Source code for torchrl.collectors.distributed.rpc
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""Generic distributed data-collector using torch.distributed.rpc backend."""
from __future__ import annotations
import collections
import os
import socket
import time
import warnings
from copy import copy, deepcopy
from typing import Callable, List, OrderedDict
from torchrl._utils import logger as torchrl_logger
from torchrl.collectors.distributed import DEFAULT_SLURM_CONF
from torchrl.collectors.distributed.default_configs import (
DEFAULT_TENSORPIPE_OPTIONS,
IDLE_TIMEOUT,
TCP_PORT,
)
from torchrl.collectors.utils import split_trajectories
from torchrl.data.utils import CloudpickleWrapper
from torchrl.envs.utils import _convert_exploration_type
SUBMITIT_ERR = None
try:
import submitit
_has_submitit = True
except ModuleNotFoundError as err:
_has_submitit = False
SUBMITIT_ERR = err
import torch.cuda
from tensordict import TensorDict
from torch import nn
from torch.distributed import rpc
from torchrl._utils import _ProcessNoWarn, VERBOSE
from torchrl.collectors import MultiaSyncDataCollector
from torchrl.collectors.collectors import (
DataCollectorBase,
DEFAULT_EXPLORATION_TYPE,
MultiSyncDataCollector,
SyncDataCollector,
)
from torchrl.envs.common import EnvBase
from torchrl.envs.env_creator import EnvCreator
def _rpc_init_collection_node(
rank,
rank0_ip,
tcp_port,
world_size,
visible_device,
tensorpipe_options,
verbose=VERBOSE,
):
os.environ["MASTER_ADDR"] = str(rank0_ip)
os.environ["MASTER_PORT"] = str(tcp_port)
if isinstance(visible_device, list):
pass
elif isinstance(visible_device, (str, int, torch.device)):
visible_device = [visible_device]
elif visible_device is None:
pass
else:
raise RuntimeError(f"unrecognised dtype {type(visible_device)}")
options = rpc.TensorPipeRpcBackendOptions(
devices=visible_device,
**tensorpipe_options,
)
if verbose:
torchrl_logger.info(
f"init rpc with master addr: {os.environ['MASTER_ADDR']}:{os.environ['MASTER_PORT']}"
)
rpc.init_rpc(
f"COLLECTOR_NODE_{rank}",
rank=rank,
backend=rpc.BackendType.TENSORPIPE,
rpc_backend_options=options,
world_size=world_size,
)
rpc.shutdown()
[docs]class RPCDataCollector(DataCollectorBase):
"""An RPC-based distributed data collector.
Supports sync and async data collection.
Args:
create_env_fn (Callable or List[Callabled]): list of Callables, each returning an
instance of :class:`~torchrl.envs.EnvBase`.
policy (Callable): Policy to be executed in the environment.
Must accept :class:`tensordict.tensordict.TensorDictBase` object as input.
If ``None`` is provided, the policy used will be a
:class:`~torchrl.collectors.RandomPolicy` instance with the environment
``action_spec``.
Accepted policies are usually subclasses of :class:`~tensordict.nn.TensorDictModuleBase`.
This is the recommended usage of the collector.
Other callables are accepted too:
If the policy is not a ``TensorDictModuleBase`` (e.g., a regular :class:`~torch.nn.Module`
instances) it will be wrapped in a `nn.Module` first.
Then, the collector will try to assess if these
modules require wrapping in a :class:`~tensordict.nn.TensorDictModule` or not.
- If the policy forward signature matches any of ``forward(self, tensordict)``,
``forward(self, td)`` or ``forward(self, <anything>: TensorDictBase)`` (or
any typing with a single argument typed as a subclass of ``TensorDictBase``)
then the policy won't be wrapped in a :class:`~tensordict.nn.TensorDictModule`.
- In all other cases an attempt to wrap it will be undergone as such: ``TensorDictModule(policy, in_keys=env_obs_key, out_keys=env.action_keys)``.
Keyword Args:
frames_per_batch (int): A keyword-only argument representing the total
number of elements in a batch.
total_frames (int): A keyword-only argument representing the total
number of frames returned by the collector
during its lifespan. If the ``total_frames`` is not divisible by
``frames_per_batch``, an exception is raised.
Endless collectors can be created by passing ``total_frames=-1``.
