Source code for torchrl.envs.transforms.transforms
# Copyright (c) Meta Plobs_dictnc. 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 functools
import importlib.util
import multiprocessing as mp
import warnings
from copy import copy
from enum import IntEnum
from functools import wraps
from textwrap import indent
from typing import (
Any,
Callable,
Dict,
List,
Mapping,
Optional,
OrderedDict,
Sequence,
Tuple,
TypeVar,
Union,
)
import numpy as np
import torch
from tensordict import (
is_tensor_collection,
LazyStackedTensorDict,
NonTensorData,
set_lazy_legacy,
TensorDict,
TensorDictBase,
unravel_key,
unravel_key_list,
)
from tensordict.nn import dispatch, TensorDictModuleBase
from tensordict.utils import (
_unravel_key_to_tuple,
_zip_strict,
expand_as_right,
expand_right,
NestedKey,
)
from torch import nn, Tensor
from torch.utils._pytree import tree_map
from torchrl._utils import (
_append_last,
_ends_with,
_make_ordinal_device,
_replace_last,
implement_for,
)
from torchrl.data.tensor_specs import (
Binary,
Bounded,
Categorical,
Composite,
ContinuousBox,
MultiCategorical,
MultiOneHot,
OneHot,
TensorSpec,
Unbounded,
)
from torchrl.envs.common import _do_nothing, _EnvPostInit, EnvBase, make_tensordict
from torchrl.envs.transforms import functional as F
from torchrl.envs.transforms.utils import (
_get_reset,
_set_missing_tolerance,
check_finite,
)
from torchrl.envs.utils import _sort_keys, _update_during_reset, step_mdp
from torchrl.objectives.value.functional import reward2go
_has_tv = importlib.util.find_spec("torchvision", None) is not None
IMAGE_KEYS = ["pixels"]
_MAX_NOOPS_TRIALS = 10
FORWARD_NOT_IMPLEMENTED = "class {} cannot be executed without a parent environment."
T = TypeVar("T", bound="Transform")
def _apply_to_composite(function):
@wraps(function)
def new_fun(self, observation_spec):
if isinstance(observation_spec, Composite):
_specs = observation_spec._specs
in_keys = self.in_keys
out_keys = self.out_keys
for in_key, out_key in _zip_strict(in_keys, out_keys):
if in_key in observation_spec.keys(True, True):
_specs[out_key] = function(self, observation_spec[in_key].clone())
return Composite(
_specs, shape=observation_spec.shape, device=observation_spec.device
)
else:
return function(self, observation_spec)
return new_fun
def _apply_to_composite_inv(function):
# Changes the input_spec following a transform function.
# The usage is: if an env expects a certain input (e.g. a double tensor)
# but the input has to be transformed (e.g. it is float), this function will
# modify the spec to get a spec that from the outside matches what is given
# (ie a float).
# Now since EnvBase.step ignores new inputs (ie the root level of the
# tensor is not updated) an out_key that does not match the in_key has
# no effect on the spec.
def new_fun(self, input_spec):
action_spec = input_spec["full_action_spec"].clone()
state_spec = input_spec["full_state_spec"]
if state_spec is None:
state_spec = Composite(shape=input_spec.shape, device=input_spec.device)
else:
state_spec = state_spec.clone()
in_keys_inv = self.in_keys_inv
out_keys_inv = self.out_keys_inv
for in_key, out_key in _zip_strict(in_keys_inv, out_keys_inv):
if in_key != out_key:
# we only change the input spec if the key is the same
continue
if in_key in action_spec.keys(True, True):
action_spec[out_key] = function(self, action_spec[in_key].clone())
elif in_key in state_spec.keys(True, True):
state_spec[out_key] = function(self, state_spec[in_key].clone())
return Composite(
full_state_spec=state_spec,
full_action_spec=action_spec,
shape=input_spec.shape,
device=input_spec.device,
)
return new_fun
[docs]class Transform(nn.Module):
"""Environment transform parent class.
In principle, a transform receives a tensordict as input and returns (
the same or another) tensordict as output, where a series of values have
been modified or created with a new key. When instantiating a new
transform, the keys that are to be read from are passed to the
constructor via the :obj:`keys` argument.
Transforms are to be combined with their target environments with the
TransformedEnv class, which takes as arguments an :obj:`EnvBase` instance
and a transform. If multiple transforms are to be used, they can be
concatenated using the :obj:`Compose` class.
A transform can be stateless or stateful (e.g. CatTransform). Because of
this, Transforms support the :obj:`reset` operation, which should reset the
transform to its initial state (such that successive trajectories are kept
independent).
Notably, :obj:`Transform` subclasses take care of transforming the affected
specs from an environment: when querying
`transformed_env.observation_spec`, the resulting objects will describe
the specs of the transformed_in tensors.
"""
invertible = False
def __init__(
self,
in_keys: Sequence[NestedKey] = None,
out_keys: Sequence[NestedKey] | None = None,
in_keys_inv: Sequence[NestedKey] | None = None,
out_keys_inv: Sequence[NestedKey] | None = None,
):
super().__init__()
self.in_keys = in_keys
self.out_keys = out_keys
self.in_keys_inv = in_keys_inv
self.out_keys_inv = out_keys_inv
self._missing_tolerance = False
# we use __dict__ to avoid having nn.Module placing these objects in the module list
self.__dict__["_container"] = None
self.__dict__["_parent"] = None
@property
def in_keys(self):
in_keys = self.__dict__.get("_in_keys", None)
if in_keys is None:
return []
return in_keys
@in_keys.setter
def in_keys(self, value):
if value is not None:
if isinstance(value, (str, tuple)):
value = [value]
value = [unravel_key(val) for val in value]
self._in_keys = value
@property
def out_keys(self):
out_keys = self.__dict__.get("_out_keys", None)
if out_keys is None:
return []
return out_keys
@out_keys.setter
def out_keys(self, value):
if value is not None:
if isinstance(value, (str, tuple)):
value = [value]
value = [unravel_key(val) for val in value]
self._out_keys = value
@property
def in_keys_inv(self):
in_keys_inv = self.__dict__.get("_in_keys_inv", None)
if in_keys_inv is None:
return []
return in_keys_inv
@in_keys_inv.setter
def in_keys_inv(self, value):
if value is not None:
if isinstance(value, (str, tuple)):
value = [value]
value = [unravel_key(val) for val in value]
self._in_keys_inv = value
@property
def out_keys_inv(self):
out_keys_inv = self.__dict__.get("_out_keys_inv", None)
if out_keys_inv is None:
return []
return out_keys_inv
@out_keys_inv.setter
def out_keys_inv(self, value):
if value is not None:
if isinstance(value, (str, tuple)):
value = [value]
value = [unravel_key(val) for val in value]
self._out_keys_inv = value
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
"""Resets a transform if it is stateful."""
return tensordict_reset
def init(self, tensordict) -> None:
pass
def _apply_transform(self, obs: torch.Tensor) -> None:
"""Applies the transform to a tensor.
This operation can be called multiple times (if multiples keys of the
tensordict match the keys of the transform).
"""
raise NotImplementedError(
f"{self.__class__.__name__}._apply_transform is not coded. If the transform is coded in "
"transform._call, make sure that this method is called instead of"
"transform.forward, which is reserved for usage inside nn.Modules"
"or appended to a replay buffer."
)
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
"""Reads the input tensordict, and for the selected keys, applies the transform.
For any operation that relates exclusively to the parent env (e.g. FrameSkip),
modify the _step method instead. :meth:`~._call` should only be overwritten
if a modification of the input tensordict is needed.
:meth:`~._call` will be called by :meth:`TransformedEnv.step` and
:meth:`TransformedEnv.reset`.
"""
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys):
value = tensordict.get(in_key, default=None)
if value is not None:
observation = self._apply_transform(value)
tensordict.set(
out_key,
observation,
)
elif not self.missing_tolerance:
raise KeyError(
f"{self}: '{in_key}' not found in tensordict {tensordict}"
)
return tensordict
[docs] @dispatch(source="in_keys", dest="out_keys")
def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
"""Reads the input tensordict, and for the selected keys, applies the transform."""
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys):
data = tensordict.get(in_key, None)
if data is not None:
data = self._apply_transform(data)
tensordict.set(out_key, data)
elif not self.missing_tolerance:
raise KeyError(f"'{in_key}' not found in tensordict {tensordict}")
return tensordict
def _step(
self, tensordict: TensorDictBase, next_tensordict: TensorDictBase
) -> TensorDictBase:
"""The parent method of a transform during the ``env.step`` execution.
This method should be overwritten whenever the :meth:`~._step` needs to be
adapted. Unlike :meth:`~._call`, it is assumed that :meth:`~._step`
will execute some operation with the parent env or that it requires
access to the content of the tensordict at time ``t`` and not only
``t+1`` (the ``"next"`` entry in the input tensordict).
:meth:`~._step` will only be called by :meth:`TransformedEnv.step` and
not by :meth:`TransformedEnv.reset`.
Args:
tensordict (TensorDictBase): data at time t
next_tensordict (TensorDictBase): data at time t+1
Returns: the data at t+1
"""
next_tensordict = self._call(next_tensordict)
return next_tensordict
def _inv_apply_transform(self, state: torch.Tensor) -> torch.Tensor:
if self.invertible:
raise NotImplementedError
else:
return state
def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase:
if not self.in_keys_inv:
return tensordict
for in_key, out_key in _zip_strict(self.in_keys_inv, self.out_keys_inv):
data = tensordict.get(in_key, None)
if data is not None:
item = self._inv_apply_transform(data)
tensordict.set(out_key, item)
elif not self.missing_tolerance:
raise KeyError(f"'{in_key}' not found in tensordict {tensordict}")
return tensordict
@dispatch(source="in_keys_inv", dest="out_keys_inv")
def inv(self, tensordict: TensorDictBase) -> TensorDictBase:
def clone(data):
try:
# we privilege speed for tensordicts
return data.clone(recurse=False)
except AttributeError:
return tree_map(lambda x: x, data)
except TypeError:
return tree_map(lambda x: x, data)
out = self._inv_call(clone(tensordict))
return out
[docs] def transform_env_device(self, device: torch.device):
"""Transforms the device of the parent env."""
return device
[docs] def transform_env_batch_size(self, batch_size: torch.Size):
"""Transforms the batch-size of the parent env."""
return batch_size
[docs] def transform_output_spec(self, output_spec: Composite) -> Composite:
"""Transforms the output spec such that the resulting spec matches transform mapping.
This method should generally be left untouched. Changes should be implemented using
:meth:`~.transform_observation_spec`, :meth:`~.transform_reward_spec` and :meth:`~.transformfull_done_spec`.
Args:
output_spec (TensorSpec): spec before the transform
Returns:
expected spec after the transform
"""
output_spec = output_spec.clone()
output_spec["full_observation_spec"] = self.transform_observation_spec(
output_spec["full_observation_spec"]
)
if "full_reward_spec" in output_spec.keys():
output_spec["full_reward_spec"] = self.transform_reward_spec(
output_spec["full_reward_spec"]
)
if "full_done_spec" in output_spec.keys():
output_spec["full_done_spec"] = self.transform_done_spec(
output_spec["full_done_spec"]
)
return output_spec
[docs] def transform_input_spec(self, input_spec: TensorSpec) -> TensorSpec:
"""Transforms the input spec such that the resulting spec matches transform mapping.
Args:
input_spec (TensorSpec): spec before the transform
Returns:
expected spec after the transform
"""
return input_spec
[docs] def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
"""Transforms the observation spec such that the resulting spec matches transform mapping.
Args:
observation_spec (TensorSpec): spec before the transform
Returns:
expected spec after the transform
"""
return observation_spec
[docs] def transform_reward_spec(self, reward_spec: TensorSpec) -> TensorSpec:
"""Transforms the reward spec such that the resulting spec matches transform mapping.
Args:
reward_spec (TensorSpec): spec before the transform
Returns:
expected spec after the transform
"""
return reward_spec
[docs] def transform_done_spec(self, done_spec: TensorSpec) -> TensorSpec:
"""Transforms the done spec such that the resulting spec matches transform mapping.
Args:
done_spec (TensorSpec): spec before the transform
Returns:
expected spec after the transform
"""
return done_spec
def dump(self, **kwargs) -> None:
pass
def __repr__(self) -> str:
return f"{self.__class__.__name__}(keys={self.in_keys})"
def set_container(self, container: Union[Transform, EnvBase]) -> None:
if self.parent is not None:
raise AttributeError(
f"parent of transform {type(self)} already set. "
"Call `transform.clone()` to get a similar transform with no parent set."
