mirror of
https://github.com/NousResearch/atropos.git
synced 2026-04-19 12:57:58 +00:00
890 lines
33 KiB
Python
890 lines
33 KiB
Python
import asyncio
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import json
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import logging
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import os
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import random
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import sys
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import time
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import uuid
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import warnings
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from abc import ABC, abstractmethod
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from enum import Enum
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from typing import Any, Dict, List, Optional, Tuple, TypedDict, Union
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import aiohttp
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import jsonlines
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import numpy as np
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from pydantic import BaseModel, Field
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from pydantic_cli import Cmd, FailedExecutionException, run_and_exit
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from tenacity import retry, stop_after_attempt, wait_random_exponential
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from transformers import AutoTokenizer
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import wandb
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from atroposlib.type_definitions import UUID
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from atroposlib.utils.metrics import get_std_min_max_avg
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from ..type_definitions import Item, Message
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from .server_handling.server_manager import (
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OpenaiConfig,
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ServerBaseline,
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ServerManager,
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ServerManagerConfig,
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)
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.DEBUG)
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class ScoredDataGroup(TypedDict):
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tokens: List[List[int]]
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masks: List[List[int]]
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scores: List[float]
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advantages: Optional[List[List[float]]]
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ref_logprobs: Optional[List[List[float]]]
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messages: Optional[List[List[Message]]]
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group_overrides: Optional[Dict]
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overrides: Optional[List[Dict]]
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class EvalHandlingEnum(Enum):
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"""
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Enum for handling evals.
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"""
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STOP_TRAIN = "STOP_TRAIN"
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LIMIT_TRAIN = "LIMIT_TRAIN"
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NONE = "NONE"
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class BaseEnvConfig(BaseModel):
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"""
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Basic env configuration.
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"""
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group_size: int = Field(
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default=4, description="How many responses are grouped together for scoring"
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)
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max_num_workers: int = Field(
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default=-1,
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description="Maximum number of workers to use, -1 calculates from max_num_workers_per_node",
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)
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max_eval_workers: int = Field(
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default=16, description="Maximum number of workers to use for evaluation"
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)
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max_num_workers_per_node: int = Field(
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default=8, description="Maximum number of workers to use per node"
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)
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steps_per_eval: int = Field(
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default=100, description="Number of steps to take before evaluating"
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)
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max_token_length: int = Field(
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default=2048, description="Maximum token length used in generations"
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)
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eval_handling: EvalHandlingEnum = Field(
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default=EvalHandlingEnum.STOP_TRAIN, description="How to handle evaluations"
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)
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eval_limit_ratio: float = Field(
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default=0.5, description="Ratio of training workers to limit during evals"
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)
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inference_weight: float = Field(
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default=1.0,
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description="Inference weight, set to -1 to ignore it if you're doing something special here.",
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)
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batch_size: int = Field(
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default=-1,
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description="Batch size for training, will be set by the trainer and passed in via the fastapi interface, if applicable", # noqa: E501
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)
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max_batches_offpolicy: int = Field(
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default=3, description="Maximum number of batches to have in queue."
