batched_envs.py

# 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.

from __future__ import annotations

import os
from collections import OrderedDict
from copy import deepcopy
from functools import wraps
from multiprocessing import connection
from multiprocessing.synchronize import Lock as MpLock
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
from warnings import warn

import torch

from tensordict import TensorDict
from tensordict._tensordict import _unravel_key_to_tuple, unravel_key
from tensordict.tensordict import LazyStackedTensorDict, TensorDictBase
from torch import multiprocessing as mp
from torchrl._utils import _check_for_faulty_process, _ProcessNoWarn, VERBOSE
from torchrl.data.utils import CloudpickleWrapper, contains_lazy_spec, DEVICE_TYPING
from torchrl.envs.common import EnvBase
from torchrl.envs.env_creator import get_env_metadata

from torchrl.envs.utils import (
    _aggregate_end_of_traj,
    _set_single_key,
    _sort_keys,
    _update_during_reset,
    clear_mpi_env_vars,
)

# legacy
from .libs.envpool import MultiThreadedEnv, MultiThreadedEnvWrapper  # noqa: F401


def _check_start(fun):
    def decorated_fun(self: _BatchedEnv, *args, **kwargs):
        if self.is_closed:
            self._create_td()
            self._start_workers()
        else:
            if isinstance(self, ParallelEnv):
                _check_for_faulty_process(self._workers)
        return fun(self, *args, **kwargs)

    return decorated_fun


class _dispatch_caller_parallel:
    def __init__(self, attr, parallel_env):
        self.attr = attr
        self.parallel_env = parallel_env

    def __call__(self, *args, **kwargs):
        # remove self from args
        args = [_arg if _arg is not self.parallel_env else "_self" for _arg in args]
        for channel in self.parallel_env.parent_channels:
            channel.send((self.attr, (args, kwargs)))

        results = []
        for channel in self.parallel_env.parent_channels:
            msg, result = channel.recv()
            results.append(result)

        return results

    def __iter__(self):
        # if the object returned is not a callable
        return iter(self.__call__())


class _dispatch_caller_serial:
    def __init__(self, list_callable: List[Callable, Any]):
        self.list_callable = list_callable

    def __call__(self, *args, **kwargs):
        return [_callable(*args, **kwargs) for _callable in self.list_callable]


def lazy_property(prop: property):
    """Converts a property in a lazy property, that will call _set_properties when queried the first time."""
    return property(fget=lazy(prop.fget), fset=prop.fset)


def lazy(fun):
    """Converts a fun in a lazy fun, that will call _set_properties when queried the first time."""

    @wraps(fun)
    def new_fun(self, *args, **kwargs):
        if not self._properties_set:
            self._set_properties()
        return fun(self, *args, **kwargs)

    return new_fun


class _BatchedEnv(EnvBase):
    """Batched environments allow the user to query an arbitrary method / attribute of the environment running remotely.

    Those queries will return a list of length equal to the number of workers containing the
    values resulting from those queries.
        >>> env = ParallelEnv(3, my_env_fun)
        >>> custom_attribute_list = env.custom_attribute
        >>> custom_method_list = env.custom_method(*args)

    Args:
        num_workers: number of workers (i.e. env instances) to be deployed simultaneously;
        create_env_fn (callable or list of callables): function (or list of functions) to be used for the environment
            creation.
            If a single task is used, a callable should be used and not a list of identical callables:
            if a list of callable is provided, the environment will be executed as if multiple, diverse tasks were
            needed, which comes with a slight compute overhead;
        create_env_kwargs (dict or list of dicts, optional): kwargs to be used with the environments being created;
        share_individual_td (bool, optional): if ``True``, a different tensordict is created for every process/worker and a lazy
            stack is returned.
            default = None (False if single task);
        shared_memory (bool): whether or not the returned tensordict will be placed in shared memory;
        memmap (bool): whether or not the returned tensordict will be placed in memory map.
        policy_proof (callable, optional): if provided, it'll be used to get the list of
            tensors to return through the :obj:`step()` and :obj:`reset()` methods, such as :obj:`"hidden"` etc.
        device (str, int, torch.device): for consistency, this argument is kept. However this
            argument should not be passed, as the device will be inferred from the environments.
            It is assumed that all environments will run on the same device as a common shared
            tensordict will be used to pass data from process to process. The device can be
            changed after instantiation using :obj:`env.to(device)`.
        num_threads (int, optional): number of threads for this process.
            Defaults to the number of workers.
            This parameter has no effect for the :class:`~SerialEnv` class.
        num_sub_threads (int, optional): number of threads of the subprocesses.
            Should be equal to one plus the number of processes launched within
            each subprocess (or one if a single process is launched).
            Defaults to 1 for safety: if none is indicated, launching multiple
            workers may charge the cpu load too much and harm performance.
            This parameter has no effect for the :class:`~SerialEnv` class.

