mirror of
https://github.com/NousResearch/atropos.git
synced 2026-04-23 16:54:56 +00:00
first commit
This commit is contained in:
Dakota Nous
commit
621d00dd80
89 changed files with 15315 additions and 0 deletions
890
atroposlib/envs/base.py
Normal file
890
atroposlib/envs/base.py
Normal file
|
|
@ -0,0 +1,890 @@
|
|||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import random
|
||||
import sys
|
||||
import time
|
||||
import uuid
|
||||
import warnings
|
||||
from abc import ABC, abstractmethod
|
||||
from enum import Enum
|
||||
from typing import Any, Dict, List, Optional, Tuple, TypedDict, Union
|
||||
|
||||
import aiohttp
|
||||
import jsonlines
|
||||
import numpy as np
|
||||
from pydantic import BaseModel, Field
|
||||
from pydantic_cli import Cmd, FailedExecutionException, run_and_exit
|
||||
from tenacity import retry, stop_after_attempt, wait_random_exponential
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
import wandb
|
||||
from atroposlib.type_definitions import UUID
|
||||
from atroposlib.utils.metrics import get_std_min_max_avg
|
||||
|
||||
from ..type_definitions import Item, Message
|
||||
from .server_handling.server_manager import (
|
||||
OpenaiConfig,
|
||||
ServerBaseline,
|
||||
ServerManager,
|
||||
ServerManagerConfig,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
logger.setLevel(logging.DEBUG)
|
||||
|
||||
|
||||
class ScoredDataGroup(TypedDict):
|
||||
tokens: List[List[int]]
|
||||
masks: List[List[int]]
|
||||
scores: List[float]
|
||||
advantages: Optional[List[List[float]]]
|
||||
ref_logprobs: Optional[List[List[float]]]
|
||||
messages: Optional[List[List[Message]]]
|
||||
group_overrides: Optional[Dict]
|
||||
overrides: Optional[List[Dict]]
|
||||
|
||||
|
||||
class EvalHandlingEnum(Enum):
|
||||
"""
|
||||
Enum for handling evals.
|
||||
"""
|
||||
|
||||
STOP_TRAIN = "STOP_TRAIN"
|
||||
LIMIT_TRAIN = "LIMIT_TRAIN"
|
||||
NONE = "NONE"
|
||||
|
||||
|
||||
class BaseEnvConfig(BaseModel):
|
||||
"""
|
||||
Basic env configuration.
|
||||
"""
|
||||
|
||||
group_size: int = Field(
|
||||
default=4, description="How many responses are grouped together for scoring"
|
||||
)
|
||||
max_num_workers: int = Field(
|
||||
default=-1,
|
||||
description="Maximum number of workers to use, -1 calculates from max_num_workers_per_node",
|
||||
)
|
||||
max_eval_workers: int = Field(
|
||||
default=16, description="Maximum number of workers to use for evaluation"
|
||||
)
|
||||
max_num_workers_per_node: int = Field(
|
||||
default=8, description="Maximum number of workers to use per node"
|
||||
)
|
||||
steps_per_eval: int = Field(
|
||||
default=100, description="Number of steps to take before evaluating"
|
||||
)
|
||||
max_token_length: int = Field(
|
||||
default=2048, description="Maximum token length used in generations"
|
||||
)
|
||||
eval_handling: EvalHandlingEnum = Field(
|
||||
default=EvalHandlingEnum.STOP_TRAIN, description="How to handle evaluations"
|
||||
)
|
||||
eval_limit_ratio: float = Field(
|
||||
default=0.5, description="Ratio of training workers to limit during evals"
|
||||
)
|
||||
inference_weight: float = Field(
|
||||
default=1.0,
|
||||
description="Inference weight, set to -1 to ignore it if you're doing something special here.",
|
||||
)
|
||||
batch_size: int = Field(
|
||||
default=-1,
|
||||
description="Batch size for training, will be set by the trainer and passed in via the fastapi interface, if applicable", # noqa: E501
|
||||
)
|
||||
max_batches_offpolicy: int = Field(
|
||||
default=3, description="Maximum number of batches to have in queue."
