mirror of
https://github.com/NousResearch/atropos.git
synced 2026-04-25 17:10:42 +00:00
revert base.py
This commit is contained in:
Dakota
parent
c483840f59
commit
17bb7bdf15
1 changed files with 146 additions and 148 deletions
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@ -185,11 +185,11 @@ class BaseEnv(ABC):
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server_cls: APIServer = APIServer
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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[APIServerConfig]],
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slurm=False,
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testing=False,
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self,
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config: BaseEnvConfig,
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server_configs: Union[ServerBaseline, List[APIServerConfig]],
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slurm=False,
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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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@ -272,7 +272,7 @@ class BaseEnv(ABC):
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@classmethod
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def config_init(
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cls,
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cls,
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) -> Tuple[BaseEnvConfig, Union[ServerBaseline, List[APIServerConfig]]]:
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"""
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Initialize the config
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@ -280,7 +280,7 @@ class BaseEnv(ABC):
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return cls.env_config_cls(), ServerBaseline()
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async def collect_trajectory(
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self, item: Item
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self, item: Item
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) -> Tuple[Optional[Union[ScoredDataItem, Any]], 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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@ -339,8 +339,8 @@ class BaseEnv(ABC):
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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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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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@ -428,7 +428,7 @@ class BaseEnv(ABC):
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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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f"{self.config.rollout_server_url}/wandb_info"
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) as resp:
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data = await parse_http_response(resp, logger)
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self.wandb_group = data["group"]
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@ -462,14 +462,14 @@ class BaseEnv(ABC):
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try:
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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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"min_batch_allocation": self.config.min_batch_allocation,
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"group_size": self.config.group_size,
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},
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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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"min_batch_allocation": self.config.min_batch_allocation,
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"group_size": self.config.group_size,
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},
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) as resp:
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data = await parse_http_response(resp, logger)
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return data
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@ -577,9 +577,9 @@ class BaseEnv(ABC):
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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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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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@ -626,7 +626,7 @@ class BaseEnv(ABC):
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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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np.array(self.completion_lengths) > (0.95 * self.max_token_len)
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).mean()
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wandb_metrics = await self.create_rollout_table(wandb_metrics)
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wandb_metrics = self.perf_stats(wandb_metrics)
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@ -642,15 +642,15 @@ class BaseEnv(ABC):
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wandb.log(wandb_metrics, step=self.curr_step)
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async def evaluate_log(
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self,
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metrics: Dict,
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task_name: Optional[str] = None,
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model_name: Optional[str] = None,
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start_time: Optional[float] = None,
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end_time: Optional[float] = None,
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generation_parameters: Optional[Dict] = None,
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samples: Optional[List[Dict]] = None,
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verbose: bool = True,
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self,
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metrics: Dict,
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task_name: Optional[str] = None,
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model_name: Optional[str] = None,
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start_time: Optional[float] = None,
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end_time: Optional[float] = None,
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generation_parameters: Optional[Dict] = None,
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samples: Optional[List[Dict]] = None,
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verbose: bool = True,
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):
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"""
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Log evaluation results to a JSON file in the format expected by nous-evals.
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@ -690,7 +690,7 @@ class BaseEnv(ABC):
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# Get model name from first server config
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first_server = self.server.servers[0]
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if hasattr(first_server, "config") and hasattr(
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first_server.config, "model_name"
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first_server.config, "model_name"
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):
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model_name = first_server.config.model_name
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if start_time is None:
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@ -767,8 +767,8 @@ class BaseEnv(ABC):
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)
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async with aiohttp.ClientSession() as session:
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async with session.post(
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url,
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json=scored_data,
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url,
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json=scored_data,
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) as resp:
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if resp.status >= 500:
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# Server errors (5xx) should trigger a retry
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@ -782,11 +782,11 @@ class BaseEnv(ABC):
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print(await resp.text())
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async def handle_send_to_api(
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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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do_send_to_api: bool = True,
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abort_on_any_max_length_exceeded: bool = True,
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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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do_send_to_api: bool = True,
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abort_on_any_max_length_exceeded: bool = True,
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):
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"""
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Send the chats to the API with robust error handling and support for multiple ScoredDataGroups.