    """

    _verbose: bool = VERBOSE
    _excluded_wrapped_keys = [
        "is_closed",
        "parent_channels",
        "batch_size",
        "_dummy_env_str",
    ]

    def __init__(
        self,
        num_workers: int,
        create_env_fn: Union[Callable[[], EnvBase], Sequence[Callable[[], EnvBase]]],
        create_env_kwargs: Union[dict, Sequence[dict]] = None,
        pin_memory: bool = False,
        share_individual_td: Optional[bool] = None,
        shared_memory: bool = True,
        memmap: bool = False,
        policy_proof: Optional[Callable] = None,
        device: Optional[DEVICE_TYPING] = None,
        allow_step_when_done: bool = False,
        num_threads: int = None,
        num_sub_threads: int = 1,
    ):
        if device is not None:
            raise ValueError(
                "Device setting for batched environment can't be done at initialization. "
                "The device will be inferred from the constructed environment. "
                "It can be set through the `to(device)` method."
            )

        super().__init__(device=None)
        self.is_closed = True
        if num_threads is None:
            num_threads = num_workers + 1  # 1 more thread for this proc
        self.num_sub_threads = num_sub_threads
        self.num_threads = num_threads
        self._cache_in_keys = None

        self._single_task = callable(create_env_fn) or (len(set(create_env_fn)) == 1)
        if callable(create_env_fn):
            create_env_fn = [create_env_fn for _ in range(num_workers)]
        else:
            if len(create_env_fn) != num_workers:
                raise RuntimeError(
                    f"num_workers and len(create_env_fn) mismatch, "
                    f"got {len(create_env_fn)} and {num_workers}"
                )
            if (
                share_individual_td is False and not self._single_task
            ):  # then it has been explicitly set by the user
                raise ValueError(
                    "share_individual_td must be set to None or True when using multi-task batched environments"
                )
            share_individual_td = True
        create_env_kwargs = {} if create_env_kwargs is None else create_env_kwargs
        if isinstance(create_env_kwargs, dict):
            create_env_kwargs = [
                deepcopy(create_env_kwargs) for _ in range(num_workers)
            ]

        self.policy_proof = policy_proof
        self.num_workers = num_workers
        self.create_env_fn = create_env_fn
        self.create_env_kwargs = create_env_kwargs
        self.pin_memory = pin_memory
        if pin_memory:
            raise ValueError("pin_memory for batched envs is deprecated")

        self.share_individual_td = bool(share_individual_td)
        self._share_memory = shared_memory
        self._memmap = memmap
        self.allow_step_when_done = allow_step_when_done
        if allow_step_when_done:
            raise ValueError("allow_step_when_done is deprecated")
        if self._share_memory and self._memmap:
            raise RuntimeError(
                "memmap and shared memory are mutually exclusive features."
            )
        self._batch_size = None
        self._device = None
        self._dummy_env_str = None
        self._seeds = None
        self.__dict__["_input_spec"] = None
        self.__dict__["_output_spec"] = None
        # self._prepare_dummy_env(create_env_fn, create_env_kwargs)
        self._properties_set = False
        self._get_metadata(create_env_fn, create_env_kwargs)

    def _get_metadata(
        self, create_env_fn: List[Callable], create_env_kwargs: List[Dict]
    ):
        if self._single_task:
            # if EnvCreator, the metadata are already there
            meta_data = get_env_metadata(create_env_fn[0], create_env_kwargs[0])
            self.meta_data = meta_data.expand(
                *(self.num_workers, *meta_data.batch_size)
            )
        else:
            n_tasks = len(create_env_fn)
            self.meta_data = []
            for i in range(n_tasks):
                self.meta_data.append(
                    get_env_metadata(create_env_fn[i], create_env_kwargs[i]).clone()
                )
        self._set_properties()

    def update_kwargs(self, kwargs: Union[dict, List[dict]]) -> None:
        """Updates the kwargs of each environment given a dictionary or a list of dictionaries.

        Args:
            kwargs (dict or list of dict): new kwargs to use with the environments

        """
        if isinstance(kwargs, dict):
            for _kwargs in self.create_env_kwargs:
                _kwargs.update(kwargs)
        else:
            for _kwargs, _new_kwargs in zip(self.create_env_kwargs, kwargs):
                _kwargs.update(_new_kwargs)

    def _get_in_keys_to_exclude(self, tensordict):
        if self._cache_in_keys is None:
            self._cache_in_keys = list(
                set(self.input_spec.keys(True)).intersection(
                    tensordict.keys(True, True)
                )
            )
        return self._cache_in_keys

    def _set_properties(self):
        meta_data = self.meta_data
        self._properties_set = True
        if self._single_task:
            self._batch_size = meta_data.batch_size
            device = self._device = meta_data.device

            input_spec = meta_data.specs["input_spec"].to(device)
            output_spec = meta_data.specs["output_spec"].to(device)

            self.action_spec = input_spec["full_action_spec"]
            self.state_spec = input_spec["full_state_spec"]
            self.observation_spec = output_spec["full_observation_spec"]
            self.reward_spec = output_spec["full_reward_spec"]
            self.done_spec = output_spec["full_done_spec"]