)
self.__dict__["_container"] = container
self.__dict__["_parent"] = None
def reset_parent(self) -> None:
self.__dict__["_container"] = None
self.__dict__["_parent"] = None
def clone(self) -> T:
self_copy = copy(self)
state = copy(self.__dict__)
state["_container"] = None
state["_parent"] = None
self_copy.__dict__.update(state)
return self_copy
@property
def container(self):
"""Returns the env containing the transform.
Examples:
>>> from torchrl.envs import TransformedEnv, Compose, RewardSum, StepCounter
>>> from torchrl.envs.libs.gym import GymEnv
>>> env = TransformedEnv(GymEnv("Pendulum-v1"), Compose(RewardSum(), StepCounter()))
>>> env.transform[0].container is env
True
"""
if "_container" not in self.__dict__:
raise AttributeError("transform parent uninitialized")
container = self.__dict__["_container"]
if container is None:
return container
while not isinstance(container, EnvBase):
# if it's not an env, it should be a Compose transform
if not isinstance(container, Compose):
raise ValueError(
"A transform parent must be either another Compose transform or an environment object."
)
compose = container
container = compose.__dict__.get("_container", None)
return container
@property
def parent(self) -> Optional[EnvBase]:
"""Returns the parent env of the transform.
The parent env is the env that contains all the transforms up until the current one.
Examples:
>>> from torchrl.envs import TransformedEnv, Compose, RewardSum, StepCounter
>>> from torchrl.envs.libs.gym import GymEnv
>>> env = TransformedEnv(GymEnv("Pendulum-v1"), Compose(RewardSum(), StepCounter()))
>>> env.transform[1].parent
TransformedEnv(
env=GymEnv(env=Pendulum-v1, batch_size=torch.Size([]), device=cpu),
transform=Compose(
RewardSum(keys=['reward'])))
"""
if self.__dict__.get("_parent", None) is None:
if "_container" not in self.__dict__:
raise AttributeError("transform parent uninitialized")
container = self.__dict__["_container"]
if container is None:
return container
out = None
if not isinstance(container, EnvBase):
# if it's not an env, it should be a Compose transform
if not isinstance(container, Compose):
raise ValueError(
"A transform parent must be either another Compose transform or an environment object."
)
out, _ = container._rebuild_up_to(self)
elif isinstance(container, TransformedEnv):
out = TransformedEnv(container.base_env)
else:
raise ValueError(f"container is of type {type(container)}")
self.__dict__["_parent"] = out
return self.__dict__["_parent"]
def empty_cache(self):
self.__dict__["_parent"] = None
def set_missing_tolerance(self, mode=False):
self._missing_tolerance = mode
@property
def missing_tolerance(self):
return self._missing_tolerance
[docs] def to(self, *args, **kwargs):
# remove the parent, because it could have the wrong device associated
self.empty_cache()
return super().to(*args, **kwargs)
class _TEnvPostInit(_EnvPostInit):
def __call__(self, *args, **kwargs):
instance: EnvBase = super(_EnvPostInit, self).__call__(*args, **kwargs)
# we skip the materialization of the specs, because this can't be done with lazy
# transforms such as ObservationNorm.
return instance
[docs]class TransformedEnv(EnvBase, metaclass=_TEnvPostInit):
"""A transformed_in environment.
Args:
env (EnvBase): original environment to be transformed_in.
transform (Transform or callable, optional): transform to apply to the tensordict resulting
from :obj:`env.step(td)`. If none is provided, an empty Compose
placeholder in an eval mode is used.
.. note:: If ``transform`` is a callable, it must receive as input a single tensordict
and output a tensordict as well. The callable will be called at ``step``
and ``reset`` time: if it acts on the reward (which is absent at
reset time), a check needs to be implemented to ensure that
the transform will run smoothly:
>>> def add_1(data):
... if "reward" in data.keys():
... return data.set("reward", data.get("reward") + 1)
... return data
>>> env = TransformedEnv(base_env, add_1)
cache_specs (bool, optional): if ``True``, the specs will be cached once
and for all after the first call (i.e. the specs will be
transformed_in only once). If the transform changes during
training, the original spec transform may not be valid anymore,
in which case this value should be set to `False`. Default is
`True`.
Examples:
>>> env = GymEnv("Pendulum-v0")
>>> transform = RewardScaling(0.0, 1.0)
>>> transformed_env = TransformedEnv(env, transform)
"""
def __init__(
self,
env: EnvBase,
transform: Optional[Transform] = None,
cache_specs: bool = True,
**kwargs,
):
self._transform = None
device = kwargs.pop("device", None)
if device is not None:
env = env.to(device)
else:
device = env.device
super().__init__(device=None, allow_done_after_reset=None, **kwargs)
# Type matching must be exact here, because subtyping could introduce differences in behavior that must
# be contained within the subclass.
if type(env) is TransformedEnv and type(self) is TransformedEnv:
self._set_env(env.base_env, device)
if type(transform) is not Compose:
# we don't use isinstance as some transforms may be subclassed from
# Compose but with other features that we don't want to lose.
if not isinstance(transform, Transform):
if callable(transform):
transform = _CallableTransform(transform)
else:
raise ValueError(
"Invalid transform type, expected a Transform instance or a callable "
f"but got an object of type {type(transform)}."
)
if transform is not None:
transform = [transform]
else:
transform = []
else:
for t in transform:
t.reset_parent()
env_transform = env.transform.clone()
if type(env_transform) is not Compose:
env_transform = [env_transform]
else:
for t in env_transform:
t.reset_parent()
transform = Compose(*env_transform, *transform).to(device)
else:
self._set_env(env, device)
if transform is None:
transform = Compose()
self.transform = transform
self._last_obs = None
self.cache_specs = cache_specs
self.__dict__["_input_spec"] = None
self.__dict__["_output_spec"] = None
@property
def batch_size(self) -> torch.Size:
try:
if self.transform is not None:
return self.transform.transform_env_batch_size(self.base_env.batch_size)
return self.base_env.batch_size
except AttributeError:
# during init, the base_env is not yet defined
return torch.Size([])
@batch_size.setter
def batch_size(self, value: torch.Size) -> None:
raise RuntimeError(
"Cannot modify the batch-size of a transformed env. Change the batch size of the base_env instead."
)
[docs] def add_truncated_keys(self) -> TransformedEnv:
self.base_env.add_truncated_keys()
self.empty_cache()
return self
def _set_env(self, env: EnvBase, device) -> None:
if device != env.device:
env = env.to(device)
self.base_env = env
# updates need not be inplace, as transforms may modify values out-place
self.base_env._inplace_update = False
@property
def transform(self) -> Transform:
return getattr(self, "_transform", None)
@transform.setter
def transform(self, transform: Transform):
if not isinstance(transform, Transform):
if callable(transform):
transform = _CallableTransform(transform)
else:
raise ValueError(
f"""Expected a transform of type torchrl.envs.transforms.Transform or a callable,
but got an object of type {type(transform)}."""
)
prev_transform = getattr(self, "_transform", None)
if prev_transform is not None:
prev_transform.empty_cache()
prev_transform.reset_parent()
transform = transform.to(self.device)
transform.set_container(self)
transform.eval()
self._transform = transform
@property
def device(self) -> bool:
device = self.base_env.device
if self.transform is None:
# during init, the device is checked
return device
return self.transform.transform_env_device(device)
@device.setter
def device(self, value):
raise RuntimeError("device is a read-only property")
@property
def batch_locked(self) -> bool:
return self.base_env.batch_locked
@batch_locked.setter
def batch_locked(self, value):
raise RuntimeError("batch_locked is a read-only property")
@property
def run_type_checks(self) -> bool:
return self.base_env.run_type_checks
@run_type_checks.setter
def run_type_checks(self, value):
raise RuntimeError(
"run_type_checks is a read-only property for TransformedEnvs"
)
@property
def _allow_done_after_reset(self) -> bool:
return self.base_env._allow_done_after_reset
@_allow_done_after_reset.setter
def _allow_done_after_reset(self, value):
if value is None:
return
raise RuntimeError(
"_allow_done_after_reset is a read-only property for TransformedEnvs"
)
@property
def _inplace_update(self):
return self.base_env._inplace_update
@property
def output_spec(self) -> TensorSpec:
"""Observation spec of the transformed environment."""
if not self.cache_specs or self.__dict__.get("_output_spec", None) is None:
output_spec = self.base_env.output_spec.clone()
# remove cached key values, but not _input_spec
super().empty_cache()
output_spec = output_spec.unlock_()
output_spec = self.transform.transform_output_spec(output_spec)
output_spec.lock_()
if self.cache_specs:
self.__dict__["_output_spec"] = output_spec
else:
output_spec = self.__dict__.get("_output_spec", None)
return output_spec
@property
def input_spec(self) -> TensorSpec:
"""Action spec of the transformed environment."""
if self.__dict__.get("_input_spec", None) is None or not self.cache_specs:
input_spec = self.base_env.input_spec.clone()
# remove cached key values but not _output_spec
super().empty_cache()
input_spec.unlock_()
input_spec = self.transform.transform_input_spec(input_spec)
input_spec.lock_()
if self.cache_specs:
self.__dict__["_input_spec"] = input_spec
else:
input_spec = self.__dict__.get("_input_spec", None)
return input_spec
def _step(self, tensordict: TensorDictBase) -> TensorDictBase:
# No need to clone here because inv does it already
# tensordict = tensordict.clone(False)
next_preset = tensordict.get("next", None)
tensordict_in = self.transform.inv(tensordict)
next_tensordict = self.base_env._step(tensordict_in)
if next_preset is not None:
# tensordict could already have a "next" key
# this could be done more efficiently by not excluding but just passing
# the necessary keys
next_tensordict.update(
next_preset.exclude(*next_tensordict.keys(True, True))
)
self.base_env._complete_done(self.base_env.full_done_spec, next_tensordict)
# we want the input entries to remain unchanged
next_tensordict = self.transform._step(tensordict, next_tensordict)
return next_tensordict
[docs] def set_seed(
self, seed: Optional[int] = None, static_seed: bool = False
) -> Optional[int]:
"""Set the seeds of the environment."""
return self.base_env.set_seed(seed, static_seed=static_seed)
def _set_seed(self, seed: Optional[int]):
"""This method is not used in transformed envs."""
pass
def _reset(self, tensordict: Optional[TensorDictBase] = None, **kwargs):
if tensordict is not None:
# We must avoid modifying the original tensordict so a shallow copy is necessary.
# We just select the input data and reset signal, which is all we need.
tensordict = tensordict.select(
*self.reset_keys, *self.state_spec.keys(True, True), strict=False
)
tensordict_reset = self.base_env._reset(tensordict, **kwargs)
if tensordict is None:
# make sure all transforms see a source tensordict
tensordict = tensordict_reset.empty()
self.base_env._complete_done(self.base_env.full_done_spec, tensordict_reset)
tensordict_reset = self.transform._reset(tensordict, tensordict_reset)
return tensordict_reset
def _reset_proc_data(self, tensordict, tensordict_reset):
# self._complete_done(self.full_done_spec, reset)
self._reset_check_done(tensordict, tensordict_reset)
if tensordict is not None:
tensordict_reset = _update_during_reset(
tensordict_reset, tensordict, self.reset_keys
)
# # we need to call `_call` as some transforms don't do the work in reset
# # eg: CatTensor has only a _call method, no need for a reset since reset
# # doesn't do anything special
# mt_mode = self.transform.missing_tolerance
# self.set_missing_tolerance(True)
# reset = self.transform._call(reset)
# self.set_missing_tolerance(mt_mode)
return tensordict_reset
def _complete_done(
cls, done_spec: Composite, data: TensorDictBase
) -> TensorDictBase:
# This step has already been completed. We assume the transform module do their job correctly.
return data
[docs] def state_dict(self, *args, **kwargs) -> OrderedDict:
state_dict = self.transform.state_dict(*args, **kwargs)
return state_dict
[docs] def load_state_dict(self, state_dict: OrderedDict, **kwargs) -> None:
self.transform.load_state_dict(state_dict, **kwargs)
[docs] def eval(self) -> TransformedEnv:
if "transform" in self.__dir__():
# when calling __init__, eval() is called but transforms are not set
# yet.
self.transform.eval()
return self
@property
def is_closed(self) -> bool:
return self.base_env.is_closed
@is_closed.setter
def is_closed(self, value: bool):
self.base_env.is_closed = value
def close(self):
self.base_env.close()
self.is_closed = True
[docs] def empty_cache(self):
self.__dict__["_output_spec"] = None
self.__dict__["_input_spec"] = None
super().empty_cache()
[docs] def append_transform(
self, transform: Transform | Callable[[TensorDictBase], TensorDictBase]
) -> TransformedEnv:
"""Appends a transform to the env.
:class:`~torchrl.envs.transforms.Transform` or callable are accepted.
