mirror of
https://github.com/microsoft/SkillOpt.git
synced 2026-07-06 07:32:34 +08:00
4a1b984d87
- Rename teacher -> optimizer, student -> target across all code, configs, docs, prompts - CLI: --teacher_model -> --optimizer_model, --student_model -> --target_model - Remove best_skill files, keep only initial skills - Fix slow update gate (force write into skill) - Fix SLOW_UPDATE marker stripping - Remove deep_reflect and meta_reflect mechanisms - Update .env.example with export prefix and azure_cli docs - Add endpoint empty validation in azure_openai.py Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
109 lines
3.5 KiB
Python
109 lines
3.5 KiB
Python
"""Optimizer-driven autonomous update-size decisions."""
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from __future__ import annotations
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import json
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import re
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from typing import Any
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from skillopt.model import chat_optimizer
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from skillopt.optimizer.meta_skill import format_meta_skill_context
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from skillopt.optimizer.update_modes import describe_item, get_payload_items, payload_label
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from skillopt.prompts import load_prompt
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from skillopt.utils import extract_json
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def _coerce_nonnegative_int(value: Any) -> int | None:
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if isinstance(value, bool):
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return None
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if isinstance(value, int):
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return max(0, value)
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if isinstance(value, float) and value.is_integer():
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return max(0, int(value))
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text = str(value or "").strip()
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if not text:
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return None
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match = re.search(r"-?\d+", text)
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if not match:
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return None
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return max(0, int(match.group(0)))
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def decide_autonomous_learning_rate(
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*,
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skill_content: str,
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merged_patch: dict,
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update_mode: str,
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rollout_hard: float,
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rollout_soft: float,
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rollout_n: int,
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step_buffer_context: str = "",
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meta_skill_context: str = "",
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) -> dict:
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"""Ask the optimizer to choose the number of update items for this step.
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The prompt intentionally avoids default budgets, candidate budget lists, or
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scheduler history. The only hard post-processing is validity: the returned
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integer is clamped to the available item count.
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"""
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items = get_payload_items(merged_patch, update_mode)
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available = len(items)
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item_lines = [
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f"[{idx}] {describe_item(item, update_mode, max_chars=700)}"
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for idx, item in enumerate(items)
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]
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user = (
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f"## Current Skill\n{skill_content}\n\n"
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f"## Current Step Evidence\n"
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f"rollout_n={rollout_n}\n"
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f"rollout_hard={rollout_hard:.6f}\n"
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f"rollout_soft={rollout_soft:.6f}\n"
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f"proposed_update_items={available}\n"
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f"update_item_type={payload_label(update_mode)}\n\n"
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f"## Proposed Update Items\n"
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+ "\n".join(item_lines)
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+ "\n\nDecide how many proposed update items should be applied now."
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)
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if step_buffer_context.strip():
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user += f"\n\n## Previous Steps in This Epoch\n{step_buffer_context}"
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optimizer_ctx = format_meta_skill_context(meta_skill_context)
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if optimizer_ctx:
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user = f"{optimizer_ctx}\n\n{user}"
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response = ""
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parsed: dict | None = None
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decision: int | None = None
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try:
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response, _ = chat_optimizer(
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system=load_prompt("lr_autonomous"),
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user=user,
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max_completion_tokens=2048,
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retries=3,
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stage="lr_autonomous",
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)
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parsed = extract_json(response)
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if parsed:
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decision = _coerce_nonnegative_int(parsed.get("learning_rate"))
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except Exception as exc: # noqa: BLE001
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parsed = {"error": str(exc)}
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fallback = False
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if decision is None:
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decision = 0
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fallback = True
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chosen = min(decision, available)
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record = {
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"learning_rate": chosen,
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"raw_learning_rate": decision,
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"available_update_items": available,
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"clamped": chosen != decision,
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"fallback": fallback,
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"reasoning": (parsed or {}).get("reasoning", ""),
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"confidence": (parsed or {}).get("confidence", ""),
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"risk_notes": (parsed or {}).get("risk_notes", []),
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"raw_response": response,
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}
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if parsed and "error" in parsed:
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record["error"] = parsed["error"]
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return record
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