Defaults to ``-1`` (endless collector).
device (int, str or torch.device, optional): The generic device of the
collector. The ``device`` args fills any non-specified device: if
``device`` is not ``None`` and any of ``storing_device``, ``policy_device`` or
``env_device`` is not specified, its value will be set to ``device``.
Defaults to ``None`` (No default device).
Lists of devices are supported.
storing_device (int, str or torch.device, optional): The *remote* device on which
the output :class:`~tensordict.TensorDict` will be stored.
If ``device`` is passed and ``storing_device`` is ``None``, it will
default to the value indicated by ``device``.
For long trajectories, it may be necessary to store the data on a different
device than the one where the policy and env are executed.
Defaults to ``None`` (the output tensordict isn't on a specific device,
leaf tensors sit on the device where they were created).
Lists of devices are supported.
env_device (int, str or torch.device, optional): The *remote* device on which
the environment should be cast (or executed if that functionality is
supported). If not specified and the env has a non-``None`` device,
``env_device`` will default to that value. If ``device`` is passed
and ``env_device=None``, it will default to ``device``. If the value
as such specified of ``env_device`` differs from ``policy_device``
and one of them is not ``None``, the data will be cast to ``env_device``
before being passed to the env (i.e., passing different devices to
policy and env is supported). Defaults to ``None``.
Lists of devices are supported.
policy_device (int, str or torch.device, optional): The *remote* device on which
the policy should be cast.
If ``device`` is passed and ``policy_device=None``, it will default
to ``device``. If the value as such specified of ``policy_device``
differs from ``env_device`` and one of them is not ``None``,
the data will be cast to ``policy_device`` before being passed to
the policy (i.e., passing different devices to policy and env is
supported). Defaults to ``None``.
Lists of devices are supported.
max_frames_per_traj (int, optional): Maximum steps per trajectory.
Note that a trajectory can span across multiple batches (unless
``reset_at_each_iter`` is set to ``True``, see below).
Once a trajectory reaches ``n_steps``, the environment is reset.
If the environment wraps multiple environments together, the number
of steps is tracked for each environment independently. Negative
values are allowed, in which case this argument is ignored.
Defaults to ``None`` (i.e., no maximum number of steps).
init_random_frames (int, optional): Number of frames for which the
policy is ignored before it is called. This feature is mainly
intended to be used in offline/model-based settings, where a
batch of random trajectories can be used to initialize training.
If provided, it will be rounded up to the closest multiple of frames_per_batch.
Defaults to ``None`` (i.e. no random frames).
reset_at_each_iter (bool, optional): Whether environments should be reset
at the beginning of a batch collection.
Defaults to ``False``.
postproc (Callable, optional): A post-processing transform, such as
a :class:`~torchrl.envs.Transform` or a :class:`~torchrl.data.postprocs.MultiStep`
instance.
Defaults to ``None``.
split_trajs (bool, optional): Boolean indicating whether the resulting
TensorDict should be split according to the trajectories.
See :func:`~torchrl.collectors.utils.split_trajectories` for more
information.
Defaults to ``False``.
exploration_type (ExplorationType, optional): interaction mode to be used when
collecting data. Must be one of ``torchrl.envs.utils.ExplorationType.DETERMINISTIC``,
``torchrl.envs.utils.ExplorationType.RANDOM``, ``torchrl.envs.utils.ExplorationType.MODE``
or ``torchrl.envs.utils.ExplorationType.MEAN``.
Defaults to ``torchrl.envs.utils.ExplorationType.RANDOM``.
collector_class (type or str, optional): a collector class for the remote node. Can be
:class:`~torchrl.collectors.SyncDataCollector`,
:class:`~torchrl.collectors.MultiSyncDataCollector`,
:class:`~torchrl.collectors.MultiaSyncDataCollector`
or a derived class of these. The strings "single", "sync" and
"async" correspond to respective class.
Defaults to :class:`~torchrl.collectors.SyncDataCollector`.
.. note::
Support for :class:`MultiSyncDataCollector` and :class:`MultiaSyncDataCollector`
is experimental, and :class:`~torchrl.collectors.SyncDataCollector`
should always be preferred. If multiple simultaneous environment
need to be executed on a single node, consider using a
:class:`~torchrl.envs.ParallelEnv` instance.
collector_kwargs (dict or list, optional): a dictionary of parameters to be passed to the
remote data-collector. If a list is provided, each element will
correspond to an individual set of keyword arguments for the
dedicated collector.
num_workers_per_collector (int, optional): the number of copies of the
env constructor that is to be used on the remote nodes.