"""
self.empty_cache()
if not isinstance(transform, Transform):
if callable(transform):
transform = _CallableTransform(transform)
else:
raise ValueError(
"TransformedEnv.append_transform expected a transform or a callable, "
f"but received an object of type {type(transform)} instead."
)
transform = transform.to(self.device)
if not isinstance(self.transform, Compose):
prev_transform = self.transform
prev_transform.reset_parent()
self.transform = Compose()
self.transform.append(prev_transform)
self.transform.append(transform)
return self
[docs] def insert_transform(self, index: int, transform: Transform) -> TransformedEnv:
"""Inserts a transform to the env at the desired index.
:class:`~torchrl.envs.transforms.Transform` or callable are accepted.
"""
self.empty_cache()
if not isinstance(transform, Transform):
if callable(transform):
transform = _CallableTransform(transform)
else:
raise ValueError(
"TransformedEnv.insert_transform expected a transform or a callable, "
f"but received an object of type {type(transform)} instead."
)
transform = transform.to(self.device)
if not isinstance(self.transform, Compose):
compose = Compose(self.transform.clone())
self.transform = compose # parent set automatically
self.transform.insert(index, transform)
return self
def __getattr__(self, attr: str) -> Any:
try:
return super().__getattr__(
attr
) # make sure that appropriate exceptions are raised
except AttributeError as err:
if attr in (
"action_spec",
"done_spec",
"full_action_spec",
"full_done_spec",
"full_observation_spec",
"full_reward_spec",
"full_state_spec",
"input_spec",
"observation_spec",
"output_spec",
"reward_spec",
"state_spec",
):
raise AttributeError(
f"Could not get {attr} because an internal error was raised. To find what this error "
f"is, call env.transform.transform_<placeholder>_spec(env.base_env.spec)."
)
if attr.startswith("__"):
raise AttributeError(
"passing built-in private methods is "
f"not permitted with type {type(self)}. "
f"Got attribute {attr}."
)
elif "base_env" in self.__dir__():
base_env = self.__getattr__("base_env")
return getattr(base_env, attr)
raise AttributeError(
f"env not set in {self.__class__.__name__}, cannot access {attr}"
) from err
def __repr__(self) -> str:
env_str = indent(f"env={self.base_env}", 4 * " ")
t_str = indent(f"transform={self.transform}", 4 * " ")
return f"TransformedEnv(\n{env_str},\n{t_str})"
[docs] def to(self, *args, **kwargs) -> TransformedEnv:
device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(
*args, **kwargs
)
if device is not None:
self.base_env = self.base_env.to(device)
self._transform = self._transform.to(device)
self.empty_cache()
return super().to(*args, **kwargs)
def __setattr__(self, key, value):
propobj = getattr(self.__class__, key, None)
if isinstance(propobj, property):
ancestors = list(__class__.__mro__)[::-1]
while isinstance(propobj, property):
if propobj.fset is not None:
return propobj.fset(self, value)
propobj = getattr(ancestors.pop(), key, None)
else:
raise AttributeError(f"can't set attribute {key}")
else:
return super().__setattr__(key, value)
def __del__(self):
# we may delete a TransformedEnv that contains an env contained by another
# transformed env and that we don't want to close
pass
[docs] def set_missing_tolerance(self, mode=False):
"""Indicates if an KeyError should be raised whenever an in_key is missing from the input tensordict."""
self.transform.set_missing_tolerance(mode)
[docs]class ObservationTransform(Transform):
"""Abstract class for transformations of the observations."""
def __init__(
self,
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
in_keys_inv: Sequence[NestedKey] | None = None,
out_keys_inv: Sequence[NestedKey] | None = None,
):
if in_keys is None:
in_keys = [
"observation",
"pixels",
]
super(ObservationTransform, self).__init__(
in_keys=in_keys,
out_keys=out_keys,
in_keys_inv=in_keys_inv,
out_keys_inv=out_keys_inv,
)
[docs]class Compose(Transform):
"""Composes a chain of transforms.
:class:`~torchrl.envs.transforms.Transform` or ``callable``s are accepted.
Examples:
>>> env = GymEnv("Pendulum-v0")
>>> transforms = [RewardScaling(1.0, 1.0), RewardClipping(-2.0, 2.0)]
>>> transforms = Compose(*transforms)
>>> transformed_env = TransformedEnv(env, transforms)
"""
def __init__(self, *transforms: Transform):
super().__init__()
def map_transform(trsf):
if isinstance(trsf, Transform):
return trsf
if callable(trsf):
return _CallableTransform(trsf)
raise ValueError(
f"Transform list must contain only transforms or "
f"callable. Got a element of type {type(trsf)}."
)
transforms = [map_transform(trsf) for trsf in transforms]
self.transforms = nn.ModuleList(transforms)
for t in transforms:
t.set_container(self)
[docs] def to(self, *args, **kwargs):
# because Module.to(...) does not call to(...) on sub-modules, we have
# manually call it:
self.transforms = nn.ModuleList(
[t.to(*args, **kwargs) for t in self.transforms]
)
return super().to(*args, **kwargs)
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
for t in self.transforms:
tensordict = t._call(tensordict)
return tensordict
[docs] def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
for t in self.transforms:
tensordict = t(tensordict)
return tensordict
def _step(
self, tensordict: TensorDictBase, next_tensordict: TensorDictBase
) -> TensorDictBase:
for t in self.transforms:
next_tensordict = t._step(tensordict, next_tensordict)
return next_tensordict
def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase:
for t in reversed(self.transforms):
tensordict = t._inv_call(tensordict)
return tensordict
[docs] def transform_env_device(self, device: torch.device):
for t in self.transforms:
device = t.transform_env_device(device)
return device
[docs] def transform_env_batch_size(self, batch_size: torch.batch_size):
for t in self.transforms:
batch_size = t.transform_env_batch_size(batch_size)
return batch_size
[docs] def transform_input_spec(self, input_spec: TensorSpec) -> TensorSpec:
for t in self.transforms:
input_spec = t.transform_input_spec(input_spec)
return input_spec
[docs] def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
for t in self.transforms:
observation_spec = t.transform_observation_spec(observation_spec)
return observation_spec
[docs] def transform_output_spec(self, output_spec: TensorSpec) -> TensorSpec:
for t in self.transforms:
output_spec = t.transform_output_spec(output_spec)
return output_spec
[docs] def transform_reward_spec(self, reward_spec: TensorSpec) -> TensorSpec:
for t in self.transforms:
reward_spec = t.transform_reward_spec(reward_spec)
return reward_spec
def __getitem__(self, item: Union[int, slice, List]) -> Union:
transform = self.transforms
transform = transform[item]
if not isinstance(transform, Transform):
out = Compose(*(t.clone() for t in self.transforms[item]))
out.set_container(self.parent)
return out
return transform
def dump(self, **kwargs) -> None:
for t in self:
t.dump(**kwargs)
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
for t in self.transforms:
tensordict_reset = t._reset(tensordict, tensordict_reset)
return tensordict_reset
def init(self, tensordict: TensorDictBase) -> None:
for t in self.transforms:
t.init(tensordict)
[docs] def append(
self, transform: Transform | Callable[[TensorDictBase], TensorDictBase]
) -> None:
"""Appends a transform in the chain.
:class:`~torchrl.envs.transforms.Transform` or callable are accepted.
"""
self.empty_cache()
if not isinstance(transform, Transform):
if callable(transform):
transform = _CallableTransform(transform)
else:
raise ValueError(
"Compose.append expected a transform or a callable, "
f"but received an object of type {type(transform)} instead."
)
transform.eval()
if type(self) == type(transform) == Compose:
for t in transform:
self.append(t)
else:
self.transforms.append(transform)
transform.set_container(self)
def set_container(self, container: Union[Transform, EnvBase]) -> None:
self.reset_parent()
super().set_container(container)
for t in self.transforms:
t.set_container(self)
[docs] def insert(
self,
index: int,
transform: Transform | Callable[[TensorDictBase], TensorDictBase],
) -> None:
"""Inserts a transform in the chain at the desired index.
:class:`~torchrl.envs.transforms.Transform` or callable are accepted.
"""
if not isinstance(transform, Transform):
if callable(transform):
transform = _CallableTransform(transform)
else:
raise ValueError(
"Compose.append expected a transform or a callable, "
f"but received an object of type {type(transform)} instead."
)
if abs(index) > len(self.transforms):
raise ValueError(
f"Index expected to be between [-{len(self.transforms)}, {len(self.transforms)}] got index={index}"
)
# empty cache of all transforms to reset parents and specs
self.empty_cache()
if index < 0:
index = index + len(self.transforms)
transform.eval()
self.transforms.insert(index, transform)
transform.set_container(self)
def __iter__(self):
yield from self.transforms
def __len__(self):
return len(self.transforms)
def __repr__(self) -> str:
layers_str = ",\n".join(
[indent(str(trsf), 4 * " ") for trsf in self.transforms]
)
return f"{self.__class__.__name__}(\n{indent(layers_str, 4 * ' ')})"
def empty_cache(self):
for t in self.transforms:
t.empty_cache()
super().empty_cache()
def reset_parent(self):
for t in self.transforms:
t.reset_parent()
super().reset_parent()
def clone(self) -> T:
transforms = []
for t in self.transforms:
transforms.append(t.clone())
return Compose(*transforms)
def set_missing_tolerance(self, mode=False):
for t in self.transforms:
t.set_missing_tolerance(mode)
super().set_missing_tolerance(mode)
def _rebuild_up_to(self, final_transform):
container = self.__dict__["_container"]
if isinstance(container, Compose):
out, parent_compose = container._rebuild_up_to(self)
if out is None:
# returns None if there is no parent env
return None, None
elif isinstance(container, TransformedEnv):
out = TransformedEnv(container.base_env)
elif container is None:
# returns None if there is no parent env
return None, None
else:
raise ValueError(f"Container of type {type(container)} isn't supported.")
if final_transform not in self.transforms:
raise ValueError(f"Cannot rebuild with transform {final_transform}.")
list_of_transforms = []
for orig_trans in self.transforms:
if orig_trans is final_transform:
break
transform = orig_trans.clone()
transform.reset_parent()
list_of_transforms.append(transform)
if isinstance(container, Compose):
parent_compose.append(Compose(*list_of_transforms))
return out, parent_compose[-1]
elif isinstance(container, TransformedEnv):
for t in list_of_transforms:
out.append_transform(t)
return out, out.transform
[docs]class ToTensorImage(ObservationTransform):
"""Transforms a numpy-like image (W x H x C) to a pytorch image (C x W x H).
Transforms an observation image from a (... x W x H x C) tensor to a
(... x C x W x H) tensor. Optionally, scales the input tensor from the range
[0, 255] to the range [0.0, 1.0] (see ``from_int`` for more details).
In the other cases, tensors are returned without scaling.
Args:
from_int (bool, optional): if ``True``, the tensor will be scaled from
the range [0, 255] to the range [0.0, 1.0]. if `False``, the tensor
will not be scaled. if `None`, the tensor will be scaled if
it's not a floating-point tensor. default=None.
unsqueeze (bool): if ``True``, the observation tensor is unsqueezed
along the first dimension. default=False.
dtype (torch.dtype, optional): dtype to use for the resulting
observations.
Keyword arguments:
in_keys (list of NestedKeys): keys to process.
out_keys (list of NestedKeys): keys to write.
shape_tolerant (bool, optional): if ``True``, the shape of the input
images will be check. If the last channel is not `3`, the permuation
will be ignored. Defaults to ``False``.