|
||||
)
|
||||
tokenizer_name: str = Field(
|
||||
default="NousResearch/DeepHermes-3-Llama-3-1B-Preview",
|
||||
description="Hugging Face tokenzer to use.",
|
||||
)
|
||||
use_wandb: bool = Field(default=True, description="Whether to use wandb")
|
||||
rollout_server_url: str = Field(
|
||||
default="http://localhost:8000", description="URL of the rollout server"
|
||||
)
|
||||
total_steps: int = Field(default=1000, description="Total number of steps to run")
|
||||
wandb_name: str | None = Field(
|
||||
default=None,
|
||||
description="Name to be grouped by in wandb",
|
||||
)
|
||||
num_rollouts_to_keep: int = Field(
|
||||
default=32, description="Number of rollouts to display on wandb"
|
||||
)
|
||||
num_rollouts_per_group_for_logging: int = Field(
|
||||
default=1,
|
||||
description="Number of rollouts per group to keep for logging. If -1, keep all rollouts",
|
||||
)
|
||||
ensure_scores_are_not_same: bool = Field(
|
||||
default=True,
|
||||
description="Ensure that the scores are not the same, should usually be True",
|
||||
)
|
||||
data_path_to_save_groups: Optional[str] = Field(
|
||||
default=None,
|
||||
description="Path to save the groups, if set, will write groups to this jsonl",
|
||||
)
|
||||
min_items_sent_before_logging: int = Field(
|
||||
default=2,
|
||||
description="Minimum number of items sent before logging, if 0 or less, logs every time",
|
||||
)
|
||||
|
||||
|
||||
class BaseEnv(ABC):
|
||||
|
||||
name = None
|
||||
env_config_cls = BaseEnvConfig
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: BaseEnvConfig,
|
||||
server_configs: Union[ServerBaseline, List[OpenaiConfig]],
|
||||
slurm=True,
|
||||
testing=False,
|
||||
):
|
||||
self.items_sent_this_step = 0
|
||||
self.eval_runner = None # type: Optional[asyncio.Task]
|
||||
self.workers_added_list = list()
|
||||
self.succeeded_task_duration = list()
|
||||
self.failed_task_duration = list()
|
||||
self.task_duration = list()
|
||||
self.mainloop_timings = list()
|
||||
self.task_successful = list()
|
||||
self.last_loop_time = None
|
||||
self.last_completed_item = None
|
||||
self.config = config
|
||||
self.server = ServerManager(server_configs, slurm=slurm, testing=testing)
|
||||
self.workers = set()
|
||||
self.eval_workers = set()
|
||||
self.backlog = []
|
||||
self.rollouts_for_wandb = []
|
||||
self.running_items: dict[UUID, Item] = dict()
|
||||
self.wandb_project = None
|
||||
self.wandb_group = None
|
||||
self.curr_step = 0
|
||||
self.max_token_len = -1
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name)
|
||||
self.completion_lengths = []
|
||||
self.max_num_workers = config.max_num_workers
|
||||
if self.max_num_workers == -1:
|
||||
self.max_num_workers = config.max_num_workers_per_node * len(
|
||||
self.server.servers
|
||||
)
|
||||
self.wandb_prepend = None
|
||||
self.checkpoint_dir = ""
|
||||
self.checkpoint_interval = -1
|
||||
if self.config.data_path_to_save_groups is not None:
|
||||
if os.path.exists(self.config.data_path_to_save_groups):
|
||||
raise FileExistsError(
|
||||
"Data path already exists! Please remove it or change it."