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)
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tokenizer_name: str = Field(
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default="NousResearch/DeepHermes-3-Llama-3-1B-Preview",
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description="Hugging Face tokenzer to use.",
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)
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use_wandb: bool = Field(default=True, description="Whether to use wandb")
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rollout_server_url: str = Field(
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default="http://localhost:8000", description="URL of the rollout server"
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)
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total_steps: int = Field(default=1000, description="Total number of steps to run")
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wandb_name: str | None = Field(
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default=None,
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description="Name to be grouped by in wandb",
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)
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num_rollouts_to_keep: int = Field(
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default=32, description="Number of rollouts to display on wandb"
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)
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num_rollouts_per_group_for_logging: int = Field(
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default=1,
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description="Number of rollouts per group to keep for logging. If -1, keep all rollouts",
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)
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ensure_scores_are_not_same: bool = Field(
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default=True,
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description="Ensure that the scores are not the same, should usually be True",
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)
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data_path_to_save_groups: Optional[str] = Field(
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default=None,
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description="Path to save the groups, if set, will write groups to this jsonl",
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)
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min_items_sent_before_logging: int = Field(
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default=2,
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description="Minimum number of items sent before logging, if 0 or less, logs every time",
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)
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class BaseEnv(ABC):
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name = None
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env_config_cls = BaseEnvConfig
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def __init__(
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self,
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config: BaseEnvConfig,
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server_configs: Union[ServerBaseline, List[OpenaiConfig]],
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slurm=True,
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testing=False,
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):
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self.items_sent_this_step = 0
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self.eval_runner = None # type: Optional[asyncio.Task]
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self.workers_added_list = list()
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self.succeeded_task_duration = list()
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self.failed_task_duration = list()
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self.task_duration = list()
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self.mainloop_timings = list()
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self.task_successful = list()
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self.last_loop_time = None
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self.last_completed_item = None
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self.config = config
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self.server = ServerManager(server_configs, slurm=slurm, testing=testing)
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self.workers = set()
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self.eval_workers = set()
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self.backlog = []
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self.rollouts_for_wandb = []
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self.running_items: dict[UUID, Item] = dict()
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self.wandb_project = None
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self.wandb_group = None
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self.curr_step = 0
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self.max_token_len = -1
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self.tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name)
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self.completion_lengths = []
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self.max_num_workers = config.max_num_workers
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if self.max_num_workers == -1:
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self.max_num_workers = config.max_num_workers_per_node * len(
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self.server.servers
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)
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self.wandb_prepend = None
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self.checkpoint_dir = ""
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self.checkpoint_interval = -1
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if self.config.data_path_to_save_groups is not None:
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if os.path.exists(self.config.data_path_to_save_groups):
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raise FileExistsError(
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"Data path already exists! Please remove it or change it."
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)
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self.jsonl_writer = jsonlines.open(
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self.config.data_path_to_save_groups, "w"
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) # type: jsonlines.Writer
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else:
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self.jsonl_writer = None
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@classmethod
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def config_init(
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cls,
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) -> Tuple[BaseEnvConfig, Union[ServerBaseline, List[OpenaiConfig]]]:
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"""
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Initialize the config
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"""
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return cls.env_config_cls(), ServerBaseline()
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async def collect_trajectory(self, item: Item) -> Tuple[Any | None, List[Item]]:
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raise NotImplementedError(
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"Handle env single method must be implemented in subclass "
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)
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async def collect_trajectories(self, item: Item) -> Tuple[
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Union[
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Optional[ScoredDataGroup], List[Optional[ScoredDataGroup]], List[Any | None]
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],
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List[Item],
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]:
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"""
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:param item:
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:return:
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"""
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tasks = []
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for _ in range(self.config.group_size):
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tasks.append(self.collect_trajectory(item))
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results = await asyncio.gather(*tasks)
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backlog = []
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to_postprocess = []
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for result in results:
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if result[0] is not None:
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to_postprocess.append(result[0])
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backlog.extend(result[1])
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random.shuffle(backlog)
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return to_postprocess, backlog
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async def postprocess_histories(
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self,
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trajectories: Union[Optional[ScoredDataGroup], List[Optional[ScoredDataGroup]]],
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) -> Union[Optional[ScoredDataGroup], List[Optional[ScoredDataGroup]]]:
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"""
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Postprocess the histories, this is called after the collect_trajectories method
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If you don't need to do anything to the trajectories, you may safely ignore this.
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:param trajectories:
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:return:
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"""
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return trajectories
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@abstractmethod
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async def get_next_item(self) -> Item:
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"""
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Get the next items to be rolled out
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"""
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raise NotImplementedError(
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"Get_next_items method must be implemented in subclass "
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)
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@abstractmethod
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async def evaluate(self, *args, **kwargs):
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"""
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Evaluate the environment, this is called every steps_per_eval steps
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Included here is an example on how to use eval workers to run a task.
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You may however do whatever you want in this method.