Defaults to 1 (a single env per collector).
On a single worker node all the sub-workers will be
executing the same environment. If different environments need to
be executed, they should be dispatched across worker nodes, not
subnodes.
sync (bool, optional): if ``True``, the resulting tensordict is a stack of all the
tensordicts collected on each node. If ``False`` (default), each
tensordict results from a separate node in a "first-ready,
first-served" fashion.
slurm_kwargs (dict): a dictionary of parameters to be passed to the
submitit executor.
update_after_each_batch (bool, optional): if ``True``, the weights will
be updated after each collection. For ``sync=True``, this means that
all workers will see their weights updated. For ``sync=False``,
only the worker from which the data has been gathered will be
updated.
Defaults to ``False``, ie. updates have to be executed manually
through
:meth:`~torchrl.collectors.distributed.DistributedDataCollector.update_policy_weights_`.
max_weight_update_interval (int, optional): the maximum number of
batches that can be collected before the policy weights of a worker
is updated.
For sync collections, this parameter is overwritten by ``update_after_each_batch``.
For async collections, it may be that one worker has not seen its
parameters being updated for a certain time even if ``update_after_each_batch``
is turned on.
Defaults to -1 (no forced update).
launcher (str, optional): how jobs should be launched.
Can be one of "submitit" or "mp" for multiprocessing. The former
can launch jobs across multiple nodes, whilst the latter will only
launch jobs on a single machine. "submitit" requires the homonymous
library to be installed.
To find more about submitit, visit
https://github.com/facebookincubator/submitit
Defaults to "submitit".
tcp_port (int, optional): the TCP port to be used. Defaults to 10003.
visible_devices (list of Union[int, torch.device, str], optional): a
list of the same length as the number of nodes containing the
device used to pass data to main.
tensorpipe_options (dict, optional): a dictionary of keyword argument
to pass to :class:`torch.distributed.rpc.TensorPipeRpcBackendOption`.
"""
_VERBOSE = VERBOSE # for debugging
def __init__(
self,
create_env_fn,
policy,
*,
frames_per_batch: int,
total_frames: int = -1,
device: torch.device | List[torch.device] = None,
storing_device: torch.device | List[torch.device] = None,
env_device: torch.device | List[torch.device] = None,
policy_device: torch.device | List[torch.device] = None,
max_frames_per_traj: int = -1,
init_random_frames: int = -1,
reset_at_each_iter: bool = False,
postproc: Callable | None = None,
split_trajs: bool = False,
exploration_type: "ExporationType" = DEFAULT_EXPLORATION_TYPE, # noqa
exploration_mode: str = None,
collector_class=SyncDataCollector,
collector_kwargs=None,
num_workers_per_collector=1,
sync=False,
slurm_kwargs=None,
update_after_each_batch=False,
max_weight_update_interval=-1,
launcher="submitit",
tcp_port=None,
visible_devices=None,
tensorpipe_options=None,
):
exploration_type = _convert_exploration_type(
exploration_mode=exploration_mode, exploration_type=exploration_type
)
if collector_class == "async":
collector_class = MultiaSyncDataCollector
elif collector_class == "sync":
collector_class = MultiSyncDataCollector
elif collector_class == "single":
collector_class = SyncDataCollector
self.collector_class = collector_class
self.env_constructors = create_env_fn
self.policy = policy
if isinstance(policy, nn.Module):
policy_weights = TensorDict(dict(policy.named_parameters()), [])
else:
policy_weights = TensorDict({}, [])
self.policy_weights = policy_weights
self.num_workers = len(create_env_fn)
self.frames_per_batch = frames_per_batch
self.device = device
self.storing_device = storing_device
self.env_device = env_device
self.policy_device = policy_device
self.storing_device = storing_device
# make private to avoid changes from users during collection
self._sync = sync
self.update_after_each_batch = update_after_each_batch
self.max_weight_update_interval = max_weight_update_interval
if self.update_after_each_batch and self.max_weight_update_interval > -1:
raise RuntimeError(
"Got conflicting udpate instructions: `update_after_each_batch` "
"`max_weight_update_interval` are incompatible."