            self._dummy_env_str = meta_data.env_str
            self._env_tensordict = meta_data.tensordict
            self._batch_locked = meta_data.batch_locked
        else:
            self._batch_size = torch.Size([self.num_workers, *meta_data[0].batch_size])
            device = self._device = meta_data[0].device
            # TODO: check that all action_spec and reward spec match (issue #351)

            input_spec = []
            for md in meta_data:
                input_spec.append(md.specs["input_spec"])
            input_spec = torch.stack(input_spec, 0)
            output_spec = []
            for md in meta_data:
                output_spec.append(md.specs["output_spec"])
            output_spec = torch.stack(output_spec, 0)

            self.action_spec = input_spec["full_action_spec"]
            self.state_spec = input_spec["full_state_spec"]

            self.observation_spec = output_spec["full_observation_spec"]
            self.reward_spec = output_spec["full_reward_spec"]
            self.done_spec = output_spec["full_done_spec"]

            self._dummy_env_str = str(meta_data[0])
            self._env_tensordict = torch.stack(
                [meta_data.tensordict for meta_data in meta_data], 0
            )
            self._batch_locked = meta_data[0].batch_locked
        self.has_lazy_inputs = contains_lazy_spec(self.input_spec)

    def state_dict(self) -> OrderedDict:
        raise NotImplementedError

    def load_state_dict(self, state_dict: OrderedDict) -> None:
        raise NotImplementedError

    batch_size = lazy_property(EnvBase.batch_size)
    device = lazy_property(EnvBase.device)
    input_spec = lazy_property(EnvBase.input_spec)
    output_spec = lazy_property(EnvBase.output_spec)

    def _create_td(self) -> None:
        """Creates self.shared_tensordict_parent, a TensorDict used to store the most recent observations."""
        if self._single_task:
            shared_tensordict_parent = self._env_tensordict.clone()
            if not self._env_tensordict.shape[0] == self.num_workers:
                raise RuntimeError(
                    "batched environment base tensordict has the wrong shape"
                )
        else:
            shared_tensordict_parent = self._env_tensordict.clone()

        if self._single_task:
            self._env_input_keys = sorted(
                list(self.input_spec["full_action_spec"].keys(True, True))
                + list(self.state_spec.keys(True, True)),
                key=_sort_keys,
            )
            self._env_output_keys = []
            self._env_obs_keys = []
            for key in self.output_spec["full_observation_spec"].keys(True, True):
                self._env_output_keys.append(key)
                self._env_obs_keys.append(key)
            self._env_output_keys += self.reward_keys + self.done_keys
        else:
            env_input_keys = set()
            for meta_data in self.meta_data:
                if meta_data.specs["input_spec", "full_state_spec"] is not None:
                    env_input_keys = env_input_keys.union(
                        meta_data.specs["input_spec", "full_state_spec"].keys(
                            True, True
                        )
                    )
                env_input_keys = env_input_keys.union(
                    meta_data.specs["input_spec", "full_action_spec"].keys(True, True)
                )
            env_output_keys = set()
            env_obs_keys = set()
            for meta_data in self.meta_data:
                env_obs_keys = env_obs_keys.union(
                    key
                    for key in meta_data.specs["output_spec"][
                        "full_observation_spec"
                    ].keys(True, True)
                )
                env_output_keys = env_output_keys.union(
                    meta_data.specs["output_spec"]["full_observation_spec"].keys(
                        True, True
                    )
                )
            env_output_keys = env_output_keys.union(self.reward_keys + self.done_keys)
            self._env_obs_keys = sorted(env_obs_keys, key=_sort_keys)
            self._env_input_keys = sorted(env_input_keys, key=_sort_keys)
            self._env_output_keys = sorted(env_output_keys, key=_sort_keys)

        reset_keys = self.reset_keys
        self._selected_keys = (
            set(self._env_output_keys)
            .union(self._env_input_keys)
            .union(self._env_obs_keys)
            .union(set(self.done_keys))
        )
        self._selected_keys = self._selected_keys.union(reset_keys)

        # input keys
        self._selected_input_keys = {
            _unravel_key_to_tuple(key) for key in self._env_input_keys
        }
        # output keys after reset
        self._selected_reset_keys = {
            _unravel_key_to_tuple(key)
            for key in self._env_obs_keys + self.done_keys + reset_keys
        }
        # output keys after reset, filtered
        self._selected_reset_keys_filt = {
            unravel_key(key) for key in self._env_obs_keys + self.done_keys
        }
        # output keys after step
        self._selected_step_keys = {
            _unravel_key_to_tuple(key) for key in self._env_output_keys
        }

        if self._single_task:
            shared_tensordict_parent = shared_tensordict_parent.select(
                *self._selected_keys,
                *(unravel_key(("next", key)) for key in self._env_output_keys),
                strict=False,
            )
            self.shared_tensordict_parent = shared_tensordict_parent.to(self.device)
        else:
            # Multi-task: we share tensordict that *may* have different keys
            shared_tensordict_parent = [
                tensordict.select(
                    *self._selected_keys,
                    *(unravel_key(("next", key)) for key in self._env_output_keys),
                    strict=False,
                ).to(self.device)
                for tensordict in shared_tensordict_parent
            ]
            shared_tensordict_parent = torch.stack(
                shared_tensordict_parent,
                0,
            )
            self.shared_tensordict_parent = shared_tensordict_parent