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@ -810,7 +810,7 @@ class BaseEnv(ABC):
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)
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if not (
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(None not in group) and (len(group.get("tokens", [])) == group_size)
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(None not in group) and (len(group.get("tokens", [])) == group_size)
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):
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logger.warning(
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f"Group structure invalid, or token count mismatch (expected {group_size}), "
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@ -819,8 +819,8 @@ class BaseEnv(ABC):
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continue
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if (
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self.config.ensure_scores_are_not_same
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and len(set(group["scores"])) == 1
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self.config.ensure_scores_are_not_same
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and len(set(group["scores"])) == 1
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):
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logger.warning("Scores are the same in a group, skipping...")
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continue
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@ -838,7 +838,7 @@ class BaseEnv(ABC):
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"ensure your trainer handles this appropriately."
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)
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elif abort_on_any_max_length_exceeded and any(
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[len(x) >= self.max_token_len for x in group["tokens"]]
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[len(x) >= self.max_token_len for x in group["tokens"]]
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):
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logger.warning("Token length is too long in a group, skipping...")
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continue
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@ -877,7 +877,7 @@ class BaseEnv(ABC):
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print(f"Failed to send {data_type_str} after retries: {e}")
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async def handle_env(
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self, item_uuid: str
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self, item_uuid: str
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) -> Optional[Union[ScoredDataGroup, List[ScoredDataGroup]]]:
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"""
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Handle the rollout of an item
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@ -938,8 +938,8 @@ class BaseEnv(ABC):
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async def get_status(self):
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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}/status-env",
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json={"env_id": self.env_id},
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f"{self.config.rollout_server_url}/status-env",
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json={"env_id": self.env_id},
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) as resp:
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self.status_dict = await parse_http_response(resp, logger)
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new_weight = self.status_dict["env_weight"]
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@ -956,7 +956,7 @@ class BaseEnv(ABC):
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if self.curr_step != self.status_dict["current_step"]:
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if self.config.steps_per_eval > 0:
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if (self.curr_step % self.config.steps_per_eval) > (
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self.status_dict["current_step"] % self.config.steps_per_eval
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self.status_dict["current_step"] % self.config.steps_per_eval
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):
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if (self.eval_runner is None) or (self.eval_runner.done()):
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eval_task = asyncio.create_task(self.evaluate())
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@ -976,11 +976,11 @@ class BaseEnv(ABC):
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)
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if self.checkpoint_interval > 0:
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if (self.curr_step % self.checkpoint_interval) > (
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self.status_dict["current_step"] % self.checkpoint_interval
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self.status_dict["current_step"] % self.checkpoint_interval
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):
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checkpoint_step = (
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self.status_dict["current_step"] // self.checkpoint_interval
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) * self.checkpoint_interval
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self.status_dict["current_step"] // self.checkpoint_interval
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) * self.checkpoint_interval
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self.save_checkpoint(checkpoint_step)
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self.curr_step = self.status_dict["current_step"]
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if self.items_sent_this_step >= self.config.min_items_sent_before_logging:
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@ -1003,14 +1003,12 @@ class BaseEnv(ABC):
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max_num_workers = min(
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max_num_workers,
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(
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self.config.max_batches_offpolicy
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* self.derived_batch_size
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// self.config.group_size
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self.config.max_batches_offpolicy
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* self.derived_batch_size
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// self.config.group_size
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)
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- (self.status_dict["queue_size"]),
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)
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- (self.status_dict["self_queue_size"]),
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)
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# now minimum num workers based on allocation
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# Now if we have a minimum batch allocation, we need to add workers to fill the self queue, in case of
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# overruns by other environments
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if self.config.min_batch_allocation is not None:
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@ -1018,31 +1016,31 @@ class BaseEnv(ABC):
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0,
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math.ceil(
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(
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(
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(
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math.ceil(
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self.config.min_batch_allocation
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* self.config.batch_size
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* self.config.max_batches_offpolicy
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/ self.status_dict["max_group_size"]
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)
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+ (
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self.status_dict["max_group_size"]
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// self.config.group_size