|
||||
)
|
||||
self.jsonl_writer = jsonlines.open(
|
||||
self.config.data_path_to_save_groups, "w"
|
||||
) # type: jsonlines.Writer
|
||||
else:
|
||||
self.jsonl_writer = None
|
||||
|
||||
@classmethod
|
||||
def config_init(
|
||||
cls,
|
||||
) -> Tuple[BaseEnvConfig, Union[ServerBaseline, List[OpenaiConfig]]]:
|
||||
"""
|
||||
Initialize the config
|
||||
"""
|
||||
return cls.env_config_cls(), ServerBaseline()
|
||||
|
||||
async def collect_trajectory(self, item: Item) -> Tuple[Any | None, List[Item]]:
|
||||
raise NotImplementedError(
|
||||
"Handle env single method must be implemented in subclass "
|
||||
)
|
||||
|
||||
async def collect_trajectories(self, item: Item) -> Tuple[
|
||||
Union[
|
||||
Optional[ScoredDataGroup], List[Optional[ScoredDataGroup]], List[Any | None]
|
||||
],
|
||||
List[Item],
|
||||
]:
|
||||
"""
|
||||
|
||||
:param item:
|
||||
:return:
|
||||
"""
|
||||
tasks = []
|
||||
for _ in range(self.config.group_size):
|
||||
tasks.append(self.collect_trajectory(item))
|
||||
results = await asyncio.gather(*tasks)
|
||||
backlog = []
|
||||
to_postprocess = []
|
||||
for result in results:
|
||||
if result[0] is not None:
|
||||
to_postprocess.append(result[0])
|
||||
backlog.extend(result[1])
|
||||
random.shuffle(backlog)
|
||||
return to_postprocess, backlog
|
||||
|
||||
async def postprocess_histories(
|
||||
self,
|
||||
trajectories: Union[Optional[ScoredDataGroup], List[Optional[ScoredDataGroup]]],
|
||||
) -> Union[Optional[ScoredDataGroup], List[Optional[ScoredDataGroup]]]:
|
||||
"""
|
||||
Postprocess the histories, this is called after the collect_trajectories method
|
||||
|
||||
If you don't need to do anything to the trajectories, you may safely ignore this.
|
||||
|
||||
:param trajectories:
|
||||
:return:
|
||||
"""
|
||||
return trajectories
|
||||
|
||||
@abstractmethod
|
||||
async def get_next_item(self) -> Item:
|
||||
"""
|
||||
Get the next items to be rolled out
|
||||
"""
|
||||
raise NotImplementedError(
|
||||
"Get_next_items method must be implemented in subclass "
|
||||
)
|
||||
|
||||
@abstractmethod
|
||||
async def evaluate(self, *args, **kwargs):
|
||||
"""
|
||||
Evaluate the environment, this is called every steps_per_eval steps
|
||||
|
||||
Included here is an example on how to use eval workers to run a task.
|
||||
|
||||
You may however do whatever you want in this method.
|
||||
|
||||
:param args:
|
||||
:param kwargs:
|
||||
:return: None.
|
||||
"""
|
||||
for data in ["my", "eval", "data"]:
|
||||
while len(self.eval_workers) >= self.config.max_eval_workers:
|
||||
await asyncio.sleep(0.1)
|
||||
worker = asyncio.create_task(asyncio.sleep(0.1))
|
||||
self.eval_workers.add(worker)
|
||||
worker.add_done_callback(self.eval_workers.discard)
|
||||
raise NotImplementedError("Evaluate method must be implemented in subclass ")
|
||||
|
||||
def load_checkpoint(self):
|
||||
# check if file exists...
|
||||
ckpt_path = os.path.join(
|
||||
self.checkpoint_dir,
|
||||
"env_checkpoints",
|
||||
self.wandb_prepend,
|
||||
f"step-{self.curr_step}.json",
|
||||
)
|
||||
if os.path.exists(ckpt_path):
|
||||
with open(ckpt_path, "r") as f:
|
||||
data = json.load(f)
|
||||
# now load the data
|
||||
for key in data:
|
||||
setattr(self, key, data[key])
|
||||
|
||||
def save_checkpoint(self, step, data=None):
|
||||
print(f"Saving checkpoint at step {step} with data {data}")
|
||||
if data is None:
|
||||
# Don't have anything to save, abort
|
||||
return
|
||||
# check if file exists...
|
||||
ckpt_dir = os.path.join(
|
||||
self.checkpoint_dir, "env_checkpoints", self.wandb_prepend
|
||||
)
|
||||
# create directory if necessary
|
||||
os.makedirs(ckpt_dir, exist_ok=True)
|
||||
ckpt_path = os.path.join(
|
||||
self.checkpoint_dir,
|
||||
"env_checkpoints",
|
||||
self.wandb_prepend,
|
||||
f"step-{step}.json",
|
||||
)
|
||||
os.makedirs(os.path.dirname(ckpt_path), exist_ok=True)
|
||||
with open(ckpt_path, "w") as f:
|
||||
json.dump(data, f)
|
||||
|
||||
async def setup(self):
|
||||
"""Setup the environment"""
|
||||
raise NotImplementedError("Setup method must be implemented in subclass")
|
||||
|
||||
async def setup_wandb(self):
|
||||
if self.config.use_wandb:
|
||||
# Setup wandb getting the group and project via the server
|
||||
while self.wandb_project is None:
|
||||
async with aiohttp.ClientSession() as session:
|
||||
async with session.get(
|
||||
f"{self.config.rollout_server_url}/wandb_info"
|
||||
) as resp:
|
||||
data = await resp.json()
|
||||
self.wandb_group = data["group"]
|
||||
self.wandb_project = data["project"]
|
||||
if self.wandb_project is None:
|
||||
await asyncio.sleep(1)
|
||||
else:
|
||||
wandb.init(
|
||||
project=self.wandb_project,
|
||||
group=self.wandb_group,
|
||||
config=self.config.model_dump(),
|
||||
)
|
||||
break
|
||||
|
||||
async def register_env(self):
|
||||
# Now register the env...
|
||||
async with aiohttp.ClientSession() as session:
|
||||
async with session.post(
|
||||
f"{self.config.rollout_server_url}/register-env",
|
||||
json={
|
||||
"max_token_length": self.config.max_token_length,
|
||||
"desired_name": self.config.wandb_name,
|
||||
"weight": self.config.inference_weight,
|
||||
},
|
||||
) as resp:
|
||||
data = await resp.json()
|
||||
self.env_id = data["env_id"]
|
||||
self.wandb_prepend = data["wandb_name"]
|
||||
self.curr_step = data["starting_step"]
|
||||
self.checkpoint_dir = data["checkpoint_dir"]
|
||||
self.checkpoint_interval = data["checkpoint_interval"]
|
||||
if self.config.total_steps == -1:
|
||||
self.config.total_steps = data["num_steps"]
|
||||
if self.config.total_steps == -1:
|
||||
raise ValueError("Total steps not set in config or server!")
|
||||
print(
|
||||
f"Initialized env with id {self.env_id}: "
|
||||
f"curr_step: {self.curr_step}, "
|
||||
f"checkpoint_dir: {self.checkpoint_dir}, "
|
||||
f"checkpoint_interval: {self.checkpoint_interval}"
|
||||
)
|
||||
if self.curr_step > 0:
|
||||
self.load_checkpoint()
|
||||
|
||||
async def get_server_info(self):
|
||||
"""
|
||||
Get the server info
|
||||
"""
|
||||
async with aiohttp.ClientSession() as session:
|
||||
async with session.get(f"{self.config.rollout_server_url}/info") as resp:
|
||||
data = await resp.json()
|
||||
if data["batch_size"] != -1:
|
||||
# update the batch size
|
||||
self.config.batch_size = data["batch_size"]
|
||||
if data["max_token_len"] != -1:
|
||||
self.max_token_len = data["max_token_len"]
|
||||
if self.config.batch_size == -1:
|
||||
logging.warning("Batch size not set by config or server!")