        if self.share_individual_td:
            if not isinstance(self.shared_tensordict_parent, LazyStackedTensorDict):
                self.shared_tensordicts = [
                    td.clone() for td in self.shared_tensordict_parent.unbind(0)
                ]
                self.shared_tensordict_parent = torch.stack(self.shared_tensordicts, 0)
            else:
                # Multi-task: we share tensordict that *may* have different keys
                # LazyStacked already stores this so we don't need to do anything
                self.shared_tensordicts = self.shared_tensordict_parent
            if self.device.type == "cpu":
                if self._share_memory:
                    for td in self.shared_tensordicts:
                        td.share_memory_()
                elif self._memmap:
                    for td in self.shared_tensordicts:
                        td.memmap_()
        else:
            if self._share_memory:
                self.shared_tensordict_parent.share_memory_()
                if not self.shared_tensordict_parent.is_shared():
                    raise RuntimeError("share_memory_() failed")
            elif self._memmap:
                self.shared_tensordict_parent.memmap_()
                if not self.shared_tensordict_parent.is_memmap():
                    raise RuntimeError("memmap_() failed")
            self.shared_tensordicts = self.shared_tensordict_parent.unbind(0)
        # we cache all the keys of the shared parent td for future use. This is
        # safe since the td is locked.
        self._cache_shared_keys = set(self.shared_tensordict_parent.keys(True, True))

    def _start_workers(self) -> None:
        """Starts the various envs."""
        raise NotImplementedError

    def __repr__(self) -> str:
        if self._dummy_env_str is None:
            self._dummy_env_str = self._set_properties()
        return (
            f"{self.__class__.__name__}("
            f"\n\tenv={self._dummy_env_str}, "
            f"\n\tbatch_size={self.batch_size})"
        )

    def close(self) -> None:
        if self.is_closed:
            raise RuntimeError("trying to close a closed environment")
        if self._verbose:
            print(f"closing {self.__class__.__name__}")

        self.__dict__["_input_spec"] = None
        self.__dict__["_output_spec"] = None
        self._properties_set = False
        self.event = None

        self._shutdown_workers()
        self.is_closed = True

    def _shutdown_workers(self) -> None:
        raise NotImplementedError

    def _set_seed(self, seed: Optional[int]):
        """This method is not used in batched envs."""
        pass

    @lazy
    def start(self) -> None:
        if not self.is_closed:
            raise RuntimeError("trying to start a environment that is not closed.")
        self._create_td()
        self._start_workers()

    def to(self, device: DEVICE_TYPING):
        device = torch.device(device)
        if device == self.device:
            return self
        self._device = device
        self.meta_data = (
            self.meta_data.to(device)
            if self._single_task
            else [meta_data.to(device) for meta_data in self.meta_data]
        )
        if not self.is_closed:
            warn(
                "Casting an open environment to another device requires closing and re-opening it. "
                "This may have unexpected and unwanted effects (e.g. on seeding etc.)"
            )
            # the tensordicts must be re-created on device
            super().to(device)
            self.close()
            self.start()
        else:
            if self.__dict__["_input_spec"] is not None:
                self.__dict__["_input_spec"] = self.__dict__["_input_spec"].to(device)
            if self.__dict__["_output_spec"] is not None:
                self.__dict__["_output_spec"] = self.__dict__["_output_spec"].to(device)
        return self


class SerialEnv(_BatchedEnv):
    """Creates a series of environments in the same process."""

    __doc__ += _BatchedEnv.__doc__

    _share_memory = False

    def _start_workers(self) -> None:
        _num_workers = self.num_workers

        self._envs = []

        for idx in range(_num_workers):
            env = self.create_env_fn[idx](**self.create_env_kwargs[idx])
            self._envs.append(env.to(self.device))
        self.is_closed = False

    @_check_start
    def state_dict(self) -> OrderedDict:
        state_dict = OrderedDict()
        for idx, env in enumerate(self._envs):
            state_dict[f"worker{idx}"] = env.state_dict()

        return state_dict

    @_check_start
    def load_state_dict(self, state_dict: OrderedDict) -> None:
        if "worker0" not in state_dict:
            state_dict = OrderedDict(
                **{f"worker{idx}": state_dict for idx in range(self.num_workers)}
            )
        for idx, env in enumerate(self._envs):
            env.load_state_dict(state_dict[f"worker{idx}"])

    def _shutdown_workers(self) -> None:
        if not self.is_closed:
            for env in self._envs:
                env.close()
            del self._envs

    @_check_start
    def set_seed(
        self, seed: Optional[int] = None, static_seed: bool = False
    ) -> Optional[int]:
        for env in self._envs:
            new_seed = env.set_seed(seed, static_seed=static_seed)
            seed = new_seed
        return seed