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(
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math.ceil(
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self.config.min_batch_allocation
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* self.config.batch_size
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* self.config.max_batches_offpolicy
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/ self.status_dict["max_group_size"]
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)
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+ (
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self.status_dict["max_group_size"]
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// self.config.group_size
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)
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)
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* self.status_dict["max_group_size"]
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)
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- (
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(
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self.status_dict["max_group_size"]
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* self.status_dict["self_queue_size"]
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// (
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self.status_dict["max_group_size"]
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/ self.config.group_size
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)
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)
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)
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* self.status_dict["max_group_size"]
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)
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- (
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(
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self.status_dict["max_group_size"]
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* self.status_dict["self_queue_size"]
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// (
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self.status_dict["max_group_size"]
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/ self.config.group_size
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)
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)
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)
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)
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/ self.config.group_size
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),
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@ -1124,45 +1122,45 @@ class BaseEnv(ABC):
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await self.env_step_checks()
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logger.info(f"env_manager: Status dict: {self.status_dict}")
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if (
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self.status_dict["current_step"]
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+ (
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self.status_dict["self_queue_size"]
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* self.config.group_size
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// self.config.batch_size
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)
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self.status_dict["current_step"]
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+ (
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self.status_dict["queue_size"]
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* self.config.group_size
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// self.config.batch_size
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)
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) > self.config.total_steps:
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for worker in self.workers:
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worker.cancel()
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break
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if (
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(
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self.status_dict["self_queue_size"] * self.config.group_size
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>= self.config.max_batches_offpolicy * self.config.batch_size
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)
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and (self.config.max_batches_offpolicy > 0)
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and (
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(self.config.min_batch_allocation is None)
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or (
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(
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(
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(
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math.ceil(
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self.config.min_batch_allocation
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* self.config.batch_size
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* self.config.max_batches_offpolicy
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/ self.status_dict["max_group_size"]
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)
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* (
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self.status_dict["max_group_size"]
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// self.config.group_size
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)
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)
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)
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- (self.status_dict["self_queue_size"])
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)
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<= 0
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(
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self.status_dict["queue_size"] * self.config.group_size
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>= self.config.max_batches_offpolicy * self.config.batch_size
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)
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and (self.config.max_batches_offpolicy > 0)
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and (
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(self.config.min_batch_allocation is None)
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or (
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(
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(
|
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(
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math.ceil(
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self.config.min_batch_allocation
|
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* self.config.batch_size
|
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* self.config.max_batches_offpolicy
|
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/ self.status_dict["max_group_size"]
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)
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* (
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self.status_dict["max_group_size"]
|
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// self.config.group_size
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)
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)
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)
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- (self.status_dict["self_queue_size"])
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)
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<= 0
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)
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)
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)
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) or (self.derived_batch_size == -1):
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# We have too many, lets cleanup the tasks and wait a bit
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self.backlog.extend([x["item"] for x in self.running_items.values()])
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|
|
@ -1335,8 +1333,8 @@ class BaseEnv(ABC):
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# Note: This modifies the 'self' instance based on CLI args before full parsing.