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:param args:
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:param kwargs:
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:return: None.
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"""
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for data in ["my", "eval", "data"]:
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while len(self.eval_workers) >= self.config.max_eval_workers:
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await asyncio.sleep(0.1)
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worker = asyncio.create_task(asyncio.sleep(0.1))
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self.eval_workers.add(worker)
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worker.add_done_callback(self.eval_workers.discard)
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raise NotImplementedError("Evaluate method must be implemented in subclass ")
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def load_checkpoint(self):
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# check if file exists...
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ckpt_path = os.path.join(
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self.checkpoint_dir,
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"env_checkpoints",
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self.wandb_prepend,
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f"step-{self.curr_step}.json",
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)
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if os.path.exists(ckpt_path):
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with open(ckpt_path, "r") as f:
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data = json.load(f)
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# now load the data
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for key in data:
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setattr(self, key, data[key])
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def save_checkpoint(self, step, data=None):
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print(f"Saving checkpoint at step {step} with data {data}")
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if data is None:
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# Don't have anything to save, abort
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return
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# check if file exists...
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ckpt_dir = os.path.join(
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self.checkpoint_dir, "env_checkpoints", self.wandb_prepend
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)
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# create directory if necessary
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os.makedirs(ckpt_dir, exist_ok=True)
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ckpt_path = os.path.join(
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self.checkpoint_dir,
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"env_checkpoints",
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self.wandb_prepend,
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f"step-{step}.json",
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)
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os.makedirs(os.path.dirname(ckpt_path), exist_ok=True)
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with open(ckpt_path, "w") as f:
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json.dump(data, f)
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async def setup(self):
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"""Setup the environment"""
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raise NotImplementedError("Setup method must be implemented in subclass")
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async def setup_wandb(self):
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if self.config.use_wandb:
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# Setup wandb getting the group and project via the server
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while self.wandb_project is None:
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async with aiohttp.ClientSession() as session:
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async with session.get(
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f"{self.config.rollout_server_url}/wandb_info"
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) as resp:
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data = await resp.json()
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self.wandb_group = data["group"]
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self.wandb_project = data["project"]
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if self.wandb_project is None:
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await asyncio.sleep(1)
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else:
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wandb.init(
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project=self.wandb_project,
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group=self.wandb_group,
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config=self.config.model_dump(),
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)
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break
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async def register_env(self):
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# Now register the env...
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async with aiohttp.ClientSession() as session:
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async with session.post(
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f"{self.config.rollout_server_url}/register-env",
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json={
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"max_token_length": self.config.max_token_length,
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"desired_name": self.config.wandb_name,
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"weight": self.config.inference_weight,
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},
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) as resp:
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data = await resp.json()
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self.env_id = data["env_id"]
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self.wandb_prepend = data["wandb_name"]
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self.curr_step = data["starting_step"]
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self.checkpoint_dir = data["checkpoint_dir"]
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self.checkpoint_interval = data["checkpoint_interval"]
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if self.config.total_steps == -1:
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self.config.total_steps = data["num_steps"]
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if self.config.total_steps == -1:
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raise ValueError("Total steps not set in config or server!")
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print(
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f"Initialized env with id {self.env_id}: "
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f"curr_step: {self.curr_step}, "
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f"checkpoint_dir: {self.checkpoint_dir}, "
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f"checkpoint_interval: {self.checkpoint_interval}"
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)
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if self.curr_step > 0:
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self.load_checkpoint()
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async def get_server_info(self):
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"""
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Get the server info
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"""
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async with aiohttp.ClientSession() as session:
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async with session.get(f"{self.config.rollout_server_url}/info") as resp:
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data = await resp.json()
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if data["batch_size"] != -1:
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# update the batch size
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self.config.batch_size = data["batch_size"]
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if data["max_token_len"] != -1:
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self.max_token_len = data["max_token_len"]
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if self.config.batch_size == -1:
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logging.warning("Batch size not set by config or server!")