Examples:
>>> transform = ToTensorImage(in_keys=["pixels"])
>>> ri = torch.randint(0, 255, (1 , 1, 10, 11, 3), dtype=torch.uint8)
>>> td = TensorDict(
... {"pixels": ri},
... [1, 1])
>>> _ = transform(td)
>>> obs = td.get("pixels")
>>> print(obs.shape, obs.dtype)
torch.Size([1, 1, 3, 10, 11]) torch.float32
"""
def __init__(
self,
from_int: Optional[bool] = None,
unsqueeze: bool = False,
dtype: Optional[torch.device] = None,
*,
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
shape_tolerant: bool = False,
):
if in_keys is None:
in_keys = IMAGE_KEYS # default
if out_keys is None:
out_keys = copy(in_keys)
super().__init__(in_keys=in_keys, out_keys=out_keys)
self.from_int = from_int
self.unsqueeze = unsqueeze
self.dtype = dtype if dtype is not None else torch.get_default_dtype()
self.shape_tolerant = shape_tolerant
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
with _set_missing_tolerance(self, True):
tensordict_reset = self._call(tensordict_reset)
return tensordict_reset
def _apply_transform(self, observation: torch.FloatTensor) -> torch.Tensor:
if not self.shape_tolerant or observation.shape[-1] == 3:
observation = observation.permute(
*list(range(observation.ndimension() - 3)), -1, -3, -2
)
if self.from_int or (
self.from_int is None and not torch.is_floating_point(observation)
):
observation = observation.div(255)
observation = observation.to(self.dtype)
if self._should_unsqueeze(observation):
observation = observation.unsqueeze(0)
return observation
[docs] @_apply_to_composite
def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
observation_spec = self._pixel_observation(observation_spec)
dim = [1] if self._should_unsqueeze(observation_spec) else []
if not self.shape_tolerant or observation_spec.shape[-1] == 3:
observation_spec.shape = torch.Size(
[
*dim,
*observation_spec.shape[:-3],
observation_spec.shape[-1],
observation_spec.shape[-3],
observation_spec.shape[-2],
]
)
observation_spec.dtype = self.dtype
return observation_spec
def _should_unsqueeze(self, observation_like: torch.FloatTensor | TensorSpec):
if isinstance(observation_like, torch.FloatTensor):
has_3_dimensions = observation_like.ndimension() == 3
else:
has_3_dimensions = len(observation_like.shape) == 3
return has_3_dimensions and self.unsqueeze
def _pixel_observation(self, spec: TensorSpec) -> None:
if isinstance(spec.space, ContinuousBox):
spec.space.high = self._apply_transform(spec.space.high)
spec.space.low = self._apply_transform(spec.space.low)
return spec
[docs]class ClipTransform(Transform):
"""A transform to clip input (state, action) or output (observation, reward) values.
This transform can take multiple input or output keys but only one value per
transform. If multiple clipping values are needed, several transforms should
be appended one after the other.
Args:
in_keys (list of NestedKeys): input entries (read)
out_keys (list of NestedKeys): input entries (write)
in_keys_inv (list of NestedKeys): input entries (read) during :meth:`~.inv` calls.
out_keys_inv (list of NestedKeys): input entries (write) during :meth:`~.inv` calls.
Keyword Args:
low (scalar, optional): the lower bound of the clipped space.
high (scalar, optional): the higher bound of the clipped space.
.. note:: Providing just one of the arguments ``low`` or ``high`` is permitted,
but at least one must be provided.
Examples:
>>> from torchrl.envs.libs.gym import GymEnv
>>> base_env = GymEnv("Pendulum-v1")
>>> env = TransformedEnv(base_env, ClipTransform(in_keys=['observation'], low=-1, high=0.1))
>>> r = env.rollout(100)
>>> assert (r["observation"] <= 0.1).all()
"""
def __init__(
self,
in_keys=None,
out_keys=None,
in_keys_inv=None,
out_keys_inv=None,
*,
low=None,
high=None,
):
if in_keys is None:
in_keys = []
if out_keys is None:
out_keys = copy(in_keys)
if in_keys_inv is None:
in_keys_inv = []
if out_keys_inv is None:
out_keys_inv = copy(in_keys_inv)
super().__init__(in_keys, out_keys, in_keys_inv, out_keys_inv)
if low is None and high is None:
raise TypeError("Either one or both of `high` and `low` must be provided.")
def check_val(val):
if (isinstance(val, torch.Tensor) and val.numel() > 1) or (
isinstance(val, np.ndarray) and val.size > 1
):
raise TypeError(
f"low and high must be scalars or None. Got low={low} and high={high}."
)
if val is None:
return None, None, torch.finfo(torch.get_default_dtype()).max
if not isinstance(val, torch.Tensor):
val = torch.as_tensor(val)
if not val.dtype.is_floating_point:
val = val.float()
eps = torch.finfo(val.dtype).resolution
ext = torch.finfo(val.dtype).max
return val, eps, ext
low, low_eps, low_min = check_val(low)
high, high_eps, high_max = check_val(high)
if low is not None and high is not None and low >= high:
raise ValueError("`low` must be stricly lower than `high`.")
self.register_buffer("low", low)
self.low_eps = low_eps
self.low_min = -low_min
self.register_buffer("high", high)
self.high_eps = high_eps
self.high_max = high_max
def _apply_transform(self, obs: torch.Tensor) -> None:
if self.low is None:
return obs.clamp_max(self.high)
elif self.high is None:
return obs.clamp_min(self.low)
return obs.clamp(self.low, self.high)
def _inv_apply_transform(self, state: torch.Tensor) -> torch.Tensor:
if self.low is None:
return state.clamp_max(self.high)
elif self.high is None:
return state.clamp_min(self.low)
return state.clamp(self.low, self.high)
[docs] @_apply_to_composite
def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
return Bounded(
shape=observation_spec.shape,
device=observation_spec.device,
dtype=observation_spec.dtype,
high=self.high + self.high_eps if self.high is not None else self.high_max,
low=self.low - self.low_eps if self.low is not None else self.low_min,
)
[docs] def transform_reward_spec(self, reward_spec: TensorSpec) -> TensorSpec:
for key in self.in_keys:
if key in self.parent.reward_keys:
spec = self.parent.output_spec["full_reward_spec"][key]
self.parent.output_spec["full_reward_spec"][key] = Bounded(
shape=spec.shape,
device=spec.device,
dtype=spec.dtype,
high=self.high + self.high_eps
if self.high is not None
else self.high_max,
low=self.low - self.low_eps
if self.low is not None
else self.low_min,
)
return self.parent.output_spec["full_reward_spec"]
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
with _set_missing_tolerance(self, True):
tensordict_reset = self._call(tensordict_reset)
return tensordict_reset
# No need to transform the input spec since the outside world won't see the difference
# def transform_input_spec(self, input_spec: TensorSpec) -> TensorSpec:
# ...
[docs]class TargetReturn(Transform):
"""Sets a target return for the agent to achieve in the environment.
In goal-conditioned RL, the :class:`~.TargetReturn` is defined as the
expected cumulative reward obtained from the current state to the goal state
or the end of the episode. It is used as input for the policy to guide its behavior.
For a trained policy typically the maximum return in the environment is
chosen as the target return.
However, as it is used as input to the policy module, it should be scaled
accordingly.
With the :class:`~.TargetReturn` transform, the tensordict can be updated
to include the user-specified target return.
The ``mode`` parameter can be used to specify
whether the target return gets updated at every step by subtracting the
reward achieved at each step or remains constant.
Args:
target_return (:obj:`float`): target return to be achieved by the agent.
mode (str): mode to be used to update the target return. Can be either "reduce" or "constant". Default: "reduce".
in_keys (sequence of NestedKey, optional): keys pointing to the reward
entries. Defaults to the reward keys of the parent env.
out_keys (sequence of NestedKey, optional): keys pointing to the
target keys. Defaults to a copy of in_keys where the last element
has been substituted by ``"target_return"``, and raises an exception
if these keys aren't unique.
reset_key (NestedKey, optional): the reset key to be used as partial
reset indicator. Must be unique. If not provided, defaults to the
only reset key of the parent environment (if it has only one)
and raises an exception otherwise.
Examples:
>>> from torchrl.envs import GymEnv
>>> env = TransformedEnv(
... GymEnv("CartPole-v1"),
... TargetReturn(10.0, mode="reduce"))
>>> env.set_seed(0)
>>> torch.manual_seed(0)
>>> env.rollout(20)['target_return'].squeeze()
tensor([10., 9., 8., 7., 6., 5., 4., 3., 2., 1., 0., -1., -2., -3.])
"""
MODES = ["reduce", "constant"]
MODE_ERR = "Mode can only be 'reduce' or 'constant'."
def __init__(
self,
target_return: float,
mode: str = "reduce",
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
reset_key: NestedKey | None = None,
):
if mode not in self.MODES:
raise ValueError(self.MODE_ERR)
super().__init__(in_keys=in_keys, out_keys=out_keys)
self.target_return = target_return
self.mode = mode
self.reset_key = reset_key
@property
def reset_key(self):
reset_key = self.__dict__.get("_reset_key", None)
if reset_key is None:
reset_keys = self.parent.reset_keys
if len(reset_keys) > 1:
raise RuntimeError(
f"Got more than one reset key in env {self.container}, cannot infer which one to use. Consider providing the reset key in the {type(self)} constructor."
)
reset_key = self._reset_key = reset_keys[0]
return reset_key
@reset_key.setter
def reset_key(self, value):
self._reset_key = value
@property
def in_keys(self):
in_keys = self.__dict__.get("_in_keys", None)
if in_keys is None:
in_keys = self.parent.reward_keys
self._in_keys = in_keys
return in_keys
@in_keys.setter
def in_keys(self, value):
self._in_keys = value
@property
def out_keys(self):
out_keys = self.__dict__.get("_out_keys", None)
if out_keys is None:
out_keys = [
_replace_last(in_key, "target_return") for in_key in self.in_keys
]
if len(set(out_keys)) < len(out_keys):
raise ValueError(
"Could not infer the target_return because multiple rewards are located at the same level."
)
self._out_keys = out_keys
return out_keys
@out_keys.setter
def out_keys(self, value):
self._out_keys = value
def _reset(self, tensordict: TensorDict, tensordict_reset: TensorDictBase):
_reset = _get_reset(self.reset_key, tensordict)
for out_key in self.out_keys:
target_return = tensordict.get(out_key, None)
if target_return is None:
target_return = torch.full(
size=(*tensordict.batch_size, 1),
fill_value=self.target_return,
dtype=torch.float32,
device=tensordict.device,
)
else:
target_return = torch.where(
expand_as_right(~_reset, target_return),
target_return,
self.target_return,
)
tensordict_reset.set(
out_key,
target_return,
)
return tensordict_reset
def _call(self, tensordict: TensorDict) -> TensorDict:
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys):
val_in = tensordict.get(in_key, None)
val_out = tensordict.get(out_key, None)
if val_in is not None:
target_return = self._apply_transform(
val_in,
val_out,
)
tensordict.set(out_key, target_return)
elif not self.missing_tolerance:
raise KeyError(f"'{in_key}' not found in tensordict {tensordict}")
return tensordict
def _step(
self, tensordict: TensorDictBase, next_tensordict: TensorDictBase
) -> TensorDictBase:
for out_key in self.out_keys:
next_tensordict.set(out_key, tensordict.get(out_key))
return super()._step(tensordict, next_tensordict)
def _apply_transform(
self, reward: torch.Tensor, target_return: torch.Tensor
) -> torch.Tensor:
if target_return.shape != reward.shape:
raise ValueError(
f"The shape of the reward ({reward.shape}) and target return ({target_return.shape}) must match."
)
if self.mode == "reduce":
target_return = target_return - reward
return target_return
elif self.mode == "constant":
target_return = target_return
return target_return
else:
raise ValueError("Unknown mode: {}".format(self.mode))
[docs] def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
raise NotImplementedError(
FORWARD_NOT_IMPLEMENTED.format(self.__class__.__name__)
)
[docs] def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys):
if in_key in self.parent.full_observation_spec.keys(True):
target = self.parent.full_observation_spec[in_key]
elif in_key in self.parent.full_reward_spec.keys(True):
target = self.parent.full_reward_spec[in_key]
elif in_key in self.parent.full_done_spec.keys(True):
# we account for this for completeness but it should never be the case
target = self.parent.full_done_spec[in_key]
else:
raise RuntimeError(f"in_key {in_key} not found in output_spec.")
target_return_spec = Unbounded(
shape=target.shape,
dtype=target.dtype,
device=target.device,
)
# because all reward keys are discarded from the data during calls
# to step_mdp, we must put this in observation_spec
observation_spec[out_key] = target_return_spec
return observation_spec
[docs] def transform_input_spec(self, input_spec: TensorSpec) -> TensorSpec:
# we must add the target return to the input spec
input_spec["full_state_spec"] = self.transform_observation_spec(
input_spec["full_state_spec"]
)
return input_spec
[docs]class RewardClipping(Transform):
"""Clips the reward between `clamp_min` and `clamp_max`.
Args:
clip_min (scalar): minimum value of the resulting reward.
clip_max (scalar): maximum value of the resulting reward.