    @_check_start
    def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase:
        if tensordict is not None:
            needs_resetting = _aggregate_end_of_traj(
                tensordict, reset_keys=self.reset_keys
            )
            if needs_resetting.ndim > 2:
                needs_resetting = needs_resetting.flatten(1, needs_resetting.ndim - 1)
            if needs_resetting.ndim > 1:
                needs_resetting = needs_resetting.any(-1)
            elif not needs_resetting.ndim:
                needs_resetting = needs_resetting.expand((self.num_workers,))
        else:
            needs_resetting = torch.ones(
                (self.num_workers,), device=self.device, dtype=torch.bool
            )

        for i, _env in enumerate(self._envs):
            if not needs_resetting[i]:
                continue
            if tensordict is not None:
                tensordict_ = tensordict[i]
                if tensordict_.is_empty():
                    tensordict_ = None
                else:
                    # reset will do modifications in-place. We want the original
                    # tensorict to be unchaned, so we clone it
                    tensordict_ = tensordict_.clone(False)
            else:
                tensordict_ = None
            _td = _env.reset(tensordict=tensordict_, **kwargs)
            self.shared_tensordicts[i].update_(
                _td.select(*self._selected_reset_keys_filt, strict=False)
            )
        selected_output_keys = self._selected_reset_keys_filt
        if self._single_task:
            # select + clone creates 2 tds, but we can create one only
            out = TensorDict(
                {}, batch_size=self.shared_tensordict_parent.shape, device=self.device
            )
            for key in selected_output_keys:
                _set_single_key(self.shared_tensordict_parent, out, key, clone=True)
            return out
        else:
            return self.shared_tensordict_parent.select(
                *selected_output_keys,
                strict=False,
            ).clone()

    def _reset_proc_data(self, tensordict, tensordict_reset):
        # since we call `reset` directly, all the postproc has been completed
        if tensordict is not None:
            return _update_during_reset(tensordict_reset, tensordict, self.reset_keys)
        return tensordict_reset

    @_check_start
    def _step(
        self,
        tensordict: TensorDict,
    ) -> TensorDict:
        tensordict_in = tensordict.clone(False)
        next_td = self.shared_tensordict_parent.get("next")
        for i in range(self.num_workers):
            # shared_tensordicts are locked, and we need to select the keys since we update in-place.
            # There may be unexpected keys, such as "_reset", that we should comfortably ignore here.
            out_td = self._envs[i]._step(tensordict_in[i])
            next_td[i].update_(out_td.select(*self._env_output_keys, strict=False))
        # We must pass a clone of the tensordict, as the values of this tensordict
        # will be modified in-place at further steps
        if self._single_task:
            out = TensorDict(
                {}, batch_size=self.shared_tensordict_parent.shape, device=self.device
            )
            for key in self._selected_step_keys:
                _set_single_key(next_td, out, key, clone=True)
        else:
            # strict=False ensures that non-homogeneous keys are still there
            out = next_td.select(*self._selected_step_keys, strict=False).clone()
        return out

    def __getattr__(self, attr: str) -> Any:
        if attr in self.__dir__():
            return super().__getattr__(
                attr
            )  # make sure that appropriate exceptions are raised
        elif attr.startswith("__"):
            raise AttributeError(
                "dispatching built-in private methods is "
                f"not permitted with type {type(self)}. "
                f"Got attribute {attr}."
            )
        else:
            if attr in self._excluded_wrapped_keys:
                raise AttributeError(f"Getting {attr} resulted in an exception")
            try:
                # determine if attr is a callable
                list_attr = [getattr(env, attr) for env in self._envs]
                callable_attr = callable(list_attr[0])
                if callable_attr:
                    if self.is_closed:
                        raise RuntimeError(
                            "Trying to access attributes of closed/non started "
                            "environments. Check that the batched environment "
                            "has been started (e.g. by calling env.reset)"
                        )
                    return _dispatch_caller_serial(list_attr)
                else:
                    return list_attr
            except AttributeError:
                raise AttributeError(
                    f"attribute {attr} not found in " f"{self._dummy_env_str}"
                )

    def to(self, device: DEVICE_TYPING):
        device = torch.device(device)
        if device == self.device:
            return self
        super().to(device)
        if not self.is_closed:
            for env in self._envs:
                env.to(device)
        return self


class ParallelEnv(_BatchedEnv):
    """Creates one environment per process.

    TensorDicts are passed via shared memory or memory map.

    """

    __doc__ += _BatchedEnv.__doc__

    def _start_workers(self) -> None:
        from torchrl.envs.env_creator import EnvCreator

        torch.set_num_threads(self.num_threads)

        ctx = mp.get_context("spawn")

