mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 16:07:46 +00:00
you-n-g
a73f2dbc12
* feat: runnalbe -- add exp_gen_cls param, get_leaves and merge exp gen functionalities * fix: remove unused scenario_desc and update YAML task labels * feat: override selection and update merge task description * lint * lint * lint * lint * lint * fix: log competition setting to enable mle_summary * fix name error
294 lines
12 KiB
Python
294 lines
12 KiB
Python
"""
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This is a class that try to store/resume/traceback the workflow session
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Postscripts:
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- Originally, I want to implement it in a more general way with python generator.
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However, Python generator is not picklable (dill does not support pickle as well)
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"""
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import datetime
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import os
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import pickle
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import time
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from collections import defaultdict
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Any, Callable, Optional, TypeVar, Union, cast
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import pytz
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from tqdm.auto import tqdm
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from rdagent.core.conf import RD_AGENT_SETTINGS
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from rdagent.log import rdagent_logger as logger
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from rdagent.log.timer import RD_Agent_TIMER_wrapper, RDAgentTimer
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if RD_AGENT_SETTINGS.enable_mlflow:
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import mlflow
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class LoopMeta(type):
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@staticmethod
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def _get_steps(bases: tuple[type, ...]) -> list[str]:
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"""
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Recursively get all the `steps` from the base classes and combine them into a single list.
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Args:
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bases (tuple): A tuple of base classes.
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Returns:
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List[Callable]: A list of steps combined from all base classes.
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"""
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steps = []
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for base in bases:
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for step in LoopMeta._get_steps(base.__bases__) + getattr(base, "steps", []):
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if step not in steps and step not in ["load", "dump"]: # incase user override the load/dump method
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steps.append(step)
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return steps
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def __new__(mcs, clsname: str, bases: tuple[type, ...], attrs: dict[str, Any]) -> Any:
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"""
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Create a new class with combined steps from base classes and current class.
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Args:
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clsname (str): Name of the new class.
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bases (tuple): Base classes.
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attrs (dict): Attributes of the new class.
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Returns:
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LoopMeta: A new instance of LoopMeta.
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"""
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steps = LoopMeta._get_steps(bases) # all the base classes of parents
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for name, attr in attrs.items():
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if not name.startswith("_") and callable(attr):
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if name not in steps and name not in ["load", "dump"]: # incase user override the load/dump method
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# NOTE: if we override the step in the subclass
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# Then it is not the new step. So we skip it.
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steps.append(name)
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attrs["steps"] = steps
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return super().__new__(mcs, clsname, bases, attrs)
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@dataclass
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class LoopTrace:
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start: datetime.datetime # the start time of the trace
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end: datetime.datetime # the end time of the trace
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step_idx: int
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# TODO: more information about the trace
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class LoopBase:
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"""
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Assumption:
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- The last step is responsible for recording information!!!!
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"""
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steps: list[str] # a list of steps to work on
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loop_trace: dict[int, list[LoopTrace]]
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skip_loop_error: tuple[type[BaseException], ...] = () # you can define a list of error that will skip current loop
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EXCEPTION_KEY = "_EXCEPTION"
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def __init__(self) -> None:
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self.loop_idx = 0 # current loop index
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self.step_idx = 0 # the index of next step to be run
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self.loop_prev_out: dict[str, Any] = {} # the step results of current loop
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self.loop_trace = defaultdict(list[LoopTrace]) # the key is the number of loop
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self.session_folder = logger.log_trace_path / "__session__"
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self.timer: RDAgentTimer = RD_Agent_TIMER_wrapper.timer
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def run(self, step_n: int | None = None, loop_n: int | None = None, all_duration: str | None = None) -> None:
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"""
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Parameters
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----------
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step_n : int | None
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How many steps to run;
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`None` indicates to run forever until error or KeyboardInterrupt
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loop_n: int | None
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How many steps to run; if current loop is incomplete, it will be counted as the first loop for completion
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`None` indicates to run forever until error or KeyboardInterrupt
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"""
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if all_duration is not None and not self.timer.started:
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self.timer.reset(all_duration=all_duration)
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with tqdm(total=len(self.steps), desc="Workflow Progress", unit="step") as pbar:
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while True:
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if step_n is not None:
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if step_n <= 0:
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break
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step_n -= 1
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if loop_n is not None:
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if loop_n <= 0:
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break
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if RD_AGENT_SETTINGS.enable_mlflow:
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mlflow.log_metric("loop_index", self.loop_idx)
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mlflow.log_metric("step_index", self.step_idx)
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current_local_datetime = datetime.datetime.now(pytz.timezone("Asia/Shanghai"))
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float_like_datetime = (
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current_local_datetime.second
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+ current_local_datetime.minute * 1e2
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+ current_local_datetime.hour * 1e4
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+ current_local_datetime.day * 1e6
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+ current_local_datetime.month * 1e8
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+ current_local_datetime.year * 1e10
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)
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mlflow.log_metric("current_datetime", float_like_datetime)
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if self.timer.started:
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if RD_AGENT_SETTINGS.enable_mlflow:
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mlflow.log_metric("remain_time", self.timer.remain_time().seconds) # type: ignore[union-attr]
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mlflow.log_metric(
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"remain_percent", self.timer.remain_time() / self.timer.all_duration * 100 # type: ignore[operator]
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)
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if self.timer.is_timeout():
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logger.warning("Timeout, exiting the loop.")