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if self.config.group_size > self.config.batch_size:
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raise ValueError(
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f"group_size ({self.config.group_size}) "
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f"must be less than batch_size ({self.config.batch_size})"
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)
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def perf_stats(self, metrics_dict):
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"""
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returns wandb metrics for performance
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"""
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if len(self.task_duration) > 1:
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get_std_min_max_avg(
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"train_perf/task_duration", self.task_duration, metrics_dict
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)
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self.task_duration = list()
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if len(self.succeeded_task_duration) > 1:
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get_std_min_max_avg(
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"train_perf/succeeded_task_duration",
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self.succeeded_task_duration,
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metrics_dict,
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)
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metrics_dict["train/items_sent_to_api"] = len(self.succeeded_task_duration)
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self.succeeded_task_duration = list()
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if len(self.failed_task_duration) > 1:
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get_std_min_max_avg(
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"train_perf/failed_task_duration",
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self.failed_task_duration,
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metrics_dict,
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)
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metrics_dict["train/items_rejected"] = len(self.failed_task_duration)
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self.failed_task_duration = list()
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if len(self.mainloop_timings) > 1:
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get_std_min_max_avg(
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"train_perf/mainloop_timings",
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self.mainloop_timings,
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metrics_dict,
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)
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self.mainloop_timings = list()
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if len(self.workers_added_list) > 1:
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get_std_min_max_avg(
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"train_perf/workers_added_per_attempt",
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self.workers_added_list,
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metrics_dict,
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)
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self.workers_added_list = list()
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return metrics_dict
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|
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async def create_rollout_table(self, wandb_metrics):
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if len(self.rollouts_for_wandb) > 0:
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table = wandb.Table(columns=["text", "score"])
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for group in self.rollouts_for_wandb:
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for item in group:
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table.add_data(item[0], item[1])
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wandb_metrics["train/rollouts"] = table
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return wandb_metrics
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|
|
|
async def add_rollouts_for_wandb(
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self,
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scored_data: Union[ScoredDataGroup, List[ScoredDataGroup]],
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item: Item = None,
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):
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# Save rollout to trajectory
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num_keep = self.config.num_rollouts_per_group_for_logging
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if num_keep == -1:
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num_keep = self.config.group_size
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self.rollouts_for_wandb.append(
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[
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(
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self.tokenizer.decode(scored_data["tokens"][i]),
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scored_data["scores"][i],
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|
)
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|
for i in range(num_keep)
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|
]
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|
)
|
|
if len(self.rollouts_for_wandb) > self.config.num_rollouts_to_keep:
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self.rollouts_for_wandb.pop(0)
|
|
|
|
async def wandb_log(self, wandb_metrics: Optional[Dict] = None):
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|
"""
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|
Log to wandb.
|
|
|
|
To use this in your subclass, please ensure this is called after you do your metrics
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|
e.g.
|
|
def wandb_log(self, wandb_metrics: Optional[Dict] = None):
|
|
wandb_metrics = {}
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|
wandb_metrics['my_metric'] = 0.5
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|
super().wandb_log(wandb_metrics)
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|
"""
|
|
if wandb_metrics is None:
|
|
wandb_metrics = dict()
|
|
for i, server in enumerate(self.server.servers):
|
|
server_wandb_metrics = await server.wandb_metrics({}, f"server_{i}")
|
|
if len(self.completion_lengths) > 0:
|
|
wandb_metrics["train/completion_lengths"] = sum(
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self.completion_lengths
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) / len(self.completion_lengths)
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|
wandb_metrics["train/completion_lengths_std"] = np.std(
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self.completion_lengths
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)
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|
wandb_metrics["train/completion_lengths_max"] = np.max(
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self.completion_lengths
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|
)
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|
wandb_metrics["train/completion_lengths_min"] = np.min(
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self.completion_lengths
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|
)
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|
wandb_metrics["train/completion_lengths_p95"] = (
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np.array(self.completion_lengths) > (0.95 * self.max_token_len)
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|
).mean()
|
|
wandb_metrics = await self.create_rollout_table(wandb_metrics)
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|
wandb_metrics = self.perf_stats(wandb_metrics)
|
|
self.rollouts_for_wandb = []
|
|
self.completion_lengths = []
|
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if self.config.use_wandb:
|
|
if self.wandb_prepend is not None:
|
|
wandb_metrics = {
|
|
f"{self.wandb_prepend}_{k}": v for k, v in wandb_metrics.items()
|
|
}
|
|
# add server metrics to wandb without prepend to collate them all
|
|
wandb_metrics.update(server_wandb_metrics)
|
|
wandb.log(wandb_metrics, step=self.curr_step)
|
|
|
|
@retry(
|
|
stop=stop_after_attempt(3),
|
|
wait=wait_random_exponential(multiplier=1, max=10),
|
|
)
|
|
async def _send_scored_data_to_api(self, scored_data):
|
|
"""
|
|
Send scored data to the API with retry logic for timeouts and server errors.