        _num_workers = self.num_workers

        self.parent_channels = []
        self._workers = []
        self._events = []
        if self.device.type == "cuda":
            self.event = torch.cuda.Event()
        else:
            self.event = None
        with clear_mpi_env_vars():
            for idx in range(_num_workers):
                if self._verbose:
                    print(f"initiating worker {idx}")
                # No certainty which module multiprocessing_context is
                parent_pipe, child_pipe = ctx.Pipe()
                event = ctx.Event()
                self._events.append(event)
                env_fun = self.create_env_fn[idx]
                if not isinstance(env_fun, EnvCreator):
                    env_fun = CloudpickleWrapper(env_fun)

                process = _ProcessNoWarn(
                    target=_run_worker_pipe_shared_mem,
                    num_threads=self.num_sub_threads,
                    args=(
                        parent_pipe,
                        child_pipe,
                        env_fun,
                        self.create_env_kwargs[idx],
                        self.device,
                        event,
                        self.shared_tensordicts[idx],
                        self._selected_input_keys,
                        self._selected_reset_keys,
                        self._selected_step_keys,
                        self.has_lazy_inputs,
                    ),
                )
                process.daemon = True
                process.start()
                child_pipe.close()
                self.parent_channels.append(parent_pipe)
                self._workers.append(process)

        for parent_pipe in self.parent_channels:
            msg = parent_pipe.recv()
            assert msg == "started"

        # send shared tensordict to workers
        for channel in self.parent_channels:
            channel.send(("init", None))
        self.is_closed = False

    @_check_start
    def state_dict(self) -> OrderedDict:
        state_dict = OrderedDict()
        for channel in self.parent_channels:
            channel.send(("state_dict", None))
        for idx, channel in enumerate(self.parent_channels):
            msg, _state_dict = channel.recv()
            if msg != "state_dict":
                raise RuntimeError(f"Expected 'state_dict' but received {msg}")
            state_dict[f"worker{idx}"] = _state_dict

        return state_dict

    @_check_start
    def load_state_dict(self, state_dict: OrderedDict) -> None:
        if "worker0" not in state_dict:
            state_dict = OrderedDict(
                **{f"worker{idx}": state_dict for idx in range(self.num_workers)}
            )
        for i, channel in enumerate(self.parent_channels):
            channel.send(("load_state_dict", state_dict[f"worker{i}"]))
        for event in self._events:
            event.wait()
            event.clear()

    @_check_start
    def step_and_maybe_reset(
        self, tensordict: TensorDictBase
    ) -> Tuple[TensorDictBase, TensorDictBase]:
        if self._single_task and not self.has_lazy_inputs:
            # We must use the in_keys and nothing else for the following reasons:
            # - efficiency: copying all the keys will in practice mean doing a lot
            #   of writing operations since the input tensordict may (and often will)
            #   contain all the previous output data.
            # - value mismatch: if the batched env is placed within a transform
            #   and this transform overrides an observation key (eg, CatFrames)
            #   the shape, dtype or device may not necessarily match and writing
            #   the value in-place will fail.
            for key in tensordict.keys(True, True):
                # we copy the input keys as well as the keys in the 'next' td, if any
                # as this mechanism can be used by a policy to set anticipatively the
                # keys of the next call (eg, with recurrent nets)
                if key in self._env_input_keys or (
                    isinstance(key, tuple)
                    and key[0] == "next"
                    and key in self.shared_tensordict_parent.keys(True, True)
                ):
                    val = tensordict.get(key)
                    self.shared_tensordict_parent.set_(key, val)
        else:
            self.shared_tensordict_parent.update_(
                tensordict.select(*self._env_input_keys, "next", strict=False)
            )
        for i in range(self.num_workers):
            self.parent_channels[i].send(("step_and_maybe_reset", None))

        for i in range(self.num_workers):
            event = self._events[i]
            event.wait()
            event.clear()

        # We must pass a clone of the tensordict, as the values of this tensordict
        # will be modified in-place at further steps
        tensordict.set("next", self.shared_tensordict_parent.get("next").clone())
        tensordict_ = self.shared_tensordict_parent.exclude(
            "next", *self.reset_keys
        ).clone()
        return tensordict, tensordict_

    @_check_start
    def _step(self, tensordict: TensorDictBase) -> TensorDictBase:
        if self._single_task and not self.has_lazy_inputs:
            # We must use the in_keys and nothing else for the following reasons:
            # - efficiency: copying all the keys will in practice mean doing a lot
            #   of writing operations since the input tensordict may (and often will)
            #   contain all the previous output data.
            # - value mismatch: if the batched env is placed within a transform
            #   and this transform overrides an observation key (eg, CatFrames)
            #   the shape, dtype or device may not necessarily match and writing
            #   the value in-place will fail.
            for key in tensordict.keys(True, True):
                # we copy the input keys as well as the keys in the 'next' td, if any
                # as this mechanism can be used by a policy to set anticipatively the
                # keys of the next call (eg, with recurrent nets)
                if key in self._env_input_keys or (
                    isinstance(key, tuple)
                    and key[0] == "next"
                    and key in self.shared_tensordict_parent.keys(True, True)
                ):
                    val = tensordict.get(key)
                    self.shared_tensordict_parent.set_(key, val)
        else:
            self.shared_tensordict_parent.update_(
                tensordict.select(*self._env_input_keys, "next", strict=False)
            )
        if self.event is not None:
            self.event.record()
            self.event.synchronize()
        for i in range(self.num_workers):
            self.parent_channels[i].send(("step", None))

        for i in range(self.num_workers):
            event = self._events[i]
            event.wait()
            event.clear()