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break
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else:
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logger.info(f"Timer remaining time: {self.timer.remain_time()}")
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li, si = self.loop_idx, self.step_idx
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name = self.steps[si]
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logger.info(f"Start Loop {li}, Step {si}: {name}")
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with logger.tag(f"Loop_{li}.{name}"):
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start = datetime.datetime.now(datetime.timezone.utc)
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func: Callable[..., Any] = cast(Callable[..., Any], getattr(self, name))
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try:
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self.loop_prev_out[name] = func(self.loop_prev_out)
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# TODO: Fix the error logger.exception(f"Skip loop {li} due to {e}")
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except Exception as e:
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if isinstance(e, self.skip_loop_error):
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# FIXME: This does not support previous demo (due to their last step is not for recording)
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logger.warning(f"Skip loop {li} due to {e}")
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# NOTE: strong assumption! The last step is responsible for recording information
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self.step_idx = len(self.steps) - 1 # directly jump to the last step.
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self.loop_prev_out[self.EXCEPTION_KEY] = e
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continue
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else:
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raise
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finally:
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# make sure failure steps are displayed correclty
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end = datetime.datetime.now(datetime.timezone.utc)
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self.loop_trace[li].append(LoopTrace(start, end, step_idx=si))
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# Update tqdm progress bar directly to step_idx
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pbar.n = si + 1
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pbar.set_postfix(
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loop_index=li, step_index=si + 1, step_name=name
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) # step_name indicate last finished step_name
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# index increase and save session
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self.step_idx = (self.step_idx + 1) % len(self.steps)
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if self.step_idx == 0: # reset to step 0 in next round
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self.loop_idx += 1
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if loop_n is not None:
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loop_n -= 1
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self.loop_prev_out = {}
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pbar.reset() # reset the progress bar for the next loop
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self.dump(self.session_folder / f"{li}" / f"{si}_{name}") # save a snapshot after the session
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def dump(self, path: str | Path) -> None:
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if RD_Agent_TIMER_wrapper.timer.started:
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RD_Agent_TIMER_wrapper.timer.update_remain_time()
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path = Path(path)
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path.parent.mkdir(parents=True, exist_ok=True)
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with path.open("wb") as f:
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pickle.dump(self, f)
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@classmethod
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def load(
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cls,
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path: Union[str, Path],
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output_path: Optional[Union[str, Path]] = None,
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do_truncate: bool = False,
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replace_timer: bool = True,
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) -> "LoopBase":
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path = Path(path)
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with path.open("rb") as f:
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session = cast(LoopBase, pickle.load(f))
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# set session folder
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# - P1: if output_path explicitly specified.
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# - P2: RD_AGENT_SETTINGS.log_trace_path
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output_path_value = output_path if output_path is not None else RD_AGENT_SETTINGS.log_trace_path
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if output_path_value is not None:
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output_path_path = Path(output_path_value)
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output_path_path.mkdir(parents=True, exist_ok=True)
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session.session_folder = output_path_path / "__session__"
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# set trace path
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logger.set_trace_path(session.session_folder.parent)
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# truncate future message
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if do_truncate:
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max_loop = max(session.loop_trace.keys())
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logger.storage.truncate(time=session.loop_trace[max_loop][-1].end)
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if session.timer.started:
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if replace_timer:
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RD_Agent_TIMER_wrapper.replace_timer(session.timer)
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RD_Agent_TIMER_wrapper.timer.restart_by_remain_time()
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else:
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# Use the default timer to replace the session timer
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session.timer = RD_Agent_TIMER_wrapper.timer
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return session
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ASpecificRet = TypeVar("ASpecificRet")
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def wait_retry(
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retry_n: int = 3, sleep_time: int = 1, transform_args_fn: Callable[[tuple, dict], tuple[tuple, dict]] | None = None
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) -> Callable[[Callable[..., ASpecificRet]], Callable[..., ASpecificRet]]:
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"""Decorator to wait and retry the function for retry_n times.
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Example:
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>>> import time
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>>> @wait_retry(retry_n=2, sleep_time=1)
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... def test_func():
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... global counter
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... counter += 1
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... if counter < 3:
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... raise ValueError("Counter is less than 3")
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... return counter
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>>> counter = 0
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>>> try:
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... test_func()
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... except ValueError as e:
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... print(f"Caught an exception: {e}")
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Error: Counter is less than 3
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Error: Counter is less than 3
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Caught an exception: Counter is less than 3
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>>> counter
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2
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"""
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assert retry_n > 0, "retry_n should be greater than 0"
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def decorator(f: Callable[..., ASpecificRet]) -> Callable[..., ASpecificRet]:
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def wrapper(*args: Any, **kwargs: Any) -> ASpecificRet:
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for i in range(retry_n + 1):
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try:
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return f(*args, **kwargs)
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except Exception as e:
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print(f"Error: {e}")
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time.sleep(sleep_time)
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if i == retry_n:
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raise
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# Update args and kwargs using the transform function if provided.
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if transform_args_fn is not None:
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args, kwargs = transform_args_fn(args, kwargs)
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else:
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# just for passing mypy CI.
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return f(*args, **kwargs)
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return wrapper
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return decorator
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