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wandb_name_attr = f"{ENV_NAMESPACE}{NAMESPACE_SEP}wandb_name"
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if (
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getattr(self, wandb_name_attr, None) is None
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and cls.name is not None
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getattr(self, wandb_name_attr, None) is None
|
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and cls.name is not None
|
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):
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setattr(self, wandb_name_attr, cls.name)
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|
|
@ -1367,14 +1365,14 @@ class BaseEnv(ABC):
|
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) # CLI args
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yaml_oai_config = yaml_config.get(OPENAI_NAMESPACE, {})
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if isinstance(default_server_configs, ServerBaseline) and (
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oai_cli_passed_args or yaml_oai_config
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oai_cli_passed_args or yaml_oai_config
|
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):
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raise ValueError(
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"ServerBaseline is not compatible with OpenAI-namespaced CLI arguments. Please edit `config_init` directly or use APIServerConfig." # noqa: E501
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)
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if (
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isinstance(default_server_configs, list)
|
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and len(default_server_configs) == 1
|
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isinstance(default_server_configs, list)
|
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and len(default_server_configs) == 1
|
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):
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# can't use the same var name because it shadows the class variable and we get an error
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default_openai_config_ = default_server_configs[0]
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|
|
@ -1383,7 +1381,7 @@ class BaseEnv(ABC):
|
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if isinstance(yaml_oai_config, list) and len(yaml_oai_config) == 1:
|
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yaml_oai_config = yaml_oai_config[0]
|
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if isinstance(default_openai_config_, APIServerConfig) and isinstance(
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yaml_oai_config, dict
|
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yaml_oai_config, dict
|
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):
|
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openai_config_dict = merge_dicts(
|
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default_openai_config_.model_dump(), # Default APIServerConfig (or from class init)
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|
|
@ -1487,7 +1485,7 @@ class BaseEnv(ABC):
|
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# If it's ServerBaseline, we use APIServerConfig type for CLI args to allow overrides.
|
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if isinstance(default_server_configs_from_init, list):
|
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if default_server_configs_from_init and isinstance(
|
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default_server_configs_from_init[0], APIServerConfig
|
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default_server_configs_from_init[0], APIServerConfig
|
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):
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openai_config_cls_for_cli = type(default_server_configs_from_init[0])
|
||||
# Use the actual instance for default values later if it's a single config
|
||||
|
|
@ -1530,8 +1528,8 @@ class BaseEnv(ABC):
|
|||
# Set default wandb name if not provided and class has a name
|
||||
wandb_name_attr = f"{ENV_NAMESPACE}{NAMESPACE_SEP}wandb_name"
|
||||
if (
|
||||
getattr(self, wandb_name_attr, None) is None
|
||||
and cls.name is not None
|
||||
getattr(self, wandb_name_attr, None) is None
|
||||
and cls.name is not None
|
||||
):
|
||||
setattr(self, wandb_name_attr, cls.name)
|
||||
|
||||
|
|
@ -1580,7 +1578,7 @@ class BaseEnv(ABC):
|
|||
)
|
||||
|
||||
if isinstance(default_server_configs_from_init, ServerBaseline) and (
|
||||
oai_cli_passed_args or yaml_oai_config
|
||||
oai_cli_passed_args or yaml_oai_config
|
||||
):
|
||||
# If config_init provided ServerBaseline, but CLI/YAML provides OpenAI specifics,
|
||||
# it implies an override intent for a single server.
|
||||
|
|
@ -1659,13 +1657,13 @@ class BaseEnv(ABC):
|
|||
if isinstance(final_openai_configs, list):
|
||||
for cfg in final_openai_configs:
|
||||
if (
|
||||
isinstance(cfg, APIServerConfig)
|
||||
and cfg.base_url
|
||||
and (
|
||||
isinstance(cfg, APIServerConfig)
|
||||
and cfg.base_url
|
||||
and (
|
||||
"localhost" in cfg.base_url
|
||||
or "0.0.0.0" in cfg.base_url
|
||||
or "127.0.0.1" in cfg.base_url
|
||||
)
|
||||
)
|
||||
):
|
||||
warnings.warn(
|
||||
"You are using a local Base URL for an OpenAI compatible server in 'process' mode. "
|
||||
|
|
@ -1674,13 +1672,13 @@ class BaseEnv(ABC):
|
|||
)
|
||||
break # Warn once
|
||||
elif (
|
||||
isinstance(final_openai_configs, APIServerConfig)
|
||||
and final_openai_configs.base_url
|
||||
and (
|
||||
"localhost" in final_openai_configs.base_url
|
||||
or "0.0.0.0" in final_openai_configs.base_url
|
||||
or "127.0.0.1" in final_openai_configs.base_url
|
||||
)
|
||||
isinstance(final_openai_configs, APIServerConfig)
|
||||
and final_openai_configs.base_url
|
||||
and (
|
||||
"localhost" in final_openai_configs.base_url
|
||||
or "0.0.0.0" in final_openai_configs.base_url
|
||||
or "127.0.0.1" in final_openai_configs.base_url
|
||||
)
|
||||
):
|
||||
warnings.warn(
|
||||
"You are using a local Base URL for an OpenAI compatible server in 'process' mode. "
|
||||
|
|
@ -1756,7 +1754,7 @@ class BaseEnv(ABC):