        # We must pass a clone of the tensordict, as the values of this tensordict
        # will be modified in-place at further steps
        next_td = self.shared_tensordict_parent.get("next")
        if self._single_task:
            out = TensorDict(
                {}, batch_size=self.shared_tensordict_parent.shape, device=self.device
            )
            for key in self._selected_step_keys:
                _set_single_key(next_td, out, key, clone=True)
        else:
            # strict=False ensures that non-homogeneous keys are still there
            out = next_td.select(*self._selected_step_keys, strict=False).clone()
        return out

    @_check_start
    def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase:
        if tensordict is not None:
            needs_resetting = _aggregate_end_of_traj(
                tensordict, reset_keys=self.reset_keys
            )
            if needs_resetting.ndim > 2:
                needs_resetting = needs_resetting.flatten(1, needs_resetting.ndim - 1)
            if needs_resetting.ndim > 1:
                needs_resetting = needs_resetting.any(-1)
            elif not needs_resetting.ndim:
                needs_resetting = needs_resetting.expand((self.num_workers,))
        else:
            needs_resetting = torch.ones(
                (self.num_workers,), device=self.device, dtype=torch.bool
            )

        workers = []

        for i, channel in enumerate(self.parent_channels):
            if tensordict is not None:
                tensordict_ = tensordict[i]
                if tensordict_.is_empty():
                    tensordict_ = None
            else:
                tensordict_ = None
            if not needs_resetting[i]:
                # We update the stored tensordict with the value of the "next"
                # key as one may be surprised to receive data that is not up-to-date
                # If we don't do this, the result of calling reset and skipping one env
                # will be that the env will have the data from the previous
                # step at the root (since the shared_tensordict did not go through
                # step_mdp).
                self.shared_tensordicts[i].update_(
                    self.shared_tensordicts[i]
                    .get("next")
                    .select(*self._selected_reset_keys, strict=False)
                )
                if tensordict_ is not None:
                    self.shared_tensordicts[i].update_(
                        tensordict_.select(*self._selected_reset_keys, strict=False)
                    )
                continue
            out = ("reset", tensordict_)
            channel.send(out)
            workers.append(i)

        for i in workers:
            event = self._events[i]
            event.wait()
            event.clear()

        selected_output_keys = self._selected_reset_keys_filt
        if self._single_task:
            # select + clone creates 2 tds, but we can create one only
            out = TensorDict(
                {}, batch_size=self.shared_tensordict_parent.shape, device=self.device
            )
            for key in selected_output_keys:
                _set_single_key(self.shared_tensordict_parent, out, key, clone=True)
            return out
        else:
            return self.shared_tensordict_parent.select(
                *selected_output_keys,
                strict=False,
            ).clone()

    @_check_start
    def _shutdown_workers(self) -> None:
        if self.is_closed:
            raise RuntimeError(
                "calling {self.__class__.__name__}._shutdown_workers only allowed when env.is_closed = False"
            )
        for i, channel in enumerate(self.parent_channels):
            if self._verbose:
                print(f"closing {i}")
            channel.send(("close", None))
            self._events[i].wait()
            self._events[i].clear()

        del self.shared_tensordicts, self.shared_tensordict_parent

        for channel in self.parent_channels:
            channel.close()
        for proc in self._workers:
            proc.join()
        del self._workers
        del self.parent_channels
        self._cuda_events = None
        self._events = None

    @_check_start
    def set_seed(
        self, seed: Optional[int] = None, static_seed: bool = False
    ) -> Optional[int]:
        self._seeds = []
        for channel in self.parent_channels:
            channel.send(("seed", (seed, static_seed)))
            self._seeds.append(seed)
            msg, new_seed = channel.recv()
            if msg != "seeded":
                raise RuntimeError(f"Expected 'seeded' but received {msg}")
            seed = new_seed
        return seed

    def __reduce__(self):
        if not self.is_closed:
            # ParallelEnv contains non-instantiated envs, thus it can be
            # closed and serialized if the environment building functions
            # permit it
            self.close()
        return super().__reduce__()

    def __getattr__(self, attr: str) -> Any:
        if attr in self.__dir__():
            return super().__getattr__(
                attr
            )  # make sure that appropriate exceptions are raised
        elif attr.startswith("__"):
            raise AttributeError(
                "dispatching built-in private methods is not permitted."
            )
        else:
            if attr in self._excluded_wrapped_keys:
                raise AttributeError(f"Getting {attr} resulted in an exception")
            try:
                # _ = getattr(self._dummy_env, attr)
                if self.is_closed:
                    raise RuntimeError(
                        "Trying to access attributes of closed/non started "
                        "environments. Check that the batched environment "
                        "has been started (e.g. by calling env.reset)"
                    )
                # dispatch to workers
                return _dispatch_caller_parallel(attr, self)
            except AttributeError:
                raise AttributeError(
                    f"attribute {attr} not found in " f"{self._dummy_env_str}"
                )