"""
def __init__(
self,
clamp_min: float = None,
clamp_max: float = None,
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
):
if in_keys is None:
in_keys = ["reward"]
if out_keys is None:
out_keys = copy(in_keys)
super().__init__(in_keys=in_keys, out_keys=out_keys)
clamp_min_tensor = (
clamp_min if isinstance(clamp_min, Tensor) else torch.as_tensor(clamp_min)
)
clamp_max_tensor = (
clamp_max if isinstance(clamp_max, Tensor) else torch.as_tensor(clamp_max)
)
self.register_buffer("clamp_min", clamp_min_tensor)
self.register_buffer("clamp_max", clamp_max_tensor)
def _apply_transform(self, reward: torch.Tensor) -> torch.Tensor:
if self.clamp_max is not None and self.clamp_min is not None:
reward = reward.clamp(self.clamp_min, self.clamp_max)
elif self.clamp_min is not None:
reward = reward.clamp_min(self.clamp_min)
elif self.clamp_max is not None:
reward = reward.clamp_max(self.clamp_max)
return reward
[docs] @_apply_to_composite
def transform_reward_spec(self, reward_spec: TensorSpec) -> TensorSpec:
if isinstance(reward_spec, Unbounded):
return Bounded(
self.clamp_min,
self.clamp_max,
shape=reward_spec.shape,
device=reward_spec.device,
dtype=reward_spec.dtype,
)
else:
raise NotImplementedError(
f"{self.__class__.__name__}.transform_reward_spec not "
f"implemented for tensor spec of type"
f" {type(reward_spec).__name__}"
)
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}("
f"clamp_min={float(self.clamp_min):4.4f}, clamp_max"
f"={float(self.clamp_max):4.4f}, keys={self.in_keys})"
)
[docs]class BinarizeReward(Transform):
"""Maps the reward to a binary value (0 or 1) if the reward is null or non-null, respectively.
Args:
in_keys (List[NestedKey]): input keys
out_keys (List[NestedKey], optional): output keys. Defaults to value
of ``in_keys``.
dtype (torch.dtype, optional): the dtype of the binerized reward.
Defaults to ``torch.int8``.
"""
def __init__(
self,
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
):
if in_keys is None:
in_keys = ["reward"]
if out_keys is None:
out_keys = copy(in_keys)
super().__init__(in_keys=in_keys, out_keys=out_keys)
def _apply_transform(self, reward: torch.Tensor) -> torch.Tensor:
if not reward.shape or reward.shape[-1] != 1:
raise RuntimeError(
f"Reward shape last dimension must be singleton, got reward of shape {reward.shape}"
)
return (reward > 0.0).to(torch.int8)
[docs] @_apply_to_composite
def transform_reward_spec(self, reward_spec: TensorSpec) -> TensorSpec:
return Binary(
n=1,
device=reward_spec.device,
shape=reward_spec.shape,
)
[docs]class Resize(ObservationTransform):
"""Resizes a pixel observation.
Args:
w (int): resulting width.
h (int, optional): resulting height. If not provided, the value of `w`
is taken.
interpolation (str): interpolation method
Examples:
>>> from torchrl.envs import GymEnv
>>> t = Resize(64, 84)
>>> base_env = GymEnv("HalfCheetah-v4", from_pixels=True)
>>> env = TransformedEnv(base_env, Compose(ToTensorImage(), t))
"""
def __init__(
self,
w: int,
h: int | None = None,
interpolation: str = "bilinear",
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
):
# we also allow lists or tuples
if isinstance(w, (list, tuple)):
w, h = w
if h is None:
h = w
if not _has_tv:
raise ImportError(
"Torchvision not found. The Resize transform relies on "
"torchvision implementation. "
"Consider installing this dependency."
)
if in_keys is None:
in_keys = IMAGE_KEYS # default
if out_keys is None:
out_keys = copy(in_keys)
super().__init__(in_keys=in_keys, out_keys=out_keys)
self.w = int(w)
self.h = int(h)
try:
from torchvision.transforms.functional import InterpolationMode
def interpolation_fn(interpolation): # noqa: D103
return InterpolationMode(interpolation)
except ImportError:
def interpolation_fn(interpolation): # noqa: D103
return interpolation
self.interpolation = interpolation_fn(interpolation)
def _apply_transform(self, observation: torch.Tensor) -> torch.Tensor:
# flatten if necessary
if observation.shape[-2:] == torch.Size([self.w, self.h]):
return observation
ndim = observation.ndimension()
if ndim > 4:
sizes = observation.shape[:-3]
observation = torch.flatten(observation, 0, ndim - 4)
try:
from torchvision.transforms.functional import resize
except ImportError:
from torchvision.transforms.functional_tensor import resize
observation = resize(
observation,
[self.w, self.h],
interpolation=self.interpolation,
antialias=True,
)
if ndim > 4:
observation = observation.unflatten(0, sizes)
return observation
[docs] @_apply_to_composite
def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
space = observation_spec.space
if isinstance(space, ContinuousBox):
space.low = self._apply_transform(space.low)
space.high = self._apply_transform(space.high)
observation_spec.shape = space.low.shape
else:
observation_spec.shape = self._apply_transform(
torch.zeros(observation_spec.shape)
).shape
return observation_spec
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}("
f"w={int(self.w)}, h={int(self.h)}, "
f"interpolation={self.interpolation}, keys={self.in_keys})"
)
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
with _set_missing_tolerance(self, True):
tensordict_reset = self._call(tensordict_reset)
return tensordict_reset
[docs]class Crop(ObservationTransform):
"""Crops the input image at the specified location and output size.
Args:
w (int): resulting width
h (int, optional): resulting height. If None, then w is used (square crop).
top (int, optional): top pixel coordinate to start cropping. Default is 0, i.e. top of the image.
left (int, optional): left pixel coordinate to start cropping. Default is 0, i.e. left of the image.
in_keys (sequence of NestedKey, optional): the entries to crop. If none is provided,
``["pixels"]`` is assumed.
out_keys (sequence of NestedKey, optional): the cropped images keys. If none is
provided, ``in_keys`` is assumed.
"""
def __init__(
self,
w: int,
h: int = None,
top: int = 0,
left: int = 0,
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
):
if in_keys is None:
in_keys = IMAGE_KEYS # default
if out_keys is None:
out_keys = copy(in_keys)
super().__init__(in_keys=in_keys, out_keys=out_keys)
self.w = w
self.h = h if h else w
self.top = top
self.left = left
def _apply_transform(self, observation: torch.Tensor) -> torch.Tensor:
from torchvision.transforms.functional import crop
observation = crop(observation, self.top, self.left, self.w, self.h)
return observation
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
with _set_missing_tolerance(self, True):
tensordict_reset = self._call(tensordict_reset)
return tensordict_reset
[docs] @_apply_to_composite
def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
space = observation_spec.space
if isinstance(space, ContinuousBox):
space.low = self._apply_transform(space.low)
space.high = self._apply_transform(space.high)
observation_spec.shape = space.low.shape
else:
observation_spec.shape = self._apply_transform(
torch.zeros(observation_spec.shape)
).shape
return observation_spec
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}("
f"w={float(self.w):4.4f}, h={float(self.h):4.4f}, top={float(self.top):4.4f}, left={float(self.left):4.4f}, "
)
[docs]class CenterCrop(ObservationTransform):
"""Crops the center of an image.
Args:
w (int): resulting width
h (int, optional): resulting height. If None, then w is used (square crop).
in_keys (sequence of NestedKey, optional): the entries to crop. If none is provided,
:obj:`["pixels"]` is assumed.
out_keys (sequence of NestedKey, optional): the cropped images keys. If none is
provided, :obj:`in_keys` is assumed.
"""
def __init__(
self,
w: int,
h: int = None,
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
):
if in_keys is None:
in_keys = IMAGE_KEYS # default
if out_keys is None:
out_keys = copy(in_keys)
super().__init__(in_keys=in_keys, out_keys=out_keys)
self.w = w
self.h = h if h else w
def _apply_transform(self, observation: torch.Tensor) -> torch.Tensor:
from torchvision.transforms.functional import center_crop
observation = center_crop(observation, [self.w, self.h])
return observation
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
with _set_missing_tolerance(self, True):
tensordict_reset = self._call(tensordict_reset)
return tensordict_reset
[docs] @_apply_to_composite
def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
space = observation_spec.space
if isinstance(space, ContinuousBox):
space.low = self._apply_transform(space.low)
space.high = self._apply_transform(space.high)
observation_spec.shape = space.low.shape
else:
observation_spec.shape = self._apply_transform(
torch.zeros(observation_spec.shape)
).shape
return observation_spec
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}("
f"w={float(self.w):4.4f}, h={float(self.h):4.4f}, "
)
[docs]class FlattenObservation(ObservationTransform):
"""Flatten adjacent dimensions of a tensor.
Args:
first_dim (int): first dimension of the dimensions to flatten.
last_dim (int): last dimension of the dimensions to flatten.
in_keys (sequence of NestedKey, optional): the entries to flatten. If none is provided,
:obj:`["pixels"]` is assumed.
out_keys (sequence of NestedKey, optional): the flatten observation keys. If none is
provided, :obj:`in_keys` is assumed.
allow_positive_dim (bool, optional): if ``True``, positive dimensions are accepted.
:obj:`FlattenObservation` will map these to the n^th feature dimension
(ie n^th dimension after batch size of parent env) of the input tensor.
Defaults to False, ie. non-negative dimensions are not permitted.
"""
def __init__(
self,
first_dim: int,
last_dim: int,
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
allow_positive_dim: bool = False,
):
if in_keys is None:
in_keys = IMAGE_KEYS # default
if out_keys is None:
out_keys = copy(in_keys)
super().__init__(in_keys=in_keys, out_keys=out_keys)
if not allow_positive_dim and first_dim >= 0:
raise ValueError(
"first_dim should be smaller than 0 to accommodate for "
"envs of different batch_sizes."
)
if not allow_positive_dim and last_dim >= 0:
raise ValueError(
"last_dim should be smaller than 0 to accommodate for "
"envs of different batch_sizes."
)
self._first_dim = first_dim
self._last_dim = last_dim
@property
def first_dim(self):
if self._first_dim >= 0 and self.parent is not None:
return len(self.parent.batch_size) + self._first_dim
return self._first_dim
@property
def last_dim(self):
if self._last_dim >= 0 and self.parent is not None:
return len(self.parent.batch_size) + self._last_dim
return self._last_dim
def _apply_transform(self, observation: torch.Tensor) -> torch.Tensor:
observation = torch.flatten(observation, self.first_dim, self.last_dim)
return observation
forward = ObservationTransform._call
[docs] @_apply_to_composite
def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
space = observation_spec.space
if isinstance(space, ContinuousBox):
space.low = self._apply_transform(space.low)
space.high = self._apply_transform(space.high)
observation_spec.shape = space.low.shape
else:
observation_spec.shape = self._apply_transform(
torch.zeros(observation_spec.shape)
).shape
return observation_spec
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
with _set_missing_tolerance(self, True):
return self._call(tensordict_reset)
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}("
f"first_dim={int(self.first_dim)}, last_dim={int(self.last_dim)}, in_keys={self.in_keys}, out_keys={self.out_keys})"
)
[docs]class UnsqueezeTransform(Transform):
"""Inserts a dimension of size one at the specified position.
Args:
dim (int): dimension to unsqueeze. Must be negative (or allow_positive_dim
must be turned on).
Keyword Args:
allow_positive_dim (bool, optional): if ``True``, positive dimensions are accepted.
`UnsqueezeTransform`` will map these to the n^th feature dimension
(ie n^th dimension after batch size of parent env) of the input tensor,
independently of the tensordict batch size (ie positive dims may be
dangerous in contexts where tensordict of different batch dimension
are passed).
Defaults to False, ie. non-negative dimensions are not permitted.
in_keys (list of NestedKeys): input entries (read).
out_keys (list of NestedKeys): input entries (write). Defaults to ``in_keys`` if
not provided.
in_keys_inv (list of NestedKeys): input entries (read) during :meth:`~.inv` calls.
out_keys_inv (list of NestedKeys): input entries (write) during :meth:`~.inv` calls.
Defaults to ``in_keys_in`` if not provided.
"""
invertible = True
@classmethod
def __new__(cls, *args, **kwargs):
cls._dim = None
return super().__new__(cls)
def __init__(
self,
dim: int = None,
*,
allow_positive_dim: bool = False,
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
in_keys_inv: Sequence[NestedKey] | None = None,
out_keys_inv: Sequence[NestedKey] | None = None,
):
if in_keys is None:
in_keys = [] # default
if out_keys is None:
out_keys = copy(in_keys)
if in_keys_inv is None:
in_keys_inv = [] # default
if out_keys_inv is None:
out_keys_inv = copy(in_keys_inv)
super().__init__(
in_keys=in_keys,
out_keys=out_keys,
in_keys_inv=in_keys_inv,
out_keys_inv=out_keys_inv,
)
self.allow_positive_dim = allow_positive_dim
if dim >= 0 and not allow_positive_dim:
raise RuntimeError(
"dim should be smaller than 0 to accommodate for "
"envs of different batch_sizes. Turn allow_positive_dim to accommodate "
"for positive dim."