|
|
"""
|
|
url = (
|
|
f"{self.config.rollout_server_url}/scored_data_list"
|
|
if isinstance(scored_data, list)
|
|
else f"{self.config.rollout_server_url}/scored_data"
|
|
)
|
|
async with aiohttp.ClientSession() as session:
|
|
async with session.post(
|
|
url,
|
|
json=scored_data,
|
|
) as resp:
|
|
if resp.status >= 500:
|
|
# Server errors (5xx) should trigger a retry
|
|
logging.debug(f"Server error: {resp.status}, retrying...")
|
|
raise Exception(f"Server error: {resp.status}")
|
|
elif resp.status >= 400:
|
|
# Client errors (4xx) are logged but not retried
|
|
logging.error(f"Client error: {resp.status}, not retrying")
|
|
return
|
|
# Success case: print response text
|
|
print(await resp.text())
|
|
|
|
async def handle_send_to_api(
|
|
self,
|
|
scored_data: Union[ScoredDataGroup, List[ScoredDataGroup]],
|
|
item: Item = None,
|
|
):
|
|
"""
|
|
Send the chats to the API with robust error handling and support for multiple ScoredDataGroups.
|
|
|
|
Args:
|
|
scored_data: List of scored items to send
|
|
item: Optional item for context
|
|
"""
|
|
group_size = scored_data.get("group_overrides", {}).get(
|
|
"group_size", self.config.group_size
|
|
)
|
|
if (
|
|
(scored_data is not None)
|
|
and (None not in scored_data)
|
|
and (len(scored_data["tokens"]) == group_size)
|
|
):
|
|
if self.config.ensure_scores_are_not_same:
|
|
if len(set(scored_data["scores"])) == 1:
|
|
# Scores are the same, don't send to API
|
|
return
|
|
await self.add_rollouts_for_wandb(scored_data, item)
|
|
# Check for ref_logprobs
|
|
if "ref_logprobs" not in scored_data:
|
|
# Strongly typed dict, so we need to add it
|
|
scored_data["ref_logprobs"] = None
|
|
if "overrides" not in scored_data:
|
|
scored_data["overrides"] = None
|
|
if "group_overrides" not in scored_data:
|
|
scored_data["group_overrides"] = None
|
|
|
|
# Track completion lengths
|
|
for mask in scored_data["masks"]:
|
|
self.completion_lengths.append(len(mask))
|
|
# Add the scores to the queue
|
|
if any([len(x) >= self.max_token_len for x in scored_data["tokens"]]):
|
|
# Don't send to API if the token length is too long
|
|
return
|
|
# Save data, if applicable:
|
|
if self.jsonl_writer is not None:
|
|
self.jsonl_writer.write(scored_data)
|
|
# Send data with retries and error handling
|
|
try:
|
|
self.items_sent_this_step += 1
|
|
await self._send_scored_data_to_api(scored_data)
|
|
except (Exception, TimeoutError) as e:
|
|
print(f"Failed to send scored data after retries: {e}")
|
|
|
|
async def handle_env(
|
|
self, item_uuid: str
|
|
) -> Optional[Union[ScoredDataGroup, List[ScoredDataGroup]]]:
|
|
"""
|
|
Handle the rollout of an item
|
|
"""
|
|
item = self.running_items.get(item_uuid)
|
|
if item is None:
|
|
print(f"item {item_uuid} not found... returning")
|
|
return None
|
|
start_time = time.time()
|
|