)
self.launcher = launcher
self._batches_since_weight_update = [0 for _ in range(self.num_workers)]
if tcp_port is None:
self.tcp_port = os.environ.get("TCP_PORT", TCP_PORT)
else:
self.tcp_port = str(tcp_port)
self.visible_devices = visible_devices
if self._sync:
if self.frames_per_batch % self.num_workers != 0:
raise RuntimeError(
f"Cannot dispatch {self.frames_per_batch} frames across {self.num_workers}. "
f"Consider using a number of frames per batch that is divisible by the number of workers."
)
self._frames_per_batch_corrected = self.frames_per_batch // self.num_workers
else:
self._frames_per_batch_corrected = self.frames_per_batch
self.num_workers_per_collector = num_workers_per_collector
self.total_frames = total_frames
self.slurm_kwargs = copy(DEFAULT_SLURM_CONF)
if slurm_kwargs is not None:
self.slurm_kwargs.update(slurm_kwargs)
collector_kwargs = collector_kwargs if collector_kwargs is not None else {}
self.collector_kwargs = (
deepcopy(collector_kwargs)
if isinstance(collector_kwargs, (list, tuple))
else [copy(collector_kwargs) for _ in range(self.num_workers)]
)
# update collector kwargs
for i, collector_kwarg in enumerate(self.collector_kwargs):
collector_kwarg["max_frames_per_traj"] = max_frames_per_traj
collector_kwarg["init_random_frames"] = (
init_random_frames // self.num_workers
)
if not self._sync and init_random_frames > 0:
warnings.warn(
"async distributed data collection with init_random_frames > 0 "
"may have unforeseen consequences as we do not control that once "
"non-random data is being collected all nodes are returning non-random data. "
"If this is a feature that you feel should be fixed, please raise an issue on "
"torchrl's repo."
)
collector_kwarg["reset_at_each_iter"] = reset_at_each_iter
collector_kwarg["exploration_type"] = exploration_type
collector_kwarg["device"] = self.device[i]
collector_kwarg["storing_device"] = self.storing_device[i]
collector_kwarg["env_device"] = self.env_device[i]
collector_kwarg["policy_device"] = self.policy_device[i]
self.postproc = postproc
self.split_trajs = split_trajs
if tensorpipe_options is None:
self.tensorpipe_options = copy(DEFAULT_TENSORPIPE_OPTIONS)
else:
self.tensorpipe_options = copy(DEFAULT_TENSORPIPE_OPTIONS).update(
tensorpipe_options
)
self._init()
@property
def device(self) -> List[torch.device]:
return self._device
@property
def storing_device(self) -> List[torch.device]:
return self._storing_device
@property
def env_device(self) -> List[torch.device]:
return self._env_device
@property
def policy_device(self) -> List[torch.device]:
return self._policy_device
@device.setter
def device(self, value):
if isinstance(value, (tuple, list)):
if len(value) != self.num_workers:
raise RuntimeError(
"The number of devices passed to the collector must match the number of workers."
)
self._device = value
else:
self._device = [value] * self.num_workers
@storing_device.setter
def storing_device(self, value):
if isinstance(value, (tuple, list)):
if len(value) != self.num_workers:
raise RuntimeError(
"The number of devices passed to the collector must match the number of workers."
)
self._storing_device = value
else:
self._storing_device = [value] * self.num_workers
@env_device.setter
def env_device(self, value):
if isinstance(value, (tuple, list)):
if len(value) != self.num_workers:
raise RuntimeError(
"The number of devices passed to the collector must match the number of workers."
)
self._env_device = value
else:
self._env_device = [value] * self.num_workers
@policy_device.setter
def policy_device(self, value):
if isinstance(value, (tuple, list)):
if len(value) != self.num_workers:
raise RuntimeError(
"The number of devices passed to the collector must match the number of workers."
)
self._policy_device = value
else:
self._policy_device = [value] * self.num_workers
def _init_master_rpc(
self,
world_size,
):
"""Init RPC on main node."""
options = rpc.TensorPipeRpcBackendOptions(**self.tensorpipe_options)
if torch.cuda.device_count():
if self.visible_devices:
for i in range(self.num_workers):
rank = i + 1
options.set_device_map(
f"COLLECTOR_NODE_{rank}", {0: self.visible_devices[i]}
)
if self._VERBOSE:
torchrl_logger.info("init rpc")
rpc.init_rpc(
"TRAINER_NODE",
rank=0,
backend=rpc.BackendType.TENSORPIPE,
rpc_backend_options=options,
world_size=world_size,
)
def _start_workers(
self,
world_size,
env_constructors,
collector_class,
num_workers_per_collector,
policy,
frames_per_batch,
total_frames,
collector_kwargs,
):
"""Instantiate remote collectors."""
