chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,632 @@
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import atexit
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import logging
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import queue
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import threading
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import weakref
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from queue import Queue
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from typing import Any, Dict, List
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import ray
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from ray.rllib.algorithms.impala.impala import LEARNER_RESULTS_CURR_ENTROPY_COEFF_KEY
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from ray.rllib.core import COMPONENT_RL_MODULE
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from ray.rllib.core.learner.learner import Learner
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from ray.rllib.core.learner.training_data import TrainingData
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from ray.rllib.core.rl_module.apis import ValueFunctionAPI
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from ray.rllib.utils.annotations import (
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OverrideToImplementCustomLogic_CallToSuperRecommended,
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override,
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)
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from ray.rllib.utils.framework import try_import_torch
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from ray.rllib.utils.lambda_defaultdict import LambdaDefaultDict
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from ray.rllib.utils.metrics import (
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ALL_MODULES,
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NUM_ENV_STEPS_SAMPLED_LIFETIME,
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)
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from ray.rllib.utils.metrics.metrics_logger import MetricsLogger
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from ray.rllib.utils.metrics.ray_metrics import (
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DEFAULT_HISTOGRAM_BOUNDARIES_SHORT_EVENTS,
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TimerAndPrometheusLogger,
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)
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from ray.rllib.utils.schedules.scheduler import Scheduler
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from ray.rllib.utils.typing import ModuleID, ResultDict
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from ray.util.metrics import Gauge, Histogram
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logger = logging.getLogger(__name__)
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torch, _ = try_import_torch()
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GPU_LOADER_QUEUE_WAIT_TIMER = "gpu_loader_queue_wait_timer"
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GPU_LOADER_LOAD_TO_GPU_TIMER = "gpu_loader_load_to_gpu_timer"
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LEARNER_THREAD_IN_QUEUE_WAIT_TIMER = "learner_thread_in_queue_wait_timer"
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LEARNER_THREAD_ENV_STEPS_DROPPED = "learner_thread_env_steps_dropped"
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LEARNER_THREAD_UPDATE_TIMER = "learner_thread_update_timer"
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RAY_GET_EPISODES_TIMER = "ray_get_episodes_timer"
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QUEUE_SIZE_GPU_LOADER_QUEUE = "queue_size_gpu_loader_queue"
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QUEUE_SIZE_LEARNER_THREAD_QUEUE = "queue_size_learner_thread_queue"
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QUEUE_SIZE_RESULTS_QUEUE = "queue_size_results_queue"
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# Aggregation cycle size.
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BATCHES_PER_AGGREGATION = 10
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# Stop sentinel for the `_LearnerThread`
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_STOP_SENTINEL = object()
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class IMPALALearner(Learner):
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@override(Learner)
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# Ray metrics
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self._metrics_learner_impala_update = Histogram(
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name="rllib_learner_impala_update_time",
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description="Time spent in the 'IMPALALearner.update()' method.",
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boundaries=DEFAULT_HISTOGRAM_BOUNDARIES_SHORT_EVENTS,
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tag_keys=("rllib",),
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)
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self._metrics_learner_impala_update.set_default_tags(
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{"rllib": self.__class__.__name__}
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)
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self._metrics_learner_impala_update_solve_refs = Histogram(
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name="rllib_learner_impala_update_solve_refs_time",
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description="Time spent on resolving refs in the 'Learner.update()'",
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boundaries=DEFAULT_HISTOGRAM_BOUNDARIES_SHORT_EVENTS,
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tag_keys=("rllib",),
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)
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self._metrics_learner_impala_update_solve_refs.set_default_tags(
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{"rllib": self.__class__.__name__}
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)
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self._metrics_learner_impala_update_make_batch_if_necessary = Histogram(
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name="rllib_learner_impala_update_make_batch_if_necessary_time",
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description="Time spent on making a batch in the 'Learner.update()'.",
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boundaries=DEFAULT_HISTOGRAM_BOUNDARIES_SHORT_EVENTS,
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tag_keys=("rllib",),
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)
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self._metrics_learner_impala_update_make_batch_if_necessary.set_default_tags(
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{"rllib": self.__class__.__name__}
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)
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self._metrics_learner_impala_get_learner_state_time = Histogram(
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name="rllib_learner_impala_get_learner_state_time",
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description="Time spent on get_state() in IMPALALearner.update().",
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boundaries=DEFAULT_HISTOGRAM_BOUNDARIES_SHORT_EVENTS,
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tag_keys=("rllib",),
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)
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self._metrics_learner_impala_get_learner_state_time.set_default_tags(
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{"rllib": self.__class__.__name__}
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)
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# Set the aggregation threshold to the broadcast interval. We return
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# a state at the same time the metrics are aggregated.
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global BATCHES_PER_AGGREGATION
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BATCHES_PER_AGGREGATION = self.config.broadcast_interval
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@override(Learner)
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def build(self) -> None:
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super().build()
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# APPO/IMPALA require RLock for thread safety around metrics.
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self.metrics._threading_lock = threading.RLock()
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# Aggregation signaling (replaces condition-variable contention) ---
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self._agg_event = threading.Event()
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self._submitted_updates = 0 # producer-side counter (update thread(s))
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self._num_updates = 0 # learner-side counter
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self._num_updates_lock = threading.Lock()
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# Set the update kwargs passed in the main thread for use in the learner thread.
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self._update_kwargs = {}
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self._model_io_lock = threading.RLock()
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self._learner_state_queue = Queue(maxsize=1)
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self._learner_state_lock = threading.Lock()
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self._learner_state = None
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# Dict mapping module IDs to entropy Scheduler instances.
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self.entropy_coeff_schedulers_per_module: Dict[
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ModuleID, Scheduler
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] = LambdaDefaultDict(
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lambda module_id: Scheduler(
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fixed_value_or_schedule=(
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self.config.get_config_for_module(module_id).entropy_coeff
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),
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framework=self.framework,
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device=self._device,
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)
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)
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# Create queues as bounded queues to create real back-pressure & stabilize
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# GPU memory usage.
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# Small loader in-queue to keep threads busy without flooding.
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# TODO (simon): Do extensive testing to find an optimal queue size.
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loader_qsize = max(2, 10 * self.config.num_gpu_loader_threads)
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# Note, we are passing now the timesteps dictionary through the queue.
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self._gpu_loader_in_queue: "Queue[tuple[TrainingData, Dict[str, Any]]]" = Queue(
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maxsize=loader_qsize
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)
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# Learner in-queue must be tiny. 1 strictly serializes GPU-resident batches.
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# TODO (simon): Add a parameter to define queue size.
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if not hasattr(self, "_learner_thread_in_queue"):
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self._learner_thread_in_queue: "Queue[tuple[Any, Dict[str, Any]]]" = Queue(
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maxsize=self.config.learner_queue_size
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)
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# Get the rank of this learner, if necessary.
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self._rank: int = (
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torch.distributed.get_rank() if torch.distributed.is_initialized() else 0
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)
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# Define the out-queue for the metrics from the `_LearnerThread`.
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# TODO (simon): Add types for items.
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self._learner_thread_out_queue: "Queue[Dict[str, Any]]" = Queue()
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# Create and start `_GPULoaderThread`(s).
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if self.config.num_gpus_per_learner > 0:
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self._gpu_loader_threads: List[threading.Thread] = [
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_GPULoaderThread(
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in_queue=self._gpu_loader_in_queue,
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out_queue=self._learner_thread_in_queue,
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device=self._device,
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metrics_logger=self.metrics,
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)
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for _ in range(self.config.num_gpu_loader_threads)
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]
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for t in self._gpu_loader_threads:
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t.start()
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# Create and start the `_LearnerThread`.
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self._learner_thread: threading.Thread = _LearnerThread(
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update_method=Learner.update,
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in_queue=self._learner_thread_in_queue,
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out_queue=self._learner_thread_out_queue,
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learner=self,
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)
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self._learner_thread.start()
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@override(Learner)
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def update(
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self,
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training_data: TrainingData,
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*,
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timesteps: Dict[str, Any],
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return_state: bool = False,
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**kwargs,
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) -> ResultDict:
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"""
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Args:
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batch:
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timesteps:
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return_state: Whether to include one of the Learner worker's state from
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after the update step in the returned results dict (under the
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`_rl_module_state_after_update` key). Note that after an update, all
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Learner workers' states should be identical, so we use the first
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Learner's state here. Useful for avoiding an extra `get_weights()` call,
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e.g. for synchronizing EnvRunner weights.
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**kwargs:
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Returns:
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"""
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# Set the update kwargs passed in the main thread for use in the learner thread.
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self._update_kwargs = kwargs
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with TimerAndPrometheusLogger(self._metrics_learner_impala_update):
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# Get the train batch from the object store.
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with TimerAndPrometheusLogger(
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self._metrics_learner_impala_update_solve_refs
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):
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# Resolve object refs and ensure we have a proper batch object.
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# TODO (simon): Check, if we can resolve the object references and
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# run the pipeline on the GPULoaderThreads.
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training_data.solve_refs()
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with TimerAndPrometheusLogger(
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self._metrics_learner_impala_update_make_batch_if_necessary
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):
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batch = self._make_batch_if_necessary(training_data=training_data)
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assert batch is not None
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# Enqeue the batch (bounded backpressure).
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if self.config.num_gpus_per_learner > 0:
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# Pass timesteps alongside batch (no globals).
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self._gpu_loader_in_queue.put((batch, timesteps))
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# Only occasionally log loader queue size.
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if (self._submitted_updates & 0xFF) == 0:
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self.metrics.log_value(
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(ALL_MODULES, QUEUE_SIZE_GPU_LOADER_QUEUE),
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self._gpu_loader_in_queue.qsize(),
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window=1,
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)
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# TODO (simon): Check, if we want to get here stats from the
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# RingBuffer.
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else:
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# No GPU loader: directly enqueue to learner queue.
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_LearnerThread.enqueue(
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self._learner_thread_in_queue, (batch, timesteps), self.metrics
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)
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# Return the module state, if requested and available.
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if return_state:
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try:
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with self._learner_state_lock:
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self._learner_state = self._learner_state_queue.get_nowait()
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except queue.Empty:
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logger.debug("No learner state available in the queue yet.")
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# Every 20th block call we submit results. Otherwise we keep the
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# thread running without interruption to avoid thread contention.
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self._submitted_updates += 1
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if (self._submitted_updates % BATCHES_PER_AGGREGATION) != 0:
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result = {}
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if return_state and self._learner_state:
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result["_rl_module_state_after_update"] = self._learner_state
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return result
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# Result submission: wait until learner finished BATCHES_PER_AGGREGATION updates (blocking).
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self._agg_event.wait()
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# Reset the aggregation event to keep the `_LearnerThread` running.
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self._agg_event.clear()
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if self._learner_thread_out_queue:
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try:
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result = self._learner_thread_out_queue.get(timeout=0.001)
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except queue.Empty:
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result = {}
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# Return the module state, if requested and existent.
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if return_state and self._learner_state:
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result["_rl_module_state_after_update"] = self._learner_state
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return result
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@OverrideToImplementCustomLogic_CallToSuperRecommended
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def before_gradient_based_update(self, *, timesteps: Dict[str, Any]) -> None:
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super().before_gradient_based_update(timesteps=timesteps)
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for module_id in self.module.keys():
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# Update entropy coefficient via our Scheduler.
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new_entropy_coeff = self.entropy_coeff_schedulers_per_module[
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module_id
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].update(timestep=timesteps.get(NUM_ENV_STEPS_SAMPLED_LIFETIME, 0))
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self.metrics.log_value(
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(module_id, LEARNER_RESULTS_CURR_ENTROPY_COEFF_KEY),
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new_entropy_coeff,
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window=1,
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)
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@override(Learner)
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def remove_module(self, module_id: str):
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super().remove_module(module_id)
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self.entropy_coeff_schedulers_per_module.pop(module_id)
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@override(Learner)
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def shutdown(self) -> None:
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# Stop the learner thread deterministically: setting the stop event
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# and enqueuing a sentinel wakes the consumer if it's blocked on
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# `_in_queue.get()`. Then `join` ensures it has fully exited before
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# we return, so any subsequent `ray.shutdown()`/interpreter teardown
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# can't race with the daemon thread.
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thread = getattr(self, "_learner_thread", None)
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if thread is not None and thread.is_alive():
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thread.request_stop()
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thread.join(timeout=5.0)
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@classmethod
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@override(Learner)
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def rl_module_required_apis(cls) -> list[type]:
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# In order for a PPOLearner to update an RLModule, it must implement the
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# following APIs:
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return [ValueFunctionAPI]
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ImpalaLearner = IMPALALearner
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class _GPULoaderThread(threading.Thread):
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def __init__(
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self,
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*,
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in_queue: "Queue[tuple[TrainingData, Dict[str, Any]]]",
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out_queue: "Queue[tuple[Any, Dict[str, Any]]]",
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device: "torch.device",
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metrics_logger: MetricsLogger,
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):
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super().__init__(name="_GPULoaderThread")
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self.daemon = True
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self._in_queue = in_queue
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self._out_queue = out_queue
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self._device = device
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self.metrics = metrics_logger
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# Use a single CUDA stream for each loader thread.
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self._use_cuda_stream = (
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torch is not None
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and hasattr(torch, "cuda")
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and device is not None
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and getattr(device, "type", None) == "cuda"
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)
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self._stream = (
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torch.cuda.Stream(device=self._device) if self._use_cuda_stream else None
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)
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self._metrics_impala_gpu_loader_thread_step_time = Histogram(
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name="rllib_learner_impala_gpu_loader_thread_step_time",
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description="Time taken in seconds for gpu loader thread _step.",
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boundaries=DEFAULT_HISTOGRAM_BOUNDARIES_SHORT_EVENTS,
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tag_keys=("rllib",),
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)
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self._metrics_impala_gpu_loader_thread_step_time.set_default_tags(
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{"rllib": "IMPALA/GPULoaderThread"}
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)
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self._metrics_impala_gpu_loader_thread_step_in_queue_get_time = Histogram(
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name="rllib_learner_impala_gpu_loader_thread_step_get_time",
|
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description="Time taken in seconds for gpu loader thread _step _in_queue.get().",
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||||
boundaries=DEFAULT_HISTOGRAM_BOUNDARIES_SHORT_EVENTS,
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tag_keys=("rllib",),
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)
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self._metrics_impala_gpu_loader_thread_step_in_queue_get_time.set_default_tags(
|
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{"rllib": "IMPALA/GPULoaderThread"}
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)
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self._metrics_impala_gpu_loader_thread_step_load_to_gpu_time = Histogram(
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name="rllib_learner_impala_gpu_loader_thread_step_load_to_gpu_time",
|
||||
description="Time taken in seconds for GPU loader thread _step to load batch to GPU.",
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||||
boundaries=DEFAULT_HISTOGRAM_BOUNDARIES_SHORT_EVENTS,
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||||
tag_keys=("rllib",),
|
||||
)
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self._metrics_impala_gpu_loader_thread_step_load_to_gpu_time.set_default_tags(
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{"rllib": "IMPALA/GPULoaderThread"}
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)
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self._metrics_impala_gpu_loader_thread_in_qsize_beginning_of_step = Gauge(
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name="rllib_impala_gpu_loader_thread_in_qsize_beginning_of_step",
|
||||
description="Size of the _GPULoaderThread in-queue size, at the beginning of the step.",
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||||
tag_keys=("rllib",),
|
||||
)
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self._metrics_impala_gpu_loader_thread_in_qsize_beginning_of_step.set_default_tags(
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{"rllib": "IMPALA/GPULoaderThread"}
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)
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# Robust pinned-memory copy: fall back if batch contains CUDA tensors already.
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# TODO (simon): Find a more compliant solution.
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def _to_device_safe(self, batch):
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try:
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return batch.to_device(self._device, pin_memory=True)
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except RuntimeError as e:
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msg = str(e)
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if "only dense CPU tensors can be pinned" in msg or "pin_memory" in msg:
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return batch.to_device(self._device, pin_memory=False)
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raise
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def run(self) -> None:
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||||
while True:
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with TimerAndPrometheusLogger(
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self._metrics_impala_gpu_loader_thread_step_time
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||||
):
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self._step()
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||||
def _step(self) -> None:
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self._metrics_impala_gpu_loader_thread_in_qsize_beginning_of_step.set(
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value=self._in_queue.qsize()
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)
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# Get a new batch (CPU) and the global timesteps from the loader in--queue (blocking).
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||||
with self.metrics.log_time((ALL_MODULES, GPU_LOADER_QUEUE_WAIT_TIMER)):
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||||
with TimerAndPrometheusLogger(
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self._metrics_impala_gpu_loader_thread_step_in_queue_get_time
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||||
):
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ma_batch_on_cpu, timesteps = self._in_queue.get()
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||||
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||||
# Load the batch onto the GPU device; enable pinned memory for async copies.
|
||||
with self.metrics.log_time((ALL_MODULES, GPU_LOADER_LOAD_TO_GPU_TIMER)):
|
||||
if self._use_cuda_stream and self._stream is not None:
|
||||
# Issue copies on a non-default stream so they can overlap with compute.
|
||||
with torch.cuda.stream(self._stream):
|
||||
ma_batch_on_gpu = self._to_device_safe(ma_batch_on_cpu)
|
||||
# TODO (simon): Maybe use the `use_stream` in `convert_to_tensor`.
|
||||
# No explicit synching here. Consumer will naturally serialize when needed.
|
||||
else:
|
||||
ma_batch_on_gpu = self._to_device_safe(ma_batch_on_cpu)
|
||||
|
||||
# Enqueue to Learner thread’s in-queue (GPU-resident batch and global timesteps).
|
||||
_LearnerThread.enqueue(
|
||||
self._out_queue, (ma_batch_on_gpu, timesteps), self.metrics
|
||||
)
|
||||
|
||||
|
||||
class _LearnerThread(threading.Thread):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
update_method,
|
||||
in_queue: "Queue[tuple[Any, Dict[str, Any]]]",
|
||||
out_queue: "Queue[Dict[str, Any]]",
|
||||
learner: IMPALALearner,
|
||||
):
|
||||
super().__init__(name="_LearnerThread")