logger.debug(f"handle_env: Starting with item: {item}")
|
|
# do a rollout with item
|
|
try:
|
|
to_postprocess, to_backlog = await self.collect_trajectories(item)
|
|
except Exception:
|
|
to_postprocess = None
|
|
to_backlog = []
|
|
# add the items to the queue
|
|
if len(to_backlog) > 0:
|
|
self.backlog.extend(to_backlog)
|
|
try:
|
|
if (to_postprocess is None) or (len(to_postprocess) == 0):
|
|
pass
|
|
else:
|
|
to_postprocess = await self.postprocess_histories(to_postprocess)
|
|
except Exception as e:
|
|
logger.error(f"Error in scoring: {item}")
|
|
print(e)
|
|
to_postprocess = None
|
|
self.running_items.pop(item_uuid, None)
|
|
duration = max(0.0, time.time() - start_time)
|
|
self.task_duration.append(duration)
|
|
if to_postprocess is not None:
|
|
self.task_successful.append(1)
|
|
self.succeeded_task_duration.append(duration)
|
|
logger.debug(f"handle_env: Collected {len(to_postprocess)} trajectories")
|
|
await self.handle_send_to_api(to_postprocess, item)
|
|
else:
|
|
self.task_successful.append(0)
|
|
self.failed_task_duration.append(duration)
|
|
logger.debug("handle_env: No trajectories collected")
|
|
# Finally pop it
|
|
await self.cleanup()
|
|
return to_postprocess
|
|
|
|
async def cleanup(self):
|
|
"""
|
|
Optional: Cleanup the environment
|
|
"""
|
|
pass
|
|
|
|
@retry(
|
|
stop=stop_after_attempt(3), wait=wait_random_exponential(multiplier=1, max=10)
|
|
)
|
|
async def get_status(self):
|
|
async with aiohttp.ClientSession() as session:
|
|
async with session.get(
|
|
f"{self.config.rollout_server_url}/status-env",
|
|
json={"env_id": self.env_id},
|
|
) as resp:
|
|
self.status_dict = await resp.json()
|
|
new_weight = self.status_dict["env_weight"]
|
|
max_num_workers = self.config.max_num_workers
|
|
if max_num_workers == -1:
|
|
max_num_workers = self.config.max_num_workers_per_node * len(
|
|
self.server.servers
|
|
)
|
|
self.max_num_workers = max_num_workers
|
|
await self.server.update_weight(new_weight)
|
|
|
|
async def env_step_checks(self):
|
|
# Check if we need to run an eval or log...
|
|
if self.curr_step != self.status_dict["current_step"]:
|
|
if self.config.steps_per_eval > 0:
|
|
if (self.curr_step % self.config.steps_per_eval) > (
|
|
self.status_dict["current_step"] % self.config.steps_per_eval
|
|
):
|
|
if (self.eval_runner is None) or (self.eval_runner.done()):
|
|
eval_task = asyncio.create_task(self.evaluate())
|
|
self.eval_runner = eval_task
|
|
if self.config.eval_handling == EvalHandlingEnum.STOP_TRAIN:
|
|
# Stop training if eval is running
|
|
self.backlog.extend(self.running_items.values())
|
|
for worker in self.workers:
|
|
worker.cancel()
|
|
self.workers = set()
|
|
self.running_items: dict[UUID, Item] = dict()
|
|
else:
|
|
warnings.warn(
|
|
"Eval is not finished in this iteration of the loop, skipping this eval step..."
