"""Scheduler for rollout tasks."""
import asyncio
import re
import time
import traceback
from collections import defaultdict, deque
from dataclasses import dataclass, field, replace
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import ray
from trinity.common.config import Config
from trinity.common.models import InferenceModel
from trinity.common.workflows import Task
from trinity.explorer.workflow_runner import Status, WorkflowRunner
from trinity.utils.log import get_logger
[文档]
@dataclass
class TaskWrapper:
"""A wrapper for a task.
Each task can run multiple times (repeat_times) on same or different runners.
"""
task: Task
batch_id: Union[int, str]
sub_task_num: int = 1 # number of sub tasks splitted from this task
# if max_repeat_times_per_runner is set, one task may be splitted into multiple sub tasks
finished_sub_task_num: int = 0
completed_runs: int = 0
total_runs: int = 0 # total planned runs for the whole task
metrics: List[Dict[str, float]] = field(default_factory=list)
experience_payloads: List[bytes] = field(default_factory=list)
first_error: Optional[str] = None
emitted: bool = False
[文档]
@dataclass(frozen=True)
class CompletedTaskResult:
"""A completed task result stored by batch and task id."""
batch_id: Union[int, str]
task_id: Union[int, str]
status: Status
experience_payloads: List[bytes] = field(default_factory=list)
[文档]
@dataclass
class RunningTaskState:
"""Per-future execution state tracked while one task is running."""
task: TaskWrapper
runner_id: int
restart_runner_on_cancel: bool = True
# Adapted from verl/trainer/ppo/metric_utils.py
[文档]
def bootstrap_metric(
data: list[Any],
subset_size: int,
reduce_fns: list[Callable[[np.ndarray], float]],
n_bootstrap: int = 1000,
seed: int = 42,
) -> list[tuple[float, float]]:
"""
Performs bootstrap resampling to estimate statistics of metrics.
This function uses bootstrap resampling to estimate the mean and standard deviation
of metrics computed by the provided reduction functions on random subsets of the data.
Args:
data: List of data points to bootstrap from.
subset_size: Size of each bootstrap sample.
reduce_fns: List of functions that compute a metric from a subset of data.
n_bootstrap: Number of bootstrap iterations. Defaults to 1000.
seed: Random seed for reproducibility. Defaults to 42.
Returns:
A list of tuples, where each tuple contains (mean, std) for a metric
corresponding to each reduction function in reduce_fns.
Example:
>>> data = [1, 2, 3, 4, 5]
>>> reduce_fns = [np.mean, np.max]
>>> bootstrap_metric(data, 3, reduce_fns)
[(3.0, 0.5), (4.5, 0.3)] # Example values
"""
np.random.seed(seed)
bootstrap_metric_lsts = [[] for _ in range(len(reduce_fns))]
for _ in range(n_bootstrap):
bootstrap_idxs = np.random.choice(len(data), size=subset_size, replace=True)
bootstrap_data = [data[i] for i in bootstrap_idxs]
for i, reduce_fn in enumerate(reduce_fns):
bootstrap_metric_lsts[i].append(reduce_fn(bootstrap_data))
return [(np.mean(lst), np.std(lst)) for lst in bootstrap_metric_lsts]
[文档]
def calculate_task_level_metrics(metrics: List[Dict], is_eval: bool) -> Dict[str, float]:
"""Calculate task level metrics (mean) from multiple runs of the same task.
Args:
metrics (`List[Dict]`): A list of metric dictionaries from multiple runs of the same task.
is_eval (`bool`): Whether this is an evaluation task.
Returns:
`Dict[str, float]`: A dictionary of aggregated metrics, where each metric is averaged over all runs.
"""
if not metrics:
return {}
aggregated_metrics: Dict[str, List[float]] = defaultdict(list)
for m in metrics:
for key, value in m.items():
if isinstance(value, (int, float)):
aggregated_metrics[key].append(value)
if is_eval:
result = {}
for key, values in aggregated_metrics.items():
if "time/task_execution" in key or "time/run_execution" in key:
result[key] = sum(values) / len(values)
continue
n_values = len(values)
result[f"{key}/mean@{n_values}"] = np.mean(values)
result[f"{key}/std@{n_values}"] = np.std(values)
if n_values > 1:
ns = []
n = 2
while n < n_values:
ns.append(n)
n *= 2
ns.append(n_values)
for n in ns:
[(bon_mean, bon_std), (won_mean, won_std)] = bootstrap_metric(
data=values, subset_size=n, reduce_fns=[np.max, np.min], seed=42
)
result[f"{key}/best@{n}"] = bon_mean
result[f"{key}/worst@{n}"] = won_mean
return result
else:
return {
key: sum(values) / len(values) for key, values in aggregated_metrics.items() if values
}
[文档]
class RunnerWrapper:
"""A wrapper for a WorkflowRunner"""
[文档]
def __init__(
self,
runner_id: int,
rollout_model: InferenceModel,
auxiliary_models: List[InferenceModel],
config: Config,
):
self.logger = get_logger(__name__)
self.runner_id = runner_id
self.rollout_model = rollout_model
self.auxiliary_models = auxiliary_models
self.config = config
self.retry_times = config.explorer.max_retry_times
self.timeout = config.explorer.max_timeout
self.namespace = config.ray_namespace
self.runner = self._create_runner()
self.state = {}
def _create_runner(self):
return (
ray.remote(WorkflowRunner)
.options(
num_cpus=0,
namespace=self.namespace,
scheduling_strategy="SPREAD",
runtime_env={
"env_vars": self.config.explorer.env_vars,
},
)
.remote(
self.config,
self.rollout_model,
self.auxiliary_models,
self.runner_id,
)
)
[文档]
async def prepare(self):
await self.runner.prepare.remote()
[文档]
async def update_state(self) -> None:
"""Get the runner state."""
self.state = await self.runner.get_runner_state.remote()
self.state["running_time"] = time.time() - self.state.get("begin_time", time.time())
[文档]
async def run_with_retry(
self,
task: TaskWrapper,
repeat_times: int,
run_id_base: int,
timeout: float,
collect_partial_runs: bool,
) -> Tuple[Status, bytes, int, float]:
"""
Args:
task (`TaskWrapper`): The task to run.
repeat_times (`int`): The number of times to repeat the task.
run_id_base (`int`): The base run id for this task runs.
timeout (`float`): The timeout for each task run.
Returns:
`Status`: The return status of the task.
`List`: The experiences generated by the task.
`int`: The runner_id of current runner.
`float`: The time taken to run the task.
"""
last_exception_msg = None
await self.runner.__ray_ready__.remote()
start_time = time.time()
status = Status(completed_runs=0, total_runs=repeat_times, metrics=list())
exp_payload = b""
run_task_ref = None
task2run = replace(
task.task,
rollout_args=replace(
task.task.rollout_args,
n=repeat_times,
),
)
try:
for attempt in range(self.retry_times + 1):
try:
run_task_ref = self.runner.run_task.remote(
task=task2run,
batch_id=str(task.batch_id),
repeat_times=repeat_times,
run_id_base=run_id_base,
collect_partial_runs=collect_partial_runs,
)
status, exp_payload = await asyncio.wait_for(
run_task_ref,
timeout=timeout,
)
run_task_ref = None
if status.ok:
break
if collect_partial_runs and status.completed_runs > 0:
self.logger.warning(
"Task returned partial success; skipping retry to avoid "
"re-running successful runs. %s",
status.message,
)
break
else:
self.logger.error(status.message)
except asyncio.TimeoutError:
run_task_ref = None
last_exception_msg = f"Timeout when running task of batch {task.batch_id} at runner {self.runner_id} at attempt {attempt + 1}: {task.task}"
self.logger.error(last_exception_msg)
status = Status(
completed_runs=0,
total_runs=repeat_times,
metrics=list(),
message=last_exception_msg,
)
except asyncio.CancelledError:
if run_task_ref is not None:
ray.cancel(run_task_ref, force=False)
raise
except Exception:
run_task_ref = None
last_exception_msg = traceback.format_exc()
self.logger.warning(
f"Task execution attempt {attempt + 1} failed:\n{last_exception_msg}"
)
status = Status(
completed_runs=0,
total_runs=repeat_times,
metrics=list(),
message=last_exception_msg,
)
finally:
end_time = time.time()
status.metrics.append({"time/task_execution": end_time - start_time})
return status, exp_payload, self.runner_id, end_time - start_time
[文档]
async def restart_runner(self):
old_runner = self.runner
self.runner = self._create_runner()
await self.runner.prepare.remote()
try:
ray.kill(old_runner)
except Exception:
pass
[文档]
def sort_batch_id(batch_id: Union[int, str]):
"""Priority of batch_id"""
# TODO: avoid sort the batch_id every time
if isinstance(batch_id, int):
return (batch_id, 0)
else:
match = re.match(r"^(\d+)", batch_id)
if match:
num = int(match.group(1))
return (num, 1)
else:
return (float("inf"), 1)
[文档]
class Scheduler:
"""Scheduler for rollout tasks.
Supports scheduling tasks to multiple runners, retrying failed tasks,
and collecting results at different levels.
"""
[文档]
def __init__(
self,
config: Config,
rollout_model: List[InferenceModel],
auxiliary_models: Optional[List[List[InferenceModel]]] = None,
):
self.logger = get_logger(__name__)
self.config = config
self.rollout_model = rollout_model
self.auxiliary_models = auxiliary_models or []
self.namespace = ray.get_runtime_context().namespace
self.default_timeout = config.explorer.max_timeout * (config.explorer.max_retry_times + 1)
self.max_retry_times = config.explorer.max_retry_times
self.max_repeat_times = config.explorer.max_repeat_times_per_runner
self.default_batch_size = config.buffer.batch_size
self.running = False
self.runner_num = len(rollout_model) * config.explorer.runner_per_model
self.runners: Dict[int, RunnerWrapper] = dict()
self.idle_runners: set[int] = set() # runner_id of idle runners
self.busy_runners: Dict[int, RunningTaskState] = dict() # runner_id -> running state
self.pending_tasks: Dict[Union[int, str], deque] = defaultdict(
deque
) # batch_id -> (task, repeat_times, run_id_base)
self.running_tasks: Dict[Union[int, str], set[asyncio.Future]] = defaultdict(
set
) # batch_id -> futures
self.task_num_map: Dict[Union[int, str], int] = defaultdict(
int
) # batch_id -> tasks scheduled under this batch_id
self.running_task_state_map: Dict[asyncio.Future, RunningTaskState] = dict()
self.batch_is_eval_map: Dict[Union[int, str], bool] = dict()
self.completed_tasks: Dict[
Union[int, str], Dict[Union[int, str], CompletedTaskResult]
] = defaultdict(
dict
) # batch_id -> results
self.background_tasks: set[asyncio.Task] = set()
self.scheduler_task: Optional[asyncio.Task] = None
self.monitor_task: Optional[asyncio.Task] = None
self.total_running_time = 0.0
self.total_completed_steps = 0
self.total_completed_sub_tasks = 0
self.total_completed_tasks = 0
async def _create_runner(
self,
runner_id: int,
):
runner = RunnerWrapper(
runner_id=runner_id,
rollout_model=self.rollout_model[runner_id % len(self.rollout_model)],
auxiliary_models=[
self.auxiliary_models[j][runner_id % len(self.auxiliary_models[j])]
for j in range(len(self.auxiliary_models))
],
config=self.config,
)
await runner.prepare()
self.runners[runner_id] = runner
self.idle_runners.add(runner_id)
async def _restart_runner(self, runner_id: int):
"""Restart a runner."""
await self.runners[runner_id].restart_runner()
if runner_id in self.busy_runners:
running_state = self.busy_runners.pop(runner_id)
task = running_state.task
self.logger.warning(
f"Runner {runner_id} failed to run task at batch_id {task.batch_id}: {task.task.raw_task}"
)
self.idle_runners.add(runner_id)
self.logger.info(f"Runner {runner_id} restarted.")
def _schedule_runner_restart(self, runner_id: int) -> None:
restart_task = asyncio.create_task(self._restart_runner(runner_id))
self.background_tasks.add(restart_task)
restart_task.add_done_callback(self.background_tasks.discard)
async def _scheduler_loop(self) -> None:
self.logger.info("Scheduler loop started.")
while self.running:
try:
await self._schedule_pending_tasks()
await asyncio.sleep(0.01)
except Exception:
self.logger.error(f"Error in scheduler loop:\n{traceback.format_exc()}")
await asyncio.sleep(0.1)
self.logger.info("Scheduler loop stopped.")
async def _monitor_runner_state_loop(self) -> None:
interval = self.config.explorer.runner_state_report_interval
if interval <= 0:
self.logger.info("Runner state monitoring loop disabled.")
return
self.logger.info("Runner state monitoring loop started.")
while self.running:
try:
await asyncio.gather(*[runner.update_state() for runner in self.runners.values()])
self.print_all_state()
except Exception:
self.logger.error(
f"Error in runner state monitoring loop:\n{traceback.format_exc()}"
)
await asyncio.sleep(interval)
self.logger.info("Runner state monitoring loop stopped.")
async def _schedule_pending_tasks(self) -> None:
if not self.idle_runners:
return
# TODO: Support more advanced scheduling strategies
for batch_id in sorted(self.pending_tasks.keys(), key=sort_batch_id):
task_queue = self.pending_tasks[batch_id]
while task_queue and self.idle_runners:
task, repeat_times, run_id_base = task_queue.pop()
runner_id = self.idle_runners.pop()
future = asyncio.create_task(
self.runners[runner_id].run_with_retry(
task,
repeat_times=repeat_times,
run_id_base=run_id_base,
timeout=self.dynamic_timeout(),
collect_partial_runs=self.config.explorer.over_rollout.return_partial_tasks,
)
)
running_state = RunningTaskState(task=task, runner_id=runner_id)
self.busy_runners[runner_id] = running_state
self.running_task_state_map[future] = running_state
future.add_done_callback(self.task_done_callback)
self.running_tasks[batch_id].add(future)
if not task_queue:
del self.pending_tasks[batch_id]
[文档]
def task_done_callback(self, async_task: asyncio.Task):
running_state = self.running_task_state_map.pop(async_task)
task = running_state.task
runner_id = running_state.runner_id
if async_task.cancelled():
if not running_state.restart_runner_on_cancel:
self.busy_runners.pop(runner_id, None)
self.idle_runners.add(runner_id)
elif async_task.exception():
self.logger.error(f"Task {task.task.task_id} failed: {async_task.exception()}")
self._schedule_runner_restart(runner_id)
else:
status, exp_payload, runner_id, run_time = async_task.result()
if not task.task.is_eval:
self.total_running_time += run_time
self.total_completed_sub_tasks += 1
self._accumulate_task_result(task, status, exp_payload)
self.busy_runners.pop(runner_id, None)
self.idle_runners.add(runner_id)
# If all sub runs in a task are completed
if task.finished_sub_task_num == task.sub_task_num:
if not task.task.is_eval:
self.total_completed_tasks += 1
self._emit_task_result(task)
self.logger.debug(f"Task completed (batch_id {task.batch_id}).")
if task.batch_id in self.running_tasks:
self.running_tasks[task.batch_id].remove(async_task)
if not self.running_tasks[task.batch_id]:
del self.running_tasks[task.batch_id]
def _accumulate_task_result(
self, task: TaskWrapper, status: Status, experience_payload: bytes
) -> None:
task.finished_sub_task_num += 1
task.completed_runs += status.completed_runs
task.metrics.extend(status.metrics)
if experience_payload:
task.experience_payloads.append(experience_payload)
if not status.ok and task.first_error is None:
task.first_error = status.message
def _build_task_result(self, task: TaskWrapper) -> Tuple[Status, List[bytes]]:
if task.completed_runs < task.total_runs:
message = f"{task.completed_runs}/{task.total_runs} runs completed successfully."
if task.first_error:
message = f"{message} First error: {task.first_error}"
else:
message = f"{message} Remaining runs were cancelled during scheduler cleanup."
else:
message = None
status = Status(
completed_runs=task.completed_runs,
total_runs=task.total_runs,
metrics=[calculate_task_level_metrics(task.metrics, task.task.is_eval)],
message=message,
)
return status, list(task.experience_payloads)
def _emit_task_result(self, task: TaskWrapper) -> None:
if task.emitted:
return
status, experience_payloads = self._build_task_result(task)
task_id = task.task.task_id
completed_result = CompletedTaskResult(
batch_id=task.batch_id,
task_id=task_id,
status=status,
experience_payloads=experience_payloads,
)
self.completed_tasks[task.batch_id][task_id] = completed_result
task.emitted = True
[文档]
def discard_completed_results(self, batch_id: Union[int, str]) -> None:
"""Drop cached completed results for one batch."""
self.completed_tasks.pop(batch_id, None)
self.task_num_map.pop(batch_id, None)
self.batch_is_eval_map.pop(batch_id, None)
def _collect_incomplete_tasks(self, batch_id: Union[int, str]) -> List[TaskWrapper]:
tasks = {}
for task, _, _ in self.pending_tasks.get(batch_id, deque()):
tasks[id(task)] = task
for future in self.running_tasks.get(batch_id, set()):
running_state = self.running_task_state_map.get(future)
if running_state is not None:
tasks[id(running_state.task)] = running_state.task
return list(tasks.values())
def _emit_partial_tasks_for_batch(self, batch_id: Union[int, str]) -> None:
for task in self._collect_incomplete_tasks(batch_id):
if task.emitted or task.completed_runs <= 0:
continue
self._emit_task_result(task)
self.logger.debug(
f"Task partially completed and emitted (batch_id {task.batch_id}, task_id {task.task.task_id})."
)
def _clear_timeout_tasks(self, batch_id: Union[int, str]) -> List[asyncio.Future]:
cancelled_futures = []
if batch_id in self.pending_tasks:
self.logger.info(f"Clear timeout pending tasks at batch_id {batch_id}.")
del self.pending_tasks[batch_id]
if batch_id in self.running_tasks:
self.logger.info(f"Clear timeout running tasks at batch_id {batch_id}.")
for future in self.running_tasks[batch_id]:
cancelled_futures.append(future)
future.cancel()
del self.running_tasks[batch_id]
self.task_num_map.pop(batch_id, None)
return cancelled_futures
def _mark_running_tasks_for_cleanup(
self, batch_id: Union[int, str], restart_runners: bool
) -> None:
for future in self.running_tasks.get(batch_id, set()):
running_state = self.running_task_state_map.get(future)
if running_state is not None:
running_state.restart_runner_on_cancel = restart_runners
[文档]
async def start(self) -> None:
if self.running:
return
self.running = True
await asyncio.gather(*[self._create_runner(i) for i in range(self.runner_num)])
self.scheduler_task = asyncio.create_task(self._scheduler_loop())
ready_refs = [runner.runner.__ray_ready__.remote() for runner in self.runners.values()]
await asyncio.gather(*ready_refs)
self.monitor_task = asyncio.create_task(self._monitor_runner_state_loop())
self.logger.info(f"Starting Scheduler with {self.runner_num} runners")
[文档]
async def stop(self) -> None:
if not self.running:
return
self.running = False
all_running_futures = []
for futures in self.running_tasks.values():
all_running_futures.extend(futures)
if all_running_futures:
self.logger.info(f"Waiting for {len(all_running_futures)} running tasks to complete...")
await asyncio.gather(*all_running_futures, return_exceptions=True)
if self.background_tasks:
await asyncio.gather(*list(self.background_tasks), return_exceptions=True)
if self.scheduler_task:
self.scheduler_task.cancel()
try:
await self.scheduler_task
except asyncio.CancelledError:
pass
if self.monitor_task:
self.monitor_task.cancel()
try:
await self.monitor_task
except asyncio.CancelledError:
pass
self.logger.info("Scheduler stopped")
[文档]
def schedule(self, tasks: List[Task], batch_id: Union[int, str]) -> None:
"""Schedule the provided tasks.
Args:
tasks (`List[Task]`): The tasks to schedule.
batch_id (`Union[int, str]`):
The id of provided tasks. In most cases, it should be current step number for
training tasks and "<current_step_num>/<eval_taskset_name>" for eval tasks.
"""
if not tasks:
return
self.batch_is_eval_map[batch_id] = tasks[0].is_eval
self.task_num_map[batch_id] += len(tasks)
self._split_and_submit_tasks(tasks, batch_id=batch_id)
def _split_and_submit_tasks(self, tasks: List[Task], batch_id: Union[int, str]) -> None:
for i, task in enumerate(tasks):
assert task.repeat_times is not None, "Task repeat_times should not be None"
task_wrapper = TaskWrapper(
task=replace(task, batch_id=batch_id, task_id=i),
batch_id=batch_id,
total_runs=task.repeat_times,
)
if self.max_repeat_times is None:
task_wrapper.sub_task_num = 1
self.pending_tasks[batch_id].appendleft((task_wrapper, task.repeat_times, 0))
continue
sub_tasks = []
for run_id_base in range(0, task.repeat_times, self.max_repeat_times):
repeat_times = min(self.max_repeat_times, task.repeat_times - run_id_base)
sub_tasks.append((task_wrapper, repeat_times, run_id_base))
task_wrapper.sub_task_num = len(sub_tasks)
self.pending_tasks[batch_id].extendleft(sub_tasks)
[文档]
def dynamic_timeout(self, timeout: Optional[float] = None) -> float:
"""Calculate dynamic timeout based on historical data."""
max_timeout = timeout or self.default_timeout
if not self.config.explorer.dynamic_timeout.enable:
return max_timeout
if (
self.total_completed_steps < self.config.explorer.dynamic_timeout.warmup_min_steps
or self.total_completed_sub_tasks == 0
):
return max_timeout
avg_time_per_task = self.total_running_time / self.total_completed_sub_tasks
return min(
max_timeout,
avg_time_per_task * self.config.explorer.dynamic_timeout.ratio,
)
async def _cleanup_batch(
self,
batch_id: Union[int, str],
return_partial_tasks: bool = False,
restart_runners: bool = True,
) -> None:
"""Clear unfinished tasks for a batch and optionally restart associated runners."""
if return_partial_tasks:
self._emit_partial_tasks_for_batch(batch_id)
self._mark_running_tasks_for_cleanup(batch_id, restart_runners=restart_runners)
cancelled_futures = self._clear_timeout_tasks(batch_id=batch_id)
if cancelled_futures:
await asyncio.gather(*cancelled_futures, return_exceptions=True)
if not restart_runners:
return
runners_to_restart = [
runner_id
for runner_id, running_state in list(self.busy_runners.items())
if running_state.task.batch_id == batch_id
]
if runners_to_restart:
await asyncio.gather(*[self._restart_runner(rid) for rid in runners_to_restart])
def _resolve_result_target(
self, batch_id: Union[int, str], min_num: Optional[int]
) -> Tuple[int, int]:
scheduled_num = self.task_num_map.get(batch_id, 0)
if min_num is None:
return scheduled_num, scheduled_num
if min_num > scheduled_num:
self.logger.warning(
f"Requested min_num {min_num} is greater than scheduled tasks {scheduled_num} at batch_id {batch_id}. Adjusting min_num to {scheduled_num}."
)
return scheduled_num, scheduled_num
return scheduled_num, min_num
def _finalize_dynamic_timeout_step(
self, batch_id: Union[int, str], scheduled_num: int, completed_count: int
) -> None:
if batch_id in self.pending_tasks or batch_id in self.running_tasks:
return
is_eval = self.batch_is_eval_map.pop(batch_id, False)
if not is_eval and completed_count >= scheduled_num:
self.total_completed_steps += 1
async def _wait_for_batch_results(
self,
batch_id: Union[int, str],
min_num: int,
scheduled_num: int,
timeout: float,
clear_timeout_tasks: bool,
return_partial_tasks: bool,
) -> bool:
start_time = time.time()
min_threshold_reached_time = None
while time.time() - start_time <= timeout:
completed_count = len(self.completed_tasks.get(batch_id, {}))
if completed_count >= min_num:
min_threshold_reached_time = min_threshold_reached_time or time.time()
if completed_count >= scheduled_num:
return False
if (
time.time() - min_threshold_reached_time
>= self.config.explorer.over_rollout.wait_after_min
):
if clear_timeout_tasks:
await self._cleanup_batch(
batch_id,
return_partial_tasks=return_partial_tasks,
restart_runners=False,
)
return False
await asyncio.sleep(0.1)
return True
def _collect_batch_results(self, batch_id: Union[int, str]) -> Tuple[List[Status], List[bytes]]:
statuses = []
payload_chunks = []
completed_results = list(self.completed_tasks.get(batch_id, {}).values())
for result in completed_results:
statuses.append(result.status)
if result.experience_payloads:
payload_chunks.extend(result.experience_payloads)
return statuses, payload_chunks
[文档]
async def drain_batch_payload_results(
self, batch_id: Union[int, str]
) -> Tuple[List[Status], List[bytes]]:
"""Drain cached completed results for one batch."""
statuses, payload_chunks = self._collect_batch_results(batch_id)
if batch_id in self.completed_tasks:
del self.completed_tasks[batch_id]
completed_count = len(statuses)
scheduled_num = self.task_num_map.get(batch_id, 0)
self._finalize_dynamic_timeout_step(batch_id, scheduled_num, completed_count)
return statuses, payload_chunks
async def _get_batch_payload_results(
self,
batch_id: Union[int, str],
*,
min_num: Optional[int],
timeout: Optional[float],
clear_timeout_tasks: bool,
return_partial_tasks: bool,
) -> Tuple[List[Status], List[bytes]]:
"""Wait for one batch and drain its completed payload chunks."""
timeout = timeout or self.default_timeout
scheduled_num, min_num = self._resolve_result_target(batch_id, min_num)
self.logger.debug(f"Waiting for {min_num} tasks to complete...")
timed_out = await self._wait_for_batch_results(
batch_id=batch_id,
min_num=min_num,
scheduled_num=scheduled_num,
timeout=timeout,
clear_timeout_tasks=clear_timeout_tasks,
return_partial_tasks=return_partial_tasks,
)
if timed_out:
self.logger.error(
f"Timed out waiting for tasks at batch {batch_id} to complete after {timeout} seconds"
)
if clear_timeout_tasks:
await self._cleanup_batch(
batch_id,
return_partial_tasks=return_partial_tasks,
restart_runners=True,
)
completed_count = len(self.completed_tasks.get(batch_id, {}))
if completed_count < min_num:
self.logger.warning(
f"Timeout reached, only {completed_count}/{min_num} tasks completed"
)
return await self.drain_batch_payload_results(batch_id)
[文档]
async def get_payload_results(
self,
batch_id: Union[int, str],
min_num: Optional[int] = None,
timeout: Optional[float] = None,
clear_timeout_tasks: bool = True,
return_partial_tasks: bool = False,
) -> Tuple[List[Status], List[bytes]]:
"""Wait for one batch and return task statuses plus serialized payload chunks."""
return await self._get_batch_payload_results(
batch_id=batch_id,
min_num=min_num,
timeout=timeout,
clear_timeout_tasks=clear_timeout_tasks,
return_partial_tasks=return_partial_tasks,
)
[文档]
async def get_statuses(
self,
batch_id: Union[int, str],
min_num: Optional[int] = None,
timeout: Optional[float] = None,
clear_timeout_tasks: bool = True,
return_partial_tasks: bool = False,
) -> List[Status]:
"""Wait for one batch and return only task statuses without materializing experiences."""
statuses, _ = await self._get_batch_payload_results(
batch_id=batch_id,
min_num=min_num,
timeout=timeout,
clear_timeout_tasks=clear_timeout_tasks,
return_partial_tasks=return_partial_tasks,
)
return statuses
[文档]
async def abort_batch(
self,
batch_id: Union[int, str],
return_partial_tasks: bool = False,
restart_runners: bool = True,
) -> None:
"""Abort one batch and cleanup unfinished scheduler state."""
await self._cleanup_batch(
batch_id,
return_partial_tasks=return_partial_tasks,
restart_runners=restart_runners,
)
[文档]
def has_step(self, batch_id: Union[int, str]) -> bool:
return (
batch_id in self.completed_tasks
or batch_id in self.pending_tasks
or batch_id in self.running_tasks
)
[文档]
async def wait_all(
self, timeout: Optional[float] = None, clear_timeout_tasks: bool = True
) -> None:
"""Wait for all tasks to complete without poping results. If timeout reached, raise TimeoutError.
Args:
timeout (`float`): timeout in seconds. Raise `TimeoutError` when no new tasks is completed within timeout.
clear_timeout_tasks (`bool`): Whether to clear timeout tasks.
"""
timeout = timeout or self.default_timeout
start_time = time.time()
self.logger.debug("Waiting for all tasks to complete...")
last_completed_count = 0
while time.time() - start_time < timeout:
has_pending = bool(self.pending_tasks)
has_running = bool(self.running_tasks)
if not has_pending and not has_running:
self.logger.debug("All tasks completed successfully")
return
completed_count = sum(len(tasks) for tasks in self.completed_tasks.values())
if completed_count != last_completed_count:
# flush timeout when new tasks are completed
start_time = time.time()
last_completed_count = completed_count
await asyncio.sleep(0.1)
pending_count = sum(len(tasks) for tasks in self.pending_tasks.values())
running_count = sum(len(futures) for futures in self.running_tasks.values())
error_msg = f"Timeout after {timeout} seconds. Still have {pending_count} pending tasks and {running_count} running tasks."
self.logger.error(error_msg)
if clear_timeout_tasks:
batch_ids_to_abort = self.pending_tasks.keys() | self.running_tasks.keys()
for batch_id in batch_ids_to_abort:
self._clear_timeout_tasks(batch_id)
asyncio.gather(
*[self._restart_runner(runner_id) for runner_id in self.busy_runners.keys()]
)
raise TimeoutError(error_msg)
[文档]
def get_key_state(self, key: str) -> Dict:
"""Get the scheduler state.
Args:
key (`str`): The key of the state to get.
Returns:
`Dict`: A dictionary of runner ids to their state for the given key.
"""
result = {}
for runner in self.runners.values():
runner_state = runner.state
if runner_state and key in runner_state:
result[runner.runner_id] = runner_state[key]
return result
[文档]
def get_runner_state(self, runner_id: int) -> Dict:
"""Get the scheduler state.
Args:
runner_id (`int`): The id of the runner.
Returns:
`Dict`: The state of the runner.
"""
runner = self.runners.get(runner_id, None)
if runner:
return runner.state
else:
return {}
[文档]
def get_all_state(self) -> Dict:
"""Get all runners' state.
Returns:
`Dict`: The state of all runners.
"""
result = {}
for runner in self.runners.values():
runner_state = runner.state
if runner_state:
result[runner.runner_id] = runner_state
return result
[文档]
def print_all_state(self) -> None:
"""Print all runners' state in a clear, aligned table format."""
all_keys = set()
for runner in self.runners.values():
runner_state = runner.state
if runner_state:
all_keys.update(runner_state.keys())
all_keys = sorted(all_keys)
# Prepare header
header = ["runner_id"] + all_keys # type: ignore [operator]
# Prepare rows
rows = []
for runner in self.runners.values():
runner_state = runner.state or {}
row = [str(runner.runner_id)]
for key in all_keys:
value = runner_state.get(key, "-")
row.append(str(value))
rows.append(row)
# Calculate column widths
col_widths = [max(len(str(x)) for x in col) for col in zip(header, *rows)]
# Print header
header_line = " | ".join(str(h).ljust(w) for h, w in zip(header, col_widths))
self.logger.info(header_line)
self.logger.info("-+-".join("-" * w for w in col_widths))
# Print each row
for row in rows:
line = " | ".join(str(cell).ljust(w) for cell, w in zip(row, col_widths))
self.logger.info(line)