|
|||
# If it's ServerBaseline, we use APIServerConfig type for CLI args to allow overrides.
|
||||
if isinstance(default_server_configs_from_init, list):
|
||||
if default_server_configs_from_init and isinstance(
|
||||
default_server_configs_from_init[0], APIServerConfig
|
||||
default_server_configs_from_init[0], APIServerConfig
|
||||
):
|
||||
openai_config_cls_for_cli = type(default_server_configs_from_init[0])
|
||||
# Use the actual instance for default values later if it's a single config
|
||||
|
|
@ -1799,8 +1797,8 @@ class BaseEnv(ABC):
|
|||
# Set default wandb name if not provided and class has a name
|
||||
wandb_name_attr = f"{ENV_NAMESPACE}{NAMESPACE_SEP}wandb_name"
|
||||
if (
|
||||
getattr(self, wandb_name_attr, None) is None
|
||||
and cls.name is not None
|
||||
getattr(self, wandb_name_attr, None) is None
|
||||
and cls.name is not None
|
||||
):
|
||||
setattr(self, wandb_name_attr, cls.name)
|
||||
|
||||
|
|
@ -1843,7 +1841,7 @@ class BaseEnv(ABC):
|
|||
)
|
||||
|
||||
if isinstance(default_server_configs_from_init, ServerBaseline) and (
|
||||
oai_cli_passed_args or yaml_oai_config
|
||||
oai_cli_passed_args or yaml_oai_config
|
||||
):
|
||||
# If config_init provided ServerBaseline, but CLI/YAML provides OpenAI specifics,
|
||||
# it implies an override intent for a single server.
|
||||
|
|
@ -1922,13 +1920,13 @@ class BaseEnv(ABC):
|
|||
if isinstance(final_openai_configs, list):
|
||||
for cfg in final_openai_configs:
|
||||
if (
|
||||
isinstance(cfg, APIServerConfig)
|
||||
and cfg.base_url
|
||||
and (
|
||||
isinstance(cfg, APIServerConfig)
|
||||
and cfg.base_url
|
||||
and (
|
||||
"localhost" in cfg.base_url
|
||||
or "0.0.0.0" in cfg.base_url
|
||||
or "127.0.0.1" in cfg.base_url
|
||||
)
|
||||
)
|
||||
):
|
||||
warnings.warn(
|
||||
"You are using a local Base URL for an OpenAI compatible server in 'evaluate' mode. "
|
||||
|
|
@ -1937,13 +1935,13 @@ class BaseEnv(ABC):
|
|||
)
|
||||
break # Warn once
|
||||
elif (
|
||||
isinstance(final_openai_configs, APIServerConfig)
|
||||
and final_openai_configs.base_url
|
||||
and (
|
||||
"localhost" in final_openai_configs.base_url
|
||||
or "0.0.0.0" in final_openai_configs.base_url
|
||||
or "127.0.0.1" in final_openai_configs.base_url
|
||||
)
|
||||
isinstance(final_openai_configs, APIServerConfig)
|
||||
and final_openai_configs.base_url
|
||||
and (
|
||||
"localhost" in final_openai_configs.base_url
|
||||
or "0.0.0.0" in final_openai_configs.base_url
|
||||
or "127.0.0.1" in final_openai_configs.base_url
|
||||
)
|
||||
):
|
||||
warnings.warn(
|
||||
"You are using a local Base URL for an OpenAI compatible server in 'evaluate' mode. "
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue