Source code for ray.rllib.optimizers.torch_distributed_data_parallel_optimizer

import logging

import ray
from ray.rllib.optimizers.policy_optimizer import PolicyOptimizer
from ray.rllib.utils.annotations import override
from ray.rllib.utils.timer import TimerStat

logger = logging.getLogger(__name__)

[docs]class TorchDistributedDataParallelOptimizer(PolicyOptimizer): """EXPERIMENTAL: torch distributed multi-node SGD.""" def __init__(self, workers, expected_batch_size, num_sgd_iter=1, sgd_minibatch_size=0, standardize_fields=frozenset([]), keep_local_weights_in_sync=True, backend="gloo"): PolicyOptimizer.__init__(self, workers) self.learner_stats = {} self.num_sgd_iter = num_sgd_iter self.expected_batch_size = expected_batch_size self.sgd_minibatch_size = sgd_minibatch_size self.standardize_fields = standardize_fields self.keep_local_weights_in_sync = keep_local_weights_in_sync self.sync_down_timer = TimerStat() self.sync_up_timer = TimerStat() self.learn_timer = TimerStat() # Setup the distributed processes. if not self.workers.remote_workers(): raise ValueError("This optimizer requires >0 remote workers.") ip = ray.get(workers.remote_workers()[0].get_node_ip.remote()) port = ray.get(workers.remote_workers()[0].find_free_port.remote()) address = "tcp://{ip}:{port}".format(ip=ip, port=port) "Creating torch process group with leader {}".format(address)) # Get setup tasks in order to throw errors on failure. ray.get([ worker.setup_torch_data_parallel.remote( address, i, len(workers.remote_workers()), backend) for i, worker in enumerate(workers.remote_workers()) ])"Torch process group init completed")
[docs] @override(PolicyOptimizer) def step(self): # Sync up the weights. In principle we don't need this, but it doesn't # add too much overhead and handles the case where the user manually # updates the local weights. if self.keep_local_weights_in_sync: with self.sync_up_timer: weights = ray.put(self.workers.local_worker().get_weights()) for e in self.workers.remote_workers(): e.set_weights.remote(weights) with self.learn_timer: results = ray.get([ w.sample_and_learn.remote( self.expected_batch_size, self.num_sgd_iter, self.sgd_minibatch_size, self.standardize_fields) for w in self.workers.remote_workers() ]) for info, count in results: self.num_steps_sampled += count self.num_steps_trained += count self.learner_stats = results[0][0] # In debug mode, check the allreduce successfully synced the weights. if logger.isEnabledFor(logging.DEBUG): weights = ray.get([ w.get_weights.remote() for w in self.workers.remote_workers() ]) sums = [] for w in weights: acc = 0 for p in w.values(): for k, v in p.items(): acc += v.sum() sums.append(float(acc)) logger.debug("The worker weight sums are {}".format(sums)) assert len(set(sums)) == 1, sums # Sync down the weights. As with the sync up, this is not really # needed unless the user is reading the local weights. if self.keep_local_weights_in_sync: with self.sync_down_timer: self.workers.local_worker().set_weights( ray.get( self.workers.remote_workers()[0].get_weights.remote())) return self.learner_stats
[docs] @override(PolicyOptimizer) def stats(self): return dict( PolicyOptimizer.stats(self), **{ "sync_weights_up_time": round(1000 * self.sync_up_timer.mean, 3), "sync_weights_down_time": round( 1000 * self.sync_down_timer.mean, 3), "learn_time_ms": round(1000 * self.learn_timer.mean, 3), "learner": self.learner_stats, })