('string_f1', 0.0):.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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workers: int = 32,
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image_detail: str = "auto",
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judge_model: str = "gpt-5.4",
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judge_max_completion_tokens: int = 256,
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judge_retries: int = 5,
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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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) -> list[dict]:
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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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expected_eval_mode = evaluation_mode()
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done_ids: set[str] = set()
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existing: list[dict] = []
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rewrite_results = False
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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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if row.get("evaluation_mode") != expected_eval_mode:
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rewrite_results = True
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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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except Exception:
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rewrite_results = True
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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 and not rewrite_results:
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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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file_mode = "w" if rewrite_results else "a"
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with open(results_path, file_mode, encoding="utf-8") as outf, ThreadPoolExecutor(max_workers=workers) as ex:
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if rewrite_results:
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for row in existing:
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outf.write(json.dumps(row, ensure_ascii=False) + "\n")
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futs = {
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ex.submit(
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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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image_detail=image_detail,
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judge_model=judge_model,
|
|
judge_max_completion_tokens=judge_max_completion_tokens,
|
|
judge_retries=judge_retries,
|
|
diagnostic_mode=diagnostic_mode,
|
|
diagnostic_instruction=diagnostic_instruction,
|
|
diagnostic_trace_context=(diagnostic_trace_context_by_id or {}).get(str(item["id"]), ""),
|
|
): item
|
|
for item in pending
|
|
}
|
|
for fut in as_completed(futs):
|
|
row = fut.result()
|
|
results.append(row)
|
|
completed += 1
|
|
if row.get("hard", 0):
|
|
correct_count += 1
|
|
acc = correct_count / completed if completed else 0
|
|
print(
|
|
f" [rollout] {completed}/{total} "
|
|
f"(acc={acc:.3f}) id={row.get('id', '?')} "
|
|
f"hard={row.get('hard', '?')}",
|
|
flush=True,
|
|
)
|
|
outf.write(json.dumps(row, ensure_ascii=False) + "\n")
|
|
outf.flush()
|
|
return results
|