num_workers = world_size - 1
time_interval = 1.0
collector_infos = []
for i in range(num_workers):
counter = 0
while True:
counter += 1
time.sleep(time_interval)
try:
if self._VERBOSE:
torchrl_logger.info(
f"trying to connect to collector node {i + 1}"
)
collector_info = rpc.get_worker_info(f"COLLECTOR_NODE_{i + 1}")
break
except RuntimeError as err:
if counter * time_interval > self.tensorpipe_options["rpc_timeout"]:
raise RuntimeError("Could not connect to remote node") from err
continue
collector_infos.append(collector_info)
collector_rrefs = []
for i in range(num_workers):
env_make = env_constructors[i]
if not isinstance(env_make, (EnvBase, EnvCreator)):
env_make = CloudpickleWrapper(env_make)
if self._VERBOSE:
torchrl_logger.info("Making collector in remote node")
collector_rref = rpc.remote(
collector_infos[i],
collector_class,
args=(
[env_make] * num_workers_per_collector
if collector_class is not SyncDataCollector
else env_make,
policy,
),
kwargs={
"frames_per_batch": frames_per_batch,
"total_frames": -1,
"split_trajs": False,
**collector_kwargs[i],
},
)
collector_rrefs.append(collector_rref)
futures = collections.deque(maxlen=self.num_workers)
if not self._sync:
for i in range(num_workers):
if self._VERBOSE:
torchrl_logger.info("Asking for the first batch")
future = rpc.rpc_async(
collector_infos[i],
collector_class.next,
args=(collector_rrefs[i],),
)
futures.append((future, i))
self.futures = futures
self.collector_rrefs = collector_rrefs
self.collector_infos = collector_infos
def _init_worker_rpc(self, executor, i):
"""Init RPC node if necessary."""
visible_device = (
self.visible_devices[i] if self.visible_devices is not None else None
)
if self.launcher == "submitit":
if not _has_submitit:
raise ImportError("submitit not found.") from SUBMITIT_ERR
job = executor.submit(
_rpc_init_collection_node,
i + 1,
self.IPAddr,
self.tcp_port,
self.num_workers + 1,
visible_device,
self.tensorpipe_options,
self._VERBOSE,
)
if self._VERBOSE:
torchrl_logger.info(f"job id {job.job_id}") # ID of your job
return job
elif self.launcher == "mp":
job = _ProcessNoWarn(
target=_rpc_init_collection_node,
args=(
i + 1,
self.IPAddr,
self.tcp_port,
self.num_workers + 1,
visible_device,
self.tensorpipe_options,
self._VERBOSE,
),
)
job.start()
return job
elif self.launcher == "submitit_delayed":
# job is already launched
return None
else:
raise NotImplementedError(f"Unknown launcher {self.launcher}")
def _init(self):
self._shutdown = False
if self.launcher == "submitit":
executor = submitit.AutoExecutor(folder="log_test")
executor.update_parameters(**self.slurm_kwargs)
else:
executor = None
hostname = socket.gethostname()
if self.launcher != "mp":
IPAddr = socket.gethostbyname(hostname)
else:
IPAddr = "localhost"
self.IPAddr = IPAddr
os.environ["MASTER_ADDR"] = str(self.IPAddr)
os.environ["MASTER_PORT"] = str(self.tcp_port)
self.jobs = []
for i in range(self.num_workers):
if self._VERBOSE:
torchrl_logger.info(f"Submitting job {i}")
job = self._init_worker_rpc(
executor,
i,
)
self.jobs.append(job)
self._init_master_rpc(
self.num_workers + 1,
)
self._start_workers(
world_size=self.num_workers + 1,
env_constructors=self.env_constructors,
collector_class=self.collector_class,
num_workers_per_collector=self.num_workers_per_collector,
policy=self.policy,
frames_per_batch=self._frames_per_batch_corrected,
total_frames=self.total_frames,
collector_kwargs=self.collector_kwargs,
)
def iterator(self):
self._collected_frames = 0
while self._collected_frames < self.total_frames:
if self._sync:
data = self._next_sync_rpc()
else:
data = self._next_async_rpc()
if self.split_trajs:
data = split_trajectories(data)
if self.postproc is not None:
data = self.postproc(data)
yield data
if self.max_weight_update_interval > -1 and not self._sync:
for j in range(self.num_workers):
if (
self._batches_since_weight_update[j]
> self.max_weight_update_interval
):
self.update_policy_weights_([j], wait=False)
elif self.max_weight_update_interval > -1:
ranks = [
1
for j in range(self.num_workers)
if self._batches_since_weight_update[j]
> self.max_weight_update_interval
]
self.update_policy_weights_(ranks, wait=True)
[docs] def update_policy_weights_(self, workers=None, wait=True) -> None:
if workers is None:
workers = list(range(self.num_workers))
futures = []
for i in workers:
if self._VERBOSE:
torchrl_logger.info(f"calling update on worker {i}")
futures.append(
rpc.rpc_async(
self.collector_infos[i],
self.collector_class.update_policy_weights_,
args=(self.collector_rrefs[i], self.policy_weights.detach()),
)
)
if wait:
for i in workers:
if self._VERBOSE:
torchrl_logger.info(f"waiting for worker {i}")
futures[i].wait()
if self._VERBOSE:
torchrl_logger.info("got it!")
