carpedkm
440 lines
16 KiB
Python
440 lines
16 KiB
Python
"""MMRB rollout."""
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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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import re
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from reflact.envs.mmrb.evaluator import evaluate_item, evaluation_mode
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from reflact.model import chat_student_messages, get_student_backend, is_student_exec_backend
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from reflact.model.codex_harness import prepare_workspace, render_skill_md, run_student_exec
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from reflact.prompts import load_prompt
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_IMAGE_REF_RE = re.compile(r"\{image#(\d+)\}", re.IGNORECASE)
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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="mmrb").format(skill_section=skill_section)
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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_user_content(
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item: dict,
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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]:
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raw_question = str(item["question"])
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content: list[dict] = []
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text_parts: list[str] = []
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used_indices: set[int] = set()
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cursor = 0
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if diagnostic_trace_context.strip():
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prefix = (
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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()}\n\n"
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)
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content.append({"type": "text", "text": prefix})
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text_parts.append(prefix)
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for match in _IMAGE_REF_RE.finditer(raw_question):
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if match.start() > cursor:
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chunk = raw_question[cursor:match.start()]
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if chunk:
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content.append({"type": "text", "text": chunk})
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text_parts.append(chunk)
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image_idx = int(match.group(1)) - 1
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marker = f"[Image #{image_idx + 1}]"
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text_parts.append(marker)
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if 0 <= image_idx < len(item["image_paths"]):
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image_url = {"url": _image_to_data_uri(item["image_paths"][image_idx])}
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if image_detail and image_detail != "auto":
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image_url["detail"] = image_detail
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content.append({"type": "image_url", "image_url": image_url})
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used_indices.add(image_idx)
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else:
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content.append({"type": "text", "text": marker})
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cursor = match.end()
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if cursor < len(raw_question):
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tail = raw_question[cursor:]
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if tail:
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content.append({"type": "text", "text": tail})
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text_parts.append(tail)
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for idx, path in enumerate(item["image_paths"]):
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if idx in used_indices:
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continue
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marker = f"\n[Additional Image #{idx + 1}]"
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text_parts.append(marker)
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content.append({"type": "text", "text": marker})
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image_url = {"url": _image_to_data_uri(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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content.append({"type": "image_url", "image_url": image_url})
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answer_instruction = (
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"\n\nAnswer with the single correct option letter inside <answer>...</answer>."
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if item.get("is_choice")
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else "\n\nAnswer with the short final answer inside <answer>...</answer>."
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)
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content.append({"type": "text", "text": answer_instruction})
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text_parts.append(answer_instruction)
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if diagnostic_mode and diagnostic_instruction.strip():
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diag_block = f"\n\n## Training Readout\n{diagnostic_instruction.strip()}"
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content.append({"type": "text", "text": diag_block})
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text_parts.append(diag_block)
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return content, "".join(text_parts)
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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_content, user_text = _build_user_content(
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item,
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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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messages = [
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{"role": "system", "content": system},
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{"role": "user", "content": user_content},
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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 MMRB multi-image reasoning question.",
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preamble=(
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"Use this skill when solving the current multi-image reasoning task.\n"
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"Inspect all attached images 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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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_content(
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item,
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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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)[1]
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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 images carefully and answer again."
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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_paths"],
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)
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prompt = (
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"Use the `reflact-student` skill available in this workspace.\n"
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"Read `task.md`, inspect all attached images, and answer the question.\n"
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"Keep 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_paths"],
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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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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("subtask") or item.get("task_type") or "mmrb",
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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_paths": item["image_paths"],
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"gold_answer": item["answer"],
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"evaluation_mode": evaluation_mode(),
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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_student_exec_backend():
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from reflact.model import azure_openai as _llm
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response = ""
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conversation: list[dict] = [
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{
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"role": "user",
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"content": item["question"] + "\n\n" + "\n".join(
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f"[image] {os.path.basename(path)}" for path in item["image_paths"]
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),
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}
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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.STUDENT_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 "<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) - 1
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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_item(item=item, prediction_text=response)
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result["evaluation_mode"] = eval_result["evaluation_mode"]
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result["predicted_answer"] = eval_result["predicted_answer"]
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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["matched_gold"] = eval_result["matched_gold"]
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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"predicted '{eval_result['predicted_answer']}' but expected '{item['answer']}'"
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)
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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"Predicted label: {eval_result['predicted_label']!r}\n"
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f"Gold answer: {item['answer']!r}\n"
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f"Correct: {eval_result['em']}\n"
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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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{
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"role": "user",
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"content": user_text + "\n\n" + "\n".join(
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f"[image] {os.path.basename(path)}" for path in item["image_paths"]
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),
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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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)
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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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{
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"role": "user",
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"content": "Review the same images carefully and answer again. Keep the final answer inside <answer>...</answer>.",
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},
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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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)
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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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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_item(item=item, prediction_text=response)
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result["evaluation_mode"] = eval_result["evaluation_mode"]
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result["predicted_answer"] = eval_result["predicted_answer"]
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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["matched_gold"] = eval_result["matched_gold"]
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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"predicted '{eval_result['predicted_answer']}' but expected '{item['answer']}'"
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)
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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"Predicted label: {eval_result['predicted_label']!r}\n"
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f"Gold answer: {item['answer']!r}\n"
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f"Correct: {eval_result['em']}\n"
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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 = 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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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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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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diagnostic_mode=diagnostic_mode,
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diagnostic_instruction=diagnostic_instruction,
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diagnostic_trace_context=(diagnostic_trace_context_by_id or {}).get(str(item["id"]), ""),
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): item
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for item in pending
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}
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for fut in as_completed(futs):
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row = fut.result()
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results.append(row)
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outf.write(json.dumps(row, ensure_ascii=False) + "\n")
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outf.flush()
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return results
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