|
|
)
|
|
if self.checkpoint_interval > 0:
|
|
if (self.curr_step % self.checkpoint_interval) > (
|
|
self.status_dict["current_step"] % self.checkpoint_interval
|
|
):
|
|
checkpoint_step = (
|
|
self.status_dict["current_step"] // self.checkpoint_interval
|
|
) * self.checkpoint_interval
|
|
self.save_checkpoint(checkpoint_step)
|
|
self.curr_step = self.status_dict["current_step"]
|
|
if self.items_sent_this_step >= self.config.min_items_sent_before_logging:
|
|
self.items_sent_this_step = 0
|
|
await self.wandb_log({})
|
|
|
|
async def add_train_workers(self):
|
|
if (self.eval_runner is not None) and (not self.eval_runner.done()):
|
|
if self.config.eval_handling == EvalHandlingEnum.STOP_TRAIN:
|
|
return
|
|
elif self.config.eval_handling == EvalHandlingEnum.LIMIT_TRAIN:
|
|
max_num_workers = int(
|
|
self.max_num_workers * self.config.eval_limit_ratio
|
|
)
|
|
else:
|
|
max_num_workers = self.max_num_workers
|
|
else:
|
|
max_num_workers = self.max_num_workers
|
|
# set max_num_workers to whatever is max off policy and num workers
|
|
max_num_workers = min(
|
|
max_num_workers,
|
|
(
|
|
self.config.max_batches_offpolicy
|
|
* self.config.batch_size
|
|
// self.config.group_size
|
|
)
|
|
- (self.status_dict["queue_size"]),
|
|
)
|
|
if (self.curr_step == 0) and (len(self.workers) == 0):
|
|
# We are starting up, so we should just skip the append to the list
|
|
pass
|
|
else:
|
|
self.workers_added_list.append(max_num_workers - len(self.workers))
|
|
while len(self.workers) < max_num_workers:
|
|
# Generate a UUID for tracking this item
|
|
item_uuid = str(uuid.uuid4())
|
|
if len(self.backlog) > 0:
|
|
item = self.backlog.pop()
|
|
else:
|
|
item = await self.get_next_item()
|
|
if item is None:
|
|
break
|
|
self.running_items[item_uuid] = item
|
|
worker = asyncio.create_task(self.handle_env(item_uuid))
|
|
self.workers.add(worker)
|
|
worker.add_done_callback(
|
|
lambda fut, i=item: (
|
|
(
|
|
self.workers.discard(fut),
|
|
(
|
|
setattr(self, "last_completed_item", i)
|
|
if fut.result()
|
|
else None
|
|
),
|
|
)[1]
|
|
if fut.done() and not fut.cancelled()
|
|
else None
|
|
)
|
|
)
|
|
|
|
async def env_manager(self):
|
|
"""
|
|
Rollout manager
|
|
"""
|
|
await self.setup()
|
|
await self.setup_wandb()
|
|
await self.register_env()
|
|
await self.get_server_info()
|
|
# Wait for other instances to get setup :)
|
|
await asyncio.sleep(5)
|
|
while True:
|
|
if self.last_loop_time is not None:
|
|
self.mainloop_timings.append(
|
|
max(0.0, time.time() - self.last_loop_time)
|
|
)
|
|
# get status from server
|
|
self.last_loop_time = time.time()
|
|
await self.get_status()
|
|
await self.env_step_checks()
|
|
logger.info(f"env_manager: Status dict: {self.status_dict}")
|
|
if (
|
|
self.status_dict["current_step"]
|
|
+ (
|
|
self.status_dict["queue_size"]
|
|
* self.config.group_size
|
|
// self.config.batch_size
|
|
)
|
|
) > self.config.total_steps:
|
|
for worker in self.workers:
|
|
worker.cancel()
|
|
break
|
|
if (
|
|
(
|
|
self.status_dict["queue_size"] * self.config.group_size
|
|
>= self.config.max_batches_offpolicy * self.config.batch_size
|
|
)
|
|
and (self.config.max_batches_offpolicy > 0)
|
|
) or (self.config.batch_size == -1):
|
|
# We have too many, lets cleanup the tasks and wait a bit
|
|
self.backlog.extend(self.running_items.values())
|
|
for worker in self.workers:
|
|
worker.cancel()
|
|
self.running_items = dict()
|
|
self.workers = set()
|
|
elif len(self.workers) >= self.max_num_workers:
|
|
pass
|
|
else:
|
|
await self.add_train_workers()
|
|
await asyncio.sleep(0.1)
|
|
|
|
@classmethod
|
|
def cli(cls):
|
|
"""
|
|
Command-line interface entry point for the environment.