def _next_async_rpc(self):
if self._VERBOSE:
torchrl_logger.info("next async")
if not len(self.futures):
raise StopIteration(
f"The queue is empty, the collector has ran out of data after {self._collected_frames} collected frames."
)
while True:
future, i = self.futures.popleft()
if future.done():
if self.update_after_each_batch:
self.update_policy_weights_(workers=(i,), wait=False)
if self._VERBOSE:
torchrl_logger.info(f"future {i} is done")
data = future.value()
self._collected_frames += data.numel()
if self._collected_frames < self.total_frames:
future = rpc.rpc_async(
self.collector_infos[i],
self.collector_class.next,
args=(self.collector_rrefs[i],),
)
self.futures.append((future, i))
return data
self.futures.append((future, i))
def _next_sync_rpc(self):
if self._VERBOSE:
torchrl_logger.info("next sync: futures")
if self.update_after_each_batch:
self.update_policy_weights_()
for i in range(self.num_workers):
future = rpc.rpc_async(
self.collector_infos[i],
self.collector_class.next,
args=(self.collector_rrefs[i],),
)
self.futures.append((future, i))
data = []
while len(self.futures):
future, i = self.futures.popleft()
# the order is NOT guaranteed: should we change that?
if future.done():
data += [future.value()]
if self._VERBOSE:
torchrl_logger.info(
f"got data from {i} // data has len {len(data)} / {self.num_workers}"
)
else:
self.futures.append((future, i))
data = torch.cat(data)
traj_ids = data.get(("collector", "traj_ids"), None)
if traj_ids is not None:
for i in range(1, self.num_workers):
traj_ids[i] += traj_ids[i - 1].max()
data.set_(("collector", "traj_ids"), traj_ids)
self._collected_frames += data.numel()
return data
def set_seed(self, seed: int, static_seed: bool = False) -> int:
for worker in self.collector_infos:
seed = rpc.rpc_sync(worker, self.collector_class.set_seed, args=(seed,))
def state_dict(self) -> OrderedDict:
raise NotImplementedError
def load_state_dict(self, state_dict: OrderedDict) -> None:
raise NotImplementedError
def shutdown(self):
if not hasattr(self, "_shutdown"):
warnings.warn("shutdown has no effect has `_init` has not been called yet.")
return
if self._shutdown:
return
if self._VERBOSE:
torchrl_logger.info("shutting down")
for future, i in self.futures:
# clear the futures
while future is not None and not future.done():
torchrl_logger.info(f"waiting for proc {i} to clear")
future.wait()
for i in range(self.num_workers):
if self._VERBOSE:
torchrl_logger.info(f"shutting down {i}")
rpc.rpc_sync(
self.collector_infos[i],
self.collector_class.shutdown,
args=(self.collector_rrefs[i],),
timeout=int(IDLE_TIMEOUT),
)
if self._VERBOSE:
torchrl_logger.info("rpc shutdown")
rpc.shutdown(timeout=int(IDLE_TIMEOUT))
if self.launcher == "mp":
for job in self.jobs:
job.join(int(IDLE_TIMEOUT))
elif self.launcher == "submitit":
for job in self.jobs:
_ = job.result()
elif self.launcher == "submitit_delayed":
pass
else:
raise NotImplementedError(f"Unknown launcher {self.launcher}")
self._shutdown = True