|
||||
self.daemon = True
|
||||
self.learner = learner
|
||||
self._update_method = update_method
|
||||
# Note, we pass now the timesteps dictionary through the queue.
|
||||
self._in_queue: "queue.Queue[tuple[Any, Dict[str, Any]]]" = in_queue
|
||||
# TODO (simon): Type hints.
|
||||
self._out_queue = out_queue
|
||||
self._stop_event = threading.Event()
|
||||
|
||||
# Ray metrics
|
||||
self._metrics_learner_impala_thread_step = Histogram(
|
||||
name="rllib_learner_impala_learner_thread_step_time",
|
||||
description="Time taken in seconds for learner thread _step.",
|
||||
boundaries=DEFAULT_HISTOGRAM_BOUNDARIES_SHORT_EVENTS,
|
||||
tag_keys=("rllib",),
|
||||
)
|
||||
self._metrics_learner_impala_thread_step.set_default_tags(
|
||||
{"rllib": "IMPALA/LearnerThread"}
|
||||
)
|
||||
|
||||
self._metrics_learner_impala_thread_step_update = Histogram(
|
||||
name="rllib_learner_impala_learner_thread_step_update_time",
|
||||
description="Time taken in seconds for learner thread _step update.",
|
||||
boundaries=DEFAULT_HISTOGRAM_BOUNDARIES_SHORT_EVENTS,
|
||||
tag_keys=("rllib",),
|
||||
)
|
||||
self._metrics_learner_impala_thread_step_update.set_default_tags(
|
||||
{"rllib": "IMPALA/LearnerThread"}
|
||||
)
|
||||
|
||||
# Stop cleanly at interpreter shutdown so the daemon thread doesn't
|
||||
# get killed mid-call inside an auto_init-wrapped Ray API (which
|
||||
# would otherwise trigger e.g. `start_reaper` -> `preexec_fn not
|
||||
# supported at interpreter shutdown`). Use a weakref so this hook
|
||||
# doesn't pin the thread (and therefore the Learner) alive.
|
||||
weak_self = weakref.ref(self)
|
||||
|
||||
def _request_stop_at_exit():
|
||||
t = weak_self()
|
||||
if t is not None:
|
||||
t.request_stop()
|
||||
|
||||
atexit.register(_request_stop_at_exit)
|
||||
|
||||
# Keeps compatibility, but thread-safe.
|
||||
@property
|
||||
def stopped(self) -> bool:
|
||||
return self._stop_event.is_set()
|
||||
|
||||
# Call this to stop the thread and wake it if it's blocked on .get()
|
||||
def request_stop(self) -> None:
|
||||
self._stop_event.set()
|
||||
# Wake the consumer if it's blocked on an empty queue
|
||||
try:
|
||||
self._in_queue.put_nowait(_STOP_SENTINEL)
|
||||
except queue.Full:
|
||||
# If the queue is full, the consumer will wake soon anyway.
|
||||
logger.warning(
|
||||
"_LearnerThread.request_stop(): in_queue is full; cannot enqueue stop sentinel."
|
||||
)
|
||||
|
||||
def run(self) -> None:
|
||||
while True:
|
||||
# Returns always `True` until stop-signal/sentinel is sent.
|
||||
if not self.step():
|
||||
break
|
||||
|
||||
def step(self) -> bool:
|
||||
# Get a batch and wait, if the input queue is empty (blocking; no polling).
|
||||
with self.learner.metrics.log_time(
|
||||
(ALL_MODULES, LEARNER_THREAD_IN_QUEUE_WAIT_TIMER)
|
||||
):
|
||||
item = self._in_queue.get()
|
||||
|
||||
# Handle the stop/sentinel signal(s).
|
||||
# TODO (simon): Check, if we need `None` for belt-and-suspenders/comp.
|
||||
if item is _STOP_SENTINEL or self.stopped:
|
||||
try:
|
||||
self._in_queue.task_done()
|
||||
except Exception:
|
||||
logger.warning(
|
||||
"_LearnerThread._in_queue.task_done() failed during stop handling."
|
||||
)
|
||||
# Signal `run` to exit.
|
||||
return False
|
||||
|
||||
# Extract the multi-agent batch and the timesteps dictionary.
|
||||
ma_batch_on_gpu, timesteps = item
|
||||
|
||||
# Update the `RLModule`, but do not reduce metrics.
|
||||
with self.learner.metrics.log_time((ALL_MODULES, LEARNER_THREAD_UPDATE_TIMER)):
|
||||
with TimerAndPrometheusLogger(
|
||||
self._metrics_learner_impala_thread_step_update
|
||||
):