|
|
This method handles the CLI commands for serve and process.
|
|
"""
|
|
|
|
# Create subcommands dictionary
|
|
subcommands = {
|
|
"serve": cls.get_cli_serve_config_cls(),
|
|
"process": cls.get_cli_process_config_cls(),
|
|
}
|
|
|
|
# Custom exception handler for cleaner error output
|
|
def custom_error_handler(ex: Exception) -> int:
|
|
"""Handles exceptions with clean output for known error types."""
|
|
if isinstance(ex, FailedExecutionException):
|
|
# Handle argparse errors (already printed by argparse)
|
|
print()
|
|
print(ex.message.split("error: ")[-1])
|
|
return 2
|
|
else:
|
|
# For any other exception
|
|
print(f"Error: {str(ex)}", file=sys.stderr)
|
|
return 1
|
|
|
|
run_and_exit(
|
|
subcommands,
|
|
description=f"CLI for {cls.__name__}",
|
|
exception_handler=custom_error_handler,
|
|
)
|
|
|
|
@classmethod
|
|
def get_cli_serve_config_cls(cls) -> type:
|
|
"""
|
|
Returns the CLI configuration class for serving commands.
|
|
|
|
Returns:
|
|
type: The CliServeConfig class for serving commands.
|
|
"""
|
|
|
|
env_config, server_configs = cls.config_init()
|
|
|
|
class CliServeConfig(
|
|
cls.env_config_cls, OpenaiConfig, ServerManagerConfig, Cmd
|
|
):
|
|
"""
|
|
Configuration for the serve command.
|
|
This combines BaseEnvConfig and OpenaiConfig into a single command.
|
|
"""
|
|
|
|
def run(self) -> None:
|
|
"""The logic to execute for the 'serve' command."""
|
|
# Convert this config into the formats needed by BaseEnv
|
|
if self.wandb_name is None and cls.name is not None:
|
|
self.wandb_name = cls.name
|
|
model_dumped = self.model_dump(exclude_unset=True)
|
|
server_manager_config = ServerManagerConfig(**model_dumped)
|
|
# Create the environment instance
|
|
env = cls(
|
|
config=env_config,
|
|
server_configs=server_configs,
|
|
slurm=server_manager_config.slurm,
|
|
testing=server_manager_config.testing,
|
|
)
|
|
|
|
# Run the environment
|
|
asyncio.run(env.env_manager())
|
|
|
|
return CliServeConfig
|
|
|
|
@classmethod
|
|
def get_cli_process_config_cls(cls) -> type:
|
|
"""
|
|
Returns the CLI configuration class for processing commands.
|
|
|
|
Returns:
|
|
type: The CliProcessConfig class for processing commands.
|
|
"""
|
|
|
|
class CliProcessConfig(Cmd):
|
|
"""
|
|
Configuration for the process command.
|
|
This is a placeholder for future implementation.
|
|
"""
|
|
|
|
# Add process-specific fields here
|
|
group_size: int = Field(
|
|
default=4, description="Number of responses per prompt"
|
|
)
|
|
n_groups: int = Field(default=1, description="Number of groups to process")
|
|
output_file: str = Field(
|
|
..., description="Path to jsonl file to write results"
|
|
)
|
|
|
|
def run(self) -> None:
|
|
"""The logic to execute for the 'process' command."""
|
|
print(
|
|
f"Processing {self.n_groups} groups of "
|
|
f"{self.group_size} responses and "
|
|
f"writing to {self.output_file}"
|
|
)
|
|
print("This is a placeholder implementation for the process command.")
|
|
# Actual implementation would go here
|
|
|
|
return CliProcessConfig
|