)
self._dim = dim
@property
def unsqueeze_dim(self):
return self.dim
@property
def dim(self):
if self._dim >= 0 and self.parent is not None:
return len(self.parent.batch_size) + self._dim
return self._dim
def _apply_transform(self, observation: torch.Tensor) -> torch.Tensor:
observation = observation.unsqueeze(self.dim)
return observation
def _inv_apply_transform(self, observation: torch.Tensor) -> torch.Tensor:
observation = observation.squeeze(self.dim)
return observation
def _transform_spec(self, spec: TensorSpec):
space = spec.space
if isinstance(space, ContinuousBox):
space.low = self._apply_transform(space.low)
space.high = self._apply_transform(space.high)
spec.shape = space.low.shape
else:
spec.shape = self._apply_transform(torch.zeros(spec.shape)).shape
return spec
def _inv_transform_spec(self, spec: TensorSpec) -> None:
space = spec.space
if isinstance(space, ContinuousBox):
space.low = self._inv_apply_transform(space.low)
space.high = self._inv_apply_transform(space.high)
spec.shape = space.low.shape
else:
spec.shape = self._inv_apply_transform(torch.zeros(spec.shape)).shape
return spec
[docs] @_apply_to_composite_inv
def transform_input_spec(self, input_spec: TensorSpec) -> TensorSpec:
return self._inv_transform_spec(input_spec)
[docs] @_apply_to_composite
def transform_reward_spec(self, reward_spec: TensorSpec) -> TensorSpec:
reward_key = self.parent.reward_key if self.parent is not None else "reward"
if reward_key in self.in_keys:
reward_spec = self._transform_spec(reward_spec)
return reward_spec
[docs] @_apply_to_composite
def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
return self._transform_spec(observation_spec)
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
with _set_missing_tolerance(self, True):
tensordict_reset = self._call(tensordict_reset)
return tensordict_reset
def __repr__(self) -> str:
s = (
f"{self.__class__.__name__}(dim={self.dim}, in_keys={self.in_keys}, out_keys={self.out_keys},"
f" in_keys_inv={self.in_keys_inv}, out_keys_inv={self.out_keys_inv})"
)
return s
[docs]class SqueezeTransform(UnsqueezeTransform):
"""Removes a dimension of size one at the specified position.
Args:
dim (int): dimension to squeeze.
"""
invertible = True
def __init__(
self,
dim: int | None = None,
*args,
in_keys: Optional[Sequence[str]] = None,
out_keys: Optional[Sequence[str]] = None,
in_keys_inv: Optional[Sequence[str]] = None,
out_keys_inv: Optional[Sequence[str]] = None,
**kwargs,
):
if dim is None:
if "squeeze_dim" in kwargs:
warnings.warn(
f"squeeze_dim will be deprecated in favor of dim arg in {type(self).__name__}."
)
dim = kwargs.pop("squeeze_dim")
else:
raise TypeError(
f"dim must be passed to {type(self).__name__} constructor."
)
super().__init__(
dim,
*args,
in_keys=in_keys,
out_keys=out_keys,
in_keys_inv=in_keys_inv,
out_keys_inv=out_keys_inv,
**kwargs,
)
@property
def squeeze_dim(self):
return super().dim
_apply_transform = UnsqueezeTransform._inv_apply_transform
_inv_apply_transform = UnsqueezeTransform._apply_transform
[docs]class PermuteTransform(Transform):
"""Permutation transform.
Permutes input tensors along the desired dimensions. The permutations
must be provided along the feature dimension (not batch dimension).
Args:
dims (list of int): the permuted order of the dimensions. Must be a reordering
of the dims ``[-(len(dims)), ..., -1]``.
in_keys (list of NestedKeys): input entries (read).
out_keys (list of NestedKeys): input entries (write). Defaults to ``in_keys`` if
not provided.
in_keys_inv (list of NestedKeys): input entries (read) during :meth:`~.inv` calls.
out_keys_inv (list of NestedKeys): input entries (write) during :meth:`~.inv` calls. Defaults to ``in_keys_in`` if
not provided.
Examples:
>>> from torchrl.envs.libs.gym import GymEnv
>>> base_env = GymEnv("ALE/Pong-v5")
>>> base_env.rollout(2)
TensorDict(
fields={
action: Tensor(shape=torch.Size([2, 6]), device=cpu, dtype=torch.int64, is_shared=False),
done: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
pixels: Tensor(shape=torch.Size([2, 210, 160, 3]), device=cpu, dtype=torch.uint8, is_shared=False),
reward: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([2]),
device=cpu,
is_shared=False),
pixels: Tensor(shape=torch.Size([2, 210, 160, 3]), device=cpu, dtype=torch.uint8, is_shared=False)},
batch_size=torch.Size([2]),
device=cpu,
is_shared=False)
>>> env = TransformedEnv(base_env, PermuteTransform((-1, -3, -2), in_keys=["pixels"]))
>>> env.rollout(2) # channels are at the end
TensorDict(
fields={
action: Tensor(shape=torch.Size([2, 6]), device=cpu, dtype=torch.int64, is_shared=False),
done: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
pixels: Tensor(shape=torch.Size([2, 3, 210, 160]), device=cpu, dtype=torch.uint8, is_shared=False),
reward: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([2]),
device=cpu,
is_shared=False),
pixels: Tensor(shape=torch.Size([2, 3, 210, 160]), device=cpu, dtype=torch.uint8, is_shared=False)},
batch_size=torch.Size([2]),
device=cpu,
is_shared=False)
"""
def __init__(
self,
dims,
in_keys=None,
out_keys=None,
in_keys_inv=None,
out_keys_inv=None,
):
if in_keys is None:
in_keys = []
if out_keys is None:
out_keys = copy(in_keys)
if in_keys_inv is None:
in_keys_inv = []
if out_keys_inv is None:
out_keys_inv = copy(in_keys_inv)
super().__init__(
in_keys=in_keys,
out_keys=out_keys,
in_keys_inv=in_keys_inv,
out_keys_inv=out_keys_inv,
)
# check dims
self.dims = dims
if sorted(dims) != list(range(-len(dims), 0)):
raise ValueError(
f"Only tailing dims with negative indices are supported by {self.__class__.__name__}. Got {dims} instead."
)
@staticmethod
def _invert_permute(p):
def _find_inv(i):
for j, _p in enumerate(p):
if _p < 0:
inv = True
_p = len(p) + _p
else:
inv = False
if i == _p:
if inv:
return j - len(p)
else:
return j
else:
# unreachable
raise RuntimeError
return [_find_inv(i) for i in range(len(p))]
def _apply_transform(self, observation: torch.FloatTensor) -> torch.Tensor:
observation = observation.permute(
*list(range(observation.ndimension() - len(self.dims))), *self.dims
)
return observation
def _inv_apply_transform(self, state: torch.Tensor) -> torch.Tensor:
permuted_dims = self._invert_permute(self.dims)
state = state.permute(
*list(range(state.ndimension() - len(self.dims))), *permuted_dims
)
return state
[docs] @_apply_to_composite
def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
observation_spec = self._edit_space(observation_spec)
observation_spec.shape = torch.Size(
[
*observation_spec.shape[: -len(self.dims)],
*[observation_spec.shape[dim] for dim in self.dims],
]
)
return observation_spec
[docs] @_apply_to_composite_inv
def transform_input_spec(self, input_spec: TensorSpec) -> TensorSpec:
permuted_dims = self._invert_permute(self.dims)
input_spec = self._edit_space_inv(input_spec)
input_spec.shape = torch.Size(
[
*input_spec.shape[: -len(permuted_dims)],
*[input_spec.shape[dim] for dim in permuted_dims],
]
)
return input_spec
def _edit_space(self, spec: TensorSpec) -> None:
if isinstance(spec.space, ContinuousBox):
spec.space.high = self._apply_transform(spec.space.high)
spec.space.low = self._apply_transform(spec.space.low)
return spec
def _edit_space_inv(self, spec: TensorSpec) -> None:
if isinstance(spec.space, ContinuousBox):
spec.space.high = self._inv_apply_transform(spec.space.high)
spec.space.low = self._inv_apply_transform(spec.space.low)
return spec
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
with _set_missing_tolerance(self, True):
tensordict_reset = self._call(tensordict_reset)
return tensordict_reset
[docs]class GrayScale(ObservationTransform):
"""Turns a pixel observation to grayscale."""
def __init__(
self,
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
):
if in_keys is None:
in_keys = IMAGE_KEYS
if out_keys is None:
out_keys = copy(in_keys)
super().__init__(in_keys=in_keys, out_keys=out_keys)
def _apply_transform(self, observation: torch.Tensor) -> torch.Tensor:
observation = F.rgb_to_grayscale(observation)
return observation
[docs] @_apply_to_composite
def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
space = observation_spec.space
if isinstance(space, ContinuousBox):
space.low = self._apply_transform(space.low)
space.high = self._apply_transform(space.high)
observation_spec.shape = space.low.shape
else:
observation_spec.shape = self._apply_transform(
torch.zeros(observation_spec.shape)
).shape
return observation_spec
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
with _set_missing_tolerance(self, True):
tensordict_reset = self._call(tensordict_reset)
return tensordict_reset
[docs]class ObservationNorm(ObservationTransform):
"""Observation affine transformation layer.
Normalizes an observation according to
.. math::
obs = obs * scale + loc
Args:
loc (number or tensor): location of the affine transform
scale (number or tensor): scale of the affine transform
in_keys (sequence of NestedKey, optional): entries to be normalized. Defaults to ["observation", "pixels"].
All entries will be normalized with the same values: if a different behavior is desired
(e.g. a different normalization for pixels and states) different :obj:`ObservationNorm`
objects should be used.
out_keys (sequence of NestedKey, optional): output entries. Defaults to the value of `in_keys`.
in_keys_inv (sequence of NestedKey, optional): ObservationNorm also supports inverse transforms. This will
only occur if a list of keys is provided to :obj:`in_keys_inv`. If none is provided,
only the forward transform will be called.
out_keys_inv (sequence of NestedKey, optional): output entries for the inverse transform.
Defaults to the value of `in_keys_inv`.
standard_normal (bool, optional): if ``True``, the transform will be
.. math::
obs = (obs-loc)/scale
as it is done for standardization. Default is `False`.
eps (:obj:`float`, optional): epsilon increment for the scale in the ``standard_normal`` case.
Defaults to ``1e-6`` if not recoverable directly from the scale dtype.
Examples:
>>> torch.set_default_tensor_type(torch.DoubleTensor)
>>> r = torch.randn(100, 3)*torch.randn(3) + torch.randn(3)
>>> td = TensorDict({'obs': r}, [100])
>>> transform = ObservationNorm(
... loc = td.get('obs').mean(0),
... scale = td.get('obs').std(0),
... in_keys=["obs"],
... standard_normal=True)
>>> _ = transform(td)
>>> print(torch.isclose(td.get('obs').mean(0),
... torch.zeros(3)).all())
tensor(True)
>>> print(torch.isclose(td.get('next_obs').std(0),
... torch.ones(3)).all())
tensor(True)
The normalization stats can be automatically computed:
Examples:
>>> from torchrl.envs.libs.gym import GymEnv
>>> torch.manual_seed(0)
>>> env = GymEnv("Pendulum-v1")
>>> env = TransformedEnv(env, ObservationNorm(in_keys=["observation"]))
>>> env.set_seed(0)
>>> env.transform.init_stats(100)
>>> print(env.transform.loc, env.transform.scale)
tensor([-1.3752e+01, -6.5087e-03, 2.9294e-03], dtype=torch.float32) tensor([14.9636, 2.5608, 0.6408], dtype=torch.float32)
"""
_ERR_INIT_MSG = "Cannot have an mixed initialized and uninitialized loc and scale"
def __init__(
self,
loc: Optional[float, torch.Tensor] = None,
scale: Optional[float, torch.Tensor] = None,
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
in_keys_inv: Sequence[NestedKey] | None = None,
out_keys_inv: Sequence[NestedKey] | None = None,
standard_normal: bool = False,
eps: float | None = None,
):
if in_keys is None:
raise RuntimeError(
"Not passing in_keys to ObservationNorm is a deprecated behavior."