|
||||
self._update_method(
|
||||
self=self.learner,
|
||||
training_data=TrainingData(batch=ma_batch_on_gpu),
|
||||
timesteps=timesteps,
|
||||
_no_metrics_reduce=True,
|
||||
# Include the learner update kwargs set in the main thread.
|
||||
**self.learner._update_kwargs,
|
||||
)
|
||||
|
||||
# Signal queue done (unblocks producer’s put when bounded)
|
||||
try:
|
||||
self._in_queue.task_done()
|
||||
finally:
|
||||
# Set the Aggregation counter and signal this event (atomic).
|
||||
with self.learner._num_updates_lock:
|
||||
self.learner._num_updates += 1
|
||||
# Check, if we need to aggregate.
|
||||
do_agg = self.learner._num_updates == BATCHES_PER_AGGREGATION
|
||||
if do_agg:
|
||||
# Reset the update counter inside the lock.
|
||||
self.learner._num_updates = 0
|
||||
|
||||
# If we need to aggregate, reduce metrics and queue them.
|
||||
if do_agg:
|
||||
# If in multi-learner setup, safeguard state retrieval within barriers.
|
||||
if torch.distributed.is_initialized():
|
||||
torch.distributed.barrier()
|
||||
# Only the first rank retrieves the state.
|
||||
if self.learner._rank == 0:
|
||||
with self.learner._model_io_lock, torch.inference_mode():
|
||||
learner_state = self.learner.get_state(
|
||||
# Only return the state of those RLModules that are trainable.
|
||||
components=[
|
||||
COMPONENT_RL_MODULE + "/" + mid
|
||||
for mid in self.learner.module.keys()
|
||||
if self.learner.should_module_be_updated(mid)
|
||||
],
|
||||
inference_only=True,
|
||||
)
|
||||
learner_state[COMPONENT_RL_MODULE] = ray.put(
|
||||
learner_state[COMPONENT_RL_MODULE]
|
||||
)
|
||||
try:
|
||||
if (self.learner._submitted_updates & ~0xFF) != (
|
||||
(self.learner._submitted_updates - BATCHES_PER_AGGREGATION)
|
||||
& ~0xFF
|
||||
):
|
||||
with self.learner._learner_state_lock:
|
||||
self.learner.metrics.log_value(
|
||||
(ALL_MODULES, "learner_thread_state_queue_size"),
|
||||
self.learner._learner_state_queue.qsize(),
|
||||
window=1,
|
||||
)
|
||||
# Remove any old learner state in the queue.
|
||||
self.learner._learner_state_queue.get_nowait()
|
||||
except queue.Empty:
|
||||
logger.debug("No old learner state to remove from the queue.")
|
||||
|
||||
# Pass the learner state into the queue to the main process.
|
||||
self.learner._learner_state_queue.put_nowait(learner_state)
|
||||
self.learner.metrics.log_value(
|
||||
(ALL_MODULES, "learner_thread_out_queue_size"),
|
||||
self._out_queue.qsize(),
|
||||
window=1,
|
||||
)
|
||||
|
||||
# Reduce metrics and pass them into the queue for the main process.
|
||||
self._out_queue.put(self.learner.metrics.reduce())
|
||||
# Notify all listeners that aggregation is done and results can be
|
||||
# retrieved.
|
||||
self.learner._agg_event.set()
|
||||
if torch.distributed.is_initialized():
|
||||
torch.distributed.barrier()
|
||||
|
||||
# Keep running (see `run` method).
|
||||
return True
|
||||
|
||||
@staticmethod
|
||||
def enqueue(
|
||||
learner_queue: "queue.Queue[tuple[Any, Dict[str, Any]]]",
|
||||
batch_with_ts,
|
||||
metrics: MetricsLogger,
|
||||
):
|
||||
# Put the batch into the queue (blocking if thread is updating).
|
||||
learner_queue.put(batch_with_ts, block=True)
|
||||
Reference in New Issue
Block a user