|
||||
if self.config.group_size > self.config.batch_size:
|
||||
raise ValueError(
|
||||
f"group_size ({self.config.group_size}) "
|
||||
f"must be less than batch_size ({self.config.batch_size})"
|
||||
)
|
||||
|
||||
def perf_stats(self, metrics_dict):
|
||||
"""
|
||||
returns wandb metrics for performance
|
||||
"""
|
||||
if len(self.task_duration) > 1:
|
||||
get_std_min_max_avg(
|
||||
"train_perf/task_duration", self.task_duration, metrics_dict
|
||||
)
|
||||
self.task_duration = list()
|
||||
if len(self.succeeded_task_duration) > 1:
|
||||
get_std_min_max_avg(
|
||||
"train_perf/succeeded_task_duration",
|
||||
self.succeeded_task_duration,
|
||||
metrics_dict,
|
||||
)
|
||||
metrics_dict["train/items_sent_to_api"] = len(self.succeeded_task_duration)
|
||||
self.succeeded_task_duration = list()
|
||||
if len(self.failed_task_duration) > 1:
|
||||
get_std_min_max_avg(
|
||||
"train_perf/failed_task_duration",
|
||||
self.failed_task_duration,
|
||||
metrics_dict,
|
||||
)
|
||||
metrics_dict["train/items_rejected"] = len(self.failed_task_duration)
|
||||
self.failed_task_duration = list()
|
||||
if len(self.mainloop_timings) > 1:
|
||||
get_std_min_max_avg(
|
||||
"train_perf/mainloop_timings",
|
||||
self.mainloop_timings,
|
||||
metrics_dict,
|
||||
)
|
||||
self.mainloop_timings = list()
|
||||
if len(self.workers_added_list) > 1:
|
||||
get_std_min_max_avg(
|
||||
"train_perf/workers_added_per_attempt",
|
||||
self.workers_added_list,
|
||||
metrics_dict,
|
||||
)
|
||||
self.workers_added_list = list()
|
||||
return metrics_dict
|
||||
|
||||
async def create_rollout_table(self, wandb_metrics):
|
||||
if len(self.rollouts_for_wandb) > 0:
|
||||
table = wandb.Table(columns=["text", "score"])
|
||||
for group in self.rollouts_for_wandb:
|
||||
for item in group:
|
||||
table.add_data(item[0], item[1])
|
||||
wandb_metrics["train/rollouts"] = table
|
||||
return wandb_metrics
|
||||
|
||||
async def add_rollouts_for_wandb(
|
||||
self,
|
||||
scored_data: Union[ScoredDataGroup, List[ScoredDataGroup]],
|
||||
item: Item = None,
|
||||
):
|
||||
# Save rollout to trajectory
|
||||
num_keep = self.config.num_rollouts_per_group_for_logging
|
||||
if num_keep == -1:
|
||||
num_keep = self.config.group_size
|
||||
self.rollouts_for_wandb.append(
|
||||
[
|
||||
(
|
||||
self.tokenizer.decode(scored_data["tokens"][i]),
|
||||
scored_data["scores"][i],
|
||||
)
|
||||
for i in range(num_keep)
|
||||
]
|
||||
)
|
||||
if len(self.rollouts_for_wandb) > self.config.num_rollouts_to_keep:
|
||||
self.rollouts_for_wandb.pop(0)
|
||||
|
||||
async def wandb_log(self, wandb_metrics: Optional[Dict] = None):
|
||||
"""
|
||||
Log to wandb.
|
||||
|
||||
To use this in your subclass, please ensure this is called after you do your metrics
|
||||
e.g.
|
||||
def wandb_log(self, wandb_metrics: Optional[Dict] = None):
|
||||
wandb_metrics = {}
|
||||
wandb_metrics['my_metric'] = 0.5
|
||||
super().wandb_log(wandb_metrics)
|
||||
"""
|
||||
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(
|
||||
self.completion_lengths
|
||||
) / len(self.completion_lengths)
|
||||
wandb_metrics["train/completion_lengths_std"] = np.std(
|
||||
self.completion_lengths
|
||||
)
|
||||
wandb_metrics["train/completion_lengths_max"] = np.max(
|
||||
self.completion_lengths
|
||||
)
|
||||
wandb_metrics["train/completion_lengths_min"] = np.min(
|
||||
self.completion_lengths
|
||||
)
|
||||
wandb_metrics["train/completion_lengths_p95"] = (
|
||||
np.array(self.completion_lengths) > (0.95 * self.max_token_len)
|
||||
).mean()
|
||||
wandb_metrics = await self.create_rollout_table(wandb_metrics)
|
||||
wandb_metrics = self.perf_stats(wandb_metrics)
|
||||
self.rollouts_for_wandb = []
|
||||
self.completion_lengths = []
|
||||
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
|
||||
Loading…
Add table
Add a link
Reference in a new issue