    def to(self, device: DEVICE_TYPING):
        device = torch.device(device)
        if device == self.device:
            return self
        super().to(device)
        if self._seeds is not None:
            warn(
                "Sending a seeded ParallelEnv to another device requires "
                f"re-seeding it. Re-seeding envs to {self._seeds}."
            )
            self.set_seed(self._seeds[0])
        return self


def _recursively_strip_locks_from_state_dict(state_dict: OrderedDict) -> OrderedDict:
    return OrderedDict(
        **{
            k: _recursively_strip_locks_from_state_dict(item)
            if isinstance(item, OrderedDict)
            else None
            if isinstance(item, MpLock)
            else item
            for k, item in state_dict.items()
        }
    )


def _run_worker_pipe_shared_mem(
    parent_pipe: connection.Connection,
    child_pipe: connection.Connection,
    env_fun: Union[EnvBase, Callable],
    env_fun_kwargs: Dict[str, Any],
    device: DEVICE_TYPING = None,
    mp_event: mp.Event = None,
    shared_tensordict: TensorDictBase = None,
    _selected_input_keys=None,
    _selected_reset_keys=None,
    _selected_step_keys=None,
    has_lazy_inputs: bool = False,
    verbose: bool = False,
) -> None:
    if device is None:
        device = torch.device("cpu")
    if device.type == "cuda":
        event = torch.cuda.Event()
    else:
        event = None

    parent_pipe.close()
    pid = os.getpid()
    if not isinstance(env_fun, EnvBase):
        env = env_fun(**env_fun_kwargs)
    else:
        if env_fun_kwargs:
            raise RuntimeError(
                "env_fun_kwargs must be empty if an environment is passed to a process."
            )
        env = env_fun
    env = env.to(device)
    del env_fun

    i = -1
    initialized = False

    child_pipe.send("started")

    while True:
        try:
            cmd, data = child_pipe.recv()
        except EOFError as err:
            raise EOFError(f"proc {pid} failed, last command: {cmd}.") from err
        if cmd == "seed":
            if not initialized:
                raise RuntimeError("call 'init' before closing")
            # torch.manual_seed(data)
            # np.random.seed(data)
            new_seed = env.set_seed(data[0], static_seed=data[1])
            child_pipe.send(("seeded", new_seed))

        elif cmd == "init":
            if verbose:
                print(f"initializing {pid}")
            if initialized:
                raise RuntimeError("worker already initialized")
            i = 0
            next_shared_tensordict = shared_tensordict.get("next")
            root_shared_tensordict = shared_tensordict.exclude("next")
            shared_tensordict = shared_tensordict.clone(False)

            if not (shared_tensordict.is_shared() or shared_tensordict.is_memmap()):
                raise RuntimeError(
                    "tensordict must be placed in shared memory (share_memory_() or memmap_())"
                )
            initialized = True

        elif cmd == "reset":
            if verbose:
                print(f"resetting worker {pid}")
            if not initialized:
                raise RuntimeError("call 'init' before resetting")
            cur_td = env.reset(tensordict=data)
            shared_tensordict.update_(
                cur_td.select(*_selected_reset_keys, strict=False)
            )
            if event is not None:
                event.record()
                event.synchronize()
            mp_event.set()

        elif cmd == "step":
            if not initialized:
                raise RuntimeError("called 'init' before step")
            i += 1
            next_td = env._step(shared_tensordict)
            next_shared_tensordict.update_(next_td)
            if event is not None:
                event.record()
                event.synchronize()
            mp_event.set()

        elif cmd == "step_and_maybe_reset":
            if not initialized:
                raise RuntimeError("called 'init' before step")
            i += 1
            td, root_next_td = env.step_and_maybe_reset(shared_tensordict.clone(False))
            next_shared_tensordict.update_(td.get("next"))
            root_shared_tensordict.update_(root_next_td)
            if event is not None:
                event.record()
                event.synchronize()
            mp_event.set()

        elif cmd == "close":
            del shared_tensordict, data
            if not initialized:
                raise RuntimeError("call 'init' before closing")
            env.close()
            del env
            mp_event.set()
            child_pipe.close()
            if verbose:
                print(f"{pid} closed")
            break

        elif cmd == "load_state_dict":
            env.load_state_dict(data)
            mp_event.set()

        elif cmd == "state_dict":
            state_dict = _recursively_strip_locks_from_state_dict(env.state_dict())
            msg = "state_dict"
            child_pipe.send((msg, state_dict))

        else:
            err_msg = f"{cmd} from env"
            try:
                attr = getattr(env, cmd)
                if callable(attr):
                    args, kwargs = data
                    args_replace = []
                    for _arg in args:
                        if isinstance(_arg, str) and _arg == "_self":
                            continue
                        else:
                            args_replace.append(_arg)
                    result = attr(*args_replace, **kwargs)
                else:
                    result = attr
            except Exception as err:
                raise AttributeError(
                    f"querying {err_msg} resulted in an error."
                ) from err
            if cmd not in ("to"):
                child_pipe.send(("_".join([cmd, "done"]), result))
            else:
                # don't send env through pipe
                child_pipe.send(("_".join([cmd, "done"]), None))