)
if out_keys is None:
out_keys = copy(in_keys)
if in_keys_inv is None:
in_keys_inv = []
if out_keys_inv is None:
out_keys_inv = copy(in_keys_inv)
super().__init__(
in_keys=in_keys,
out_keys=out_keys,
in_keys_inv=in_keys_inv,
out_keys_inv=out_keys_inv,
)
if not isinstance(standard_normal, torch.Tensor):
standard_normal = torch.as_tensor(standard_normal)
self.register_buffer("standard_normal", standard_normal)
self.eps = (
eps
if eps is not None
else torch.finfo(scale.dtype).eps
if isinstance(scale, torch.Tensor) and scale.dtype.is_floating_point
else 1e-6
)
if loc is not None and not isinstance(loc, torch.Tensor):
loc = torch.tensor(loc, dtype=torch.get_default_dtype())
elif loc is None:
if scale is not None:
raise ValueError(self._ERR_INIT_MSG)
loc = nn.UninitializedBuffer()
if scale is not None and not isinstance(scale, torch.Tensor):
scale = torch.tensor(scale, dtype=torch.get_default_dtype())
scale = scale.clamp_min(self.eps)
elif scale is None:
# check that loc is None too
if not isinstance(loc, nn.UninitializedBuffer):
raise ValueError(self._ERR_INIT_MSG)
scale = nn.UninitializedBuffer()
# self.observation_spec_key = observation_spec_key
self.register_buffer("loc", loc)
self.register_buffer("scale", scale)
@property
def initialized(self):
return not isinstance(self.loc, nn.UninitializedBuffer)
[docs] def init_stats(
self,
num_iter: int,
reduce_dim: Union[int, Tuple[int]] = 0,
cat_dim: Optional[int] = None,
key: Optional[NestedKey] = None,
keep_dims: Optional[Tuple[int]] = None,
) -> None:
"""Initializes the loc and scale stats of the parent environment.
Normalization constant should ideally make the observation statistics approach
those of a standard Gaussian distribution. This method computes a location
and scale tensor that will empirically compute the mean and standard
deviation of a Gaussian distribution fitted on data generated randomly with
the parent environment for a given number of steps.
Args:
num_iter (int): number of random iterations to run in the environment.
reduce_dim (int or tuple of int, optional): dimension to compute the mean and std over.
Defaults to 0.
cat_dim (int, optional): dimension along which the batches collected will be concatenated.
It must be part equal to reduce_dim (if integer) or part of the reduce_dim tuple.
Defaults to the same value as reduce_dim.
key (NestedKey, optional): if provided, the summary statistics will be
retrieved from that key in the resulting tensordicts.
Otherwise, the first key in :obj:`ObservationNorm.in_keys` will be used.
keep_dims (tuple of int, optional): the dimensions to keep in the loc and scale.
For instance, one may want the location and scale to have shape [C, 1, 1]
when normalizing a 3D tensor over the last two dimensions, but not the
third. Defaults to None.
"""
if cat_dim is None:
cat_dim = reduce_dim
if not isinstance(cat_dim, int):
raise ValueError(
"cat_dim must be specified if reduce_dim is not an integer."
)
if (isinstance(reduce_dim, tuple) and cat_dim not in reduce_dim) or (
isinstance(reduce_dim, int) and cat_dim != reduce_dim
):
raise ValueError("cat_dim must be part of or equal to reduce_dim.")
if self.initialized:
raise RuntimeError(
f"Loc/Scale are already initialized: ({self.loc}, {self.scale})"
)
if len(self.in_keys) > 1 and key is None:
raise RuntimeError(
"Transform has multiple in_keys but no specific key was passed as an argument"
)
key = self.in_keys[0] if key is None else key
def raise_initialization_exception(module):
if isinstance(module, ObservationNorm) and not module.initialized:
raise RuntimeError(
"ObservationNorms need to be initialized in the right order."
"Trying to initialize an ObservationNorm "
"while a parent ObservationNorm transform is still uninitialized"
)
parent = self.parent
if parent is None:
raise RuntimeError(
"Cannot initialize the transform if parent env is not defined."
)
parent.apply(raise_initialization_exception)
collected_frames = 0
data = []
while collected_frames < num_iter:
tensordict = parent.rollout(max_steps=num_iter)
collected_frames += tensordict.numel()
data.append(tensordict.get(key))
data = torch.cat(data, cat_dim)
if isinstance(reduce_dim, int):
reduce_dim = [reduce_dim]
# make all reduce_dim and keep_dims negative
reduce_dim = sorted(dim if dim < 0 else dim - data.ndim for dim in reduce_dim)
if keep_dims is not None:
keep_dims = sorted(dim if dim < 0 else dim - data.ndim for dim in keep_dims)
if not all(k in reduce_dim for k in keep_dims):
raise ValueError("keep_dim elements must be part of reduce_dim list.")
else:
keep_dims = []
loc = data.mean(reduce_dim, keepdim=True)
scale = data.std(reduce_dim, keepdim=True)
for r in reduce_dim:
if r not in keep_dims:
loc = loc.squeeze(r)
scale = scale.squeeze(r)
if not self.standard_normal:
scale = 1 / scale.clamp_min(self.eps)
loc = -loc * scale
if not torch.isfinite(loc).all():
raise RuntimeError("Non-finite values found in loc")
if not torch.isfinite(scale).all():
raise RuntimeError("Non-finite values found in scale")
self.loc.materialize(shape=loc.shape, dtype=loc.dtype)
self.loc.copy_(loc)
self.scale.materialize(shape=scale.shape, dtype=scale.dtype)
self.scale.copy_(scale.clamp_min(self.eps))
def _apply_transform(self, obs: torch.Tensor) -> torch.Tensor:
if not self.initialized:
raise RuntimeError(
"Loc/Scale have not been initialized. Either pass in values in the constructor "
"or call the init_stats method"
)
if self.standard_normal:
loc = self.loc
scale = self.scale
return (obs - loc) / scale
else:
scale = self.scale
loc = self.loc
return obs * scale + loc
def _inv_apply_transform(self, state: torch.Tensor) -> torch.Tensor:
if self.loc is None or self.scale is None:
raise RuntimeError(
"Loc/Scale have not been initialized. Either pass in values in the constructor "
"or call the init_stats method"
)
if not self.standard_normal:
loc = self.loc
scale = self.scale
return (state - loc) / scale
else:
scale = self.scale
loc = self.loc
return state * scale + loc
[docs] @_apply_to_composite
def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
space = observation_spec.space
if isinstance(space, ContinuousBox):
space.low = self._apply_transform(space.low)
space.high = self._apply_transform(space.high)
return observation_spec
[docs] @_apply_to_composite_inv
def transform_input_spec(self, input_spec: TensorSpec) -> TensorSpec:
space = input_spec.space
if isinstance(space, ContinuousBox):
space.low = self._apply_transform(space.low)
space.high = self._apply_transform(space.high)
return input_spec
def __repr__(self) -> str:
if self.initialized and (self.loc.numel() == 1 and self.scale.numel() == 1):
return (
f"{self.__class__.__name__}("
f"loc={float(self.loc):4.4f}, scale"
f"={float(self.scale):4.4f}, keys={self.in_keys})"
)
else:
return super().__repr__()
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
with _set_missing_tolerance(self, True):
tensordict_reset = self._call(tensordict_reset)
return tensordict_reset
[docs]class CatFrames(ObservationTransform):
"""Concatenates successive observation frames into a single tensor.
This transform is useful for creating a sense of movement or velocity in the observed features.
It can also be used with models that require access to past observations such as transformers and the like.
It was first proposed in "Playing Atari with Deep Reinforcement Learning" (https://arxiv.org/pdf/1312.5602.pdf).
When used within a transformed environment,
:class:`CatFrames` is a stateful class, and it can be reset to its native state by
calling the :meth:`~.reset` method. This method accepts tensordicts with a
``"_reset"`` entry that indicates which buffer to reset.
Args:
N (int): number of observation to concatenate.
dim (int): dimension along which concatenate the
observations. Should be negative, to ensure that it is compatible
with environments of different batch_size.
in_keys (sequence of NestedKey, optional): keys pointing to the frames that have
to be concatenated. Defaults to ["pixels"].
out_keys (sequence of NestedKey, optional): keys pointing to where the output
has to be written. Defaults to the value of `in_keys`.
padding (str, optional): the padding method. One of ``"same"`` or ``"constant"``.
Defaults to ``"same"``, ie. the first value is used for padding.
padding_value (:obj:`float`, optional): the value to use for padding if ``padding="constant"``.
Defaults to 0.
as_inverse (bool, optional): if ``True``, the transform is applied as an inverse transform. Defaults to ``False``.
reset_key (NestedKey, optional): the reset key to be used as partial
reset indicator. Must be unique. If not provided, defaults to the
only reset key of the parent environment (if it has only one)
and raises an exception otherwise.
done_key (NestedKey, optional): the done key to be used as partial
done indicator. Must be unique. If not provided, defaults to ``"done"``.
Examples:
>>> from torchrl.envs.libs.gym import GymEnv
>>> env = TransformedEnv(GymEnv('Pendulum-v1'),
... Compose(
... UnsqueezeTransform(-1, in_keys=["observation"]),
... CatFrames(N=4, dim=-1, in_keys=["observation"]),
... )
... )
>>> print(env.rollout(3))
The :class:`CatFrames` transform can also be used offline to reproduce the
effect of the online frame concatenation at a different scale (or for the
purpose of limiting the memory consumption). The followin example
gives the complete picture, together with the usage of a :class:`torchrl.data.ReplayBuffer`:
Examples:
>>> from torchrl.envs.utils import RandomPolicy >>> from torchrl.envs import UnsqueezeTransform, CatFrames
>>> from torchrl.collectors import SyncDataCollector
>>> # Create a transformed environment with CatFrames: notice the usage of UnsqueezeTransform to create an extra dimension
>>> env = TransformedEnv(
... GymEnv("CartPole-v1", from_pixels=True),
... Compose(
... ToTensorImage(in_keys=["pixels"], out_keys=["pixels_trsf"]),
... Resize(in_keys=["pixels_trsf"], w=64, h=64),
... GrayScale(in_keys=["pixels_trsf"]),
... UnsqueezeTransform(-4, in_keys=["pixels_trsf"]),
... CatFrames(dim=-4, N=4, in_keys=["pixels_trsf"]),
... )
... )
>>> # we design a collector
>>> collector = SyncDataCollector(
... env,
... RandomPolicy(env.action_spec),
... frames_per_batch=10,
... total_frames=1000,
... )
>>> for data in collector:
... print(data)
... break
>>> # now let's create a transform for the replay buffer. We don't need to unsqueeze the data here.
>>> # however, we need to point to both the pixel entry at the root and at the next levels:
>>> t = Compose(
... ToTensorImage(in_keys=["pixels", ("next", "pixels")], out_keys=["pixels_trsf", ("next", "pixels_trsf")]),
... Resize(in_keys=["pixels_trsf", ("next", "pixels_trsf")], w=64, h=64),
... GrayScale(in_keys=["pixels_trsf", ("next", "pixels_trsf")]),
... CatFrames(dim=-4, N=4, in_keys=["pixels_trsf", ("next", "pixels_trsf")]),
... )
>>> from torchrl.data import TensorDictReplayBuffer, LazyMemmapStorage
>>> rb = TensorDictReplayBuffer(storage=LazyMemmapStorage(1000), transform=t, batch_size=16)
>>> data_exclude = data.exclude("pixels_trsf", ("next", "pixels_trsf"))
>>> rb.add(data_exclude)
>>> s = rb.sample(1) # the buffer has only one element
>>> # let's check that our sample is the same as the batch collected during inference
>>> assert (data.exclude("collector")==s.squeeze(0).exclude("index", "collector")).all()
.. note:: :class:`~CatFrames` currently only supports ``"done"``
signal at the root. Nested ``done``,
such as those found in MARL settings, are currently not supported.
If this feature is needed, please raise an issue on TorchRL repo.
.. note:: Storing stacks of frames in the replay buffer can significantly increase memory consumption (by N times).
To mitigate this, you can store trajectories directly in the replay buffer and apply :class:`CatFrames` at sampling time.
This approach involves sampling slices of the stored trajectories and then applying the frame stacking transform.
For convenience, :class:`CatFrames` provides a :meth:`~.make_rb_transform_and_sampler` method that creates:
- A modified version of the transform suitable for use in replay buffers
- A corresponding :class:`SliceSampler` to use with the buffer
"""
inplace = False
_CAT_DIM_ERR = (
"dim must be < 0 to accommodate for tensordict of "
"different batch-sizes (since negative dims are batch invariant)."
)
ACCEPTED_PADDING = {"same", "constant", "zeros"}
def __init__(
self,
N: int,
dim: int,
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
padding="same",
padding_value=0,
as_inverse=False,
reset_key: NestedKey | None = None,
done_key: NestedKey | None = None,
):
if in_keys is None:
in_keys = IMAGE_KEYS
if out_keys is None:
out_keys = copy(in_keys)
super().__init__(in_keys=in_keys, out_keys=out_keys)
self.N = N
if dim >= 0:
raise ValueError(self._CAT_DIM_ERR)
self.dim = dim
if padding not in self.ACCEPTED_PADDING:
raise ValueError(f"padding must be one of {self.ACCEPTED_PADDING}")
if padding == "zeros":
raise RuntimeError("Padding option 'zeros' will is deprecated")
self.padding = padding
self.padding_value = padding_value
for in_key in self.in_keys:
buffer_name = f"_cat_buffers_{in_key}"
self.register_buffer(
buffer_name,
torch.nn.parameter.UninitializedBuffer(
device=torch.device("cpu"), dtype=torch.get_default_dtype()
),
)
# keeps track of calls to _reset since it's only _call that will populate the buffer
self.as_inverse = as_inverse
self.reset_key = reset_key
self.done_key = done_key
[docs] def make_rb_transform_and_sampler(
self, batch_size: int, **sampler_kwargs
) -> Tuple[Transform, "torchrl.data.replay_buffers.SliceSampler"]: # noqa: F821
"""Creates a transform and sampler to be used with a replay buffer when storing frame-stacked data.
This method helps reduce redundancy in stored data by avoiding the need to
store the entire stack of frames in the buffer. Instead, it creates a
transform that stacks frames on-the-fly during sampling, and a sampler that
ensures the correct sequence length is maintained.
Args:
batch_size (int): The batch size to use for the sampler.
**sampler_kwargs: Additional keyword arguments to pass to the
:class:`~torchrl.data.replay_buffers.SliceSampler` constructor.
Returns:
A tuple containing:
- transform (Transform): A transform that stacks frames on-the-fly during sampling.
- sampler (SliceSampler): A sampler that ensures the correct sequence length is maintained.
Example:
>>> env = TransformedEnv(...)
>>> catframes = CatFrames(N=4, ...)
>>> transform, sampler = catframes.make_rb_transform_and_sampler(batch_size=32)
>>> rb = ReplayBuffer(..., sampler=sampler, transform=transform)
.. note:: When working with images, it's recommended to use distinct ``in_keys`` and ``out_keys`` in the preceding
:class:`~torchrl.envs.ToTensorImage` transform. This ensures that the tensors stored in the buffer are separate
from their processed counterparts, which we don't want to store.
For non-image data, consider inserting a :class:`~torchrl.envs.RenameTransform` before :class:`CatFrames` to create
a copy of the data that will be stored in the buffer.
.. note:: When adding the transform to the replay buffer, one should pay attention to also pass the transforms
that precede CatFrames, such as :class:`~torchrl.envs.ToTensorImage` or :class:`~torchrl.envs.UnsqueezeTransform`
in such a way that the :class:`~torchrl.envs.CatFrames` transforms sees data formatted as it was during data
collection.
.. note:: For a more complete example, refer to torchrl's github repo `examples` folder:
https://github.com/pytorch/rl/tree/main/examples/replay-buffers/catframes-in-buffer.py
"""
from torchrl.data.replay_buffers import SliceSampler
in_keys = self.in_keys
in_keys = in_keys + [unravel_key(("next", key)) for key in in_keys]
out_keys = self.out_keys
out_keys = out_keys + [unravel_key(("next", key)) for key in out_keys]
catframes = type(self)(
N=self.N,
in_keys=in_keys,
out_keys=out_keys,
dim=self.dim,
padding=self.padding,
padding_value=self.padding_value,
as_inverse=False,
reset_key=self.reset_key,
done_key=self.done_key,
)
sampler = SliceSampler(slice_len=self.N, **sampler_kwargs)
sampler._batch_size_multiplier = self.N
transform = Compose(
lambda td: td.reshape(-1, self.N),
catframes,
lambda td: td[:, -1],
# We only store "pixels" to the replay buffer to save memory
ExcludeTransform(*out_keys, inverse=True),
)
return transform, sampler
@property
def done_key(self):
done_key = self.__dict__.get("_done_key", None)
if done_key is None:
done_key = "done"
self._done_key = done_key
return done_key
@done_key.setter
def done_key(self, value):
self._done_key = value
@property
def reset_key(self):
reset_key = self.__dict__.get("_reset_key", None)
if reset_key is None:
reset_keys = self.parent.reset_keys
if len(reset_keys) > 1:
raise RuntimeError(
f"Got more than one reset key in env {self.container}, cannot infer which one to use. "
f"Consider providing the reset key in the {type(self)} constructor."
)
reset_key = self._reset_key = reset_keys[0]
return reset_key
@reset_key.setter
def reset_key(self, value):
self._reset_key = value
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
"""Resets _buffers."""
_reset = _get_reset(self.reset_key, tensordict)
if self.as_inverse and self.parent is not None:
raise Exception(
"CatFrames as inverse is not supported as a transform for environments, only for replay buffers."
)
with _set_missing_tolerance(self, True):
tensordict_reset = self._call(tensordict_reset, _reset=_reset)
return tensordict_reset
def _make_missing_buffer(self, data, buffer_name):
shape = list(data.shape)
d = shape[self.dim]
shape[self.dim] = d * self.N
shape = torch.Size(shape)
getattr(self, buffer_name).materialize(shape)
buffer = (
getattr(self, buffer_name)
.to(dtype=data.dtype, device=data.device)
.fill_(self.padding_value)
)
setattr(self, buffer_name, buffer)
return buffer
def _inv_call(self, tensordict: TensorDictBase) -> torch.Tensor:
if self.as_inverse:
return self.unfolding(tensordict)
else:
return tensordict
def _call(self, tensordict: TensorDictBase, _reset=None) -> TensorDictBase:
"""Update the episode tensordict with max pooled keys."""
_just_reset = _reset is not None
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys):
# Lazy init of buffers
buffer_name = f"_cat_buffers_{in_key}"
data = tensordict.get(in_key)
d = data.size(self.dim)
buffer = getattr(self, buffer_name)
if isinstance(buffer, torch.nn.parameter.UninitializedBuffer):
buffer = self._make_missing_buffer(data, buffer_name)
# shift obs 1 position to the right
if _just_reset:
if _reset.all():
_all = True
data_reset = data
buffer_reset = buffer
dim = self.dim
else:
_all = False
data_reset = data[_reset]
buffer_reset = buffer[_reset]
dim = self.dim - _reset.ndim + 1
shape = [1 for _ in buffer_reset.shape]
if _all:
shape[dim] = self.N
else:
shape[dim] = self.N
if self.padding == "same":
if _all:
buffer.copy_(data_reset.repeat(shape).clone())
else:
buffer[_reset] = data_reset.repeat(shape).clone()
elif self.padding == "constant":
if _all:
buffer.fill_(self.padding_value)
else:
buffer[_reset] = self.padding_value
else:
# make linter happy. An exception has already been raised
raise NotImplementedError
if self.dim < 0:
n = buffer_reset.ndimension() + self.dim
else:
raise ValueError(self._CAT_DIM_ERR)
idx = [slice(None, None) for _ in range(n)] + [slice(-d, None)]
if not _all:
buffer_reset = buffer[_reset]
buffer_reset[idx] = data_reset
if not _all:
buffer[_reset] = buffer_reset
else:
buffer.copy_(torch.roll(buffer, shifts=-d, dims=self.dim))
# add new obs
if self.dim < 0:
n = buffer.ndimension() + self.dim
else:
raise ValueError(self._CAT_DIM_ERR)
idx = [slice(None, None) for _ in range(n)] + [slice(-d, None)]
buffer[idx] = buffer[idx].copy_(data)
# add to tensordict
tensordict.set(out_key, buffer.clone())
return tensordict
[docs] @_apply_to_composite
def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
space = observation_spec.space
if isinstance(space, ContinuousBox):
space.low = torch.cat([space.low] * self.N, self.dim)
space.high = torch.cat([space.high] * self.N, self.dim)
observation_spec.shape = space.low.shape
else:
shape = list(observation_spec.shape)
shape[self.dim] = self.N * shape[self.dim]
observation_spec.shape = torch.Size(shape)
return observation_spec
[docs] def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
if self.as_inverse:
return tensordict
else:
return self.unfolding(tensordict)
def _apply_same_padding(self, dim, data, done_mask):
d = data.ndim + dim - 1
res = data.clone()
num_repeats_per_sample = done_mask.sum(dim=-1)
if num_repeats_per_sample.dim() > 2:
extra_dims = num_repeats_per_sample.dim() - 2
num_repeats_per_sample = num_repeats_per_sample.flatten(0, extra_dims)
res_flat_series = res.flatten(0, extra_dims)
else:
extra_dims = 0
res_flat_series = res
if d - 1 > extra_dims:
res_flat_series_flat_batch = res_flat_series.flatten(1, d - 1)
else:
res_flat_series_flat_batch = res_flat_series[:, None]
for sample_idx, num_repeats in enumerate(num_repeats_per_sample):
if num_repeats > 0:
res_slice = res_flat_series_flat_batch[sample_idx]
res_slice[:, :num_repeats] = res_slice[:, num_repeats : num_repeats + 1]
return res
@set_lazy_legacy(False)
def unfolding(self, tensordict: TensorDictBase) -> TensorDictBase:
# it is assumed that the last dimension of the tensordict is the time dimension
if not tensordict.ndim:
raise ValueError(
"CatFrames cannot process unbatched tensordict instances. "
"Make sure your input has more than one dimension and "
"the time dimension is marked as 'time', e.g., "
"`tensordict.refine_names(None, 'time', None)`."
)
i = 0
for i, name in enumerate(tensordict.names): # noqa: B007
if name == "time":
break
else:
warnings.warn(
"The last dimension of the tensordict should be marked as 'time'. "
"CatFrames will unfold the data along the time dimension assuming that "
"the time dimension is the last dimension of the input tensordict. "
"Define a 'time' dimension name (e.g., `tensordict.refine_names(..., 'time')`) to skip this warning. ",
category=UserWarning,
)
tensordict_orig = tensordict
if i != tensordict.ndim - 1:
tensordict = tensordict.transpose(tensordict.ndim - 1, i)
# first sort the in_keys with strings and non-strings
keys = [
(in_key, out_key)
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys)
if isinstance(in_key, str)
]
keys += [
(in_key, out_key)
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys)
if not isinstance(in_key, str)
]
def unfold_done(done, N):
prefix = (slice(None),) * (tensordict.ndim - 1)
reset = torch.cat(
[
torch.zeros_like(done[prefix + (slice(self.N - 1),)]),
torch.ones_like(done[prefix + (slice(1),)]),
done[prefix + (slice(None, -1),)],
],
tensordict.ndim - 1,
)
reset_unfold = reset.unfold(tensordict.ndim - 1, self.N, 1)
reset_unfold_slice = reset_unfold[..., -1]
reset_unfold_list = [torch.zeros_like(reset_unfold_slice)]
for r in reversed(reset_unfold.unbind(-1)):
reset_unfold_list.append(r | reset_unfold_list[-1])
# reset_unfold_slice = reset_unfold_list[-1]
reset_unfold = torch.stack(list(reversed(reset_unfold_list))[1:], -1)
reset = reset[prefix + (slice(self.N - 1, None),)]
reset[prefix + (0,)] = 1
return reset_unfold, reset
done = tensordict.get(("next", self.done_key))
done_mask, reset = unfold_done(done, self.N)
for in_key, out_key in keys:
# check if we have an obs in "next" that has already been processed.
# If so, we must add an offset
data_orig = data = tensordict.get(in_key)
n_feat = data_orig.shape[data.ndim + self.dim]
first_val = None
if isinstance(in_key, tuple) and in_key[0] == "next":
# let's get the out_key we have already processed
prev_out_key = dict(_zip_strict(self.in_keys, self.out_keys)).get(
in_key[1], None
)
if prev_out_key is not None:
prev_val = tensordict.get(prev_out_key)
# n_feat = prev_val.shape[data.ndim + self.dim] // self.N
first_val = prev_val.unflatten(
data.ndim + self.dim, (self.N, n_feat)
)
idx = [slice(None)] * (tensordict.ndim - 1) + [0]
data0 = [
torch.full_like(data[tuple(idx)], self.padding_value).unsqueeze(
tensordict.ndim - 1
)
] * (self.N - 1)
data = torch.cat(data0 + [data], tensordict.ndim - 1)
data = data.unfold(tensordict.ndim - 1, self.N, 1)
# Place -1 dim at self.dim place before squashing
done_mask_expand = done_mask.view(
*done_mask.shape[: tensordict.ndim],
*(1,) * (data.ndim - 1 - tensordict.ndim),
done_mask.shape[-1],
)
done_mask_expand = expand_as_right(done_mask_expand, data)
data = data.permute(
*range(0, data.ndim + self.dim - 1),
-1,
*range(data.ndim + self.dim - 1, data.ndim - 1),
)
done_mask_expand = done_mask_expand.permute(
*range(0, done_mask_expand.ndim + self.dim - 1),
-1,
*range(done_mask_expand.ndim + self.dim - 1, done_mask_expand.ndim - 1),
)
if self.padding != "same":
data = torch.where(done_mask_expand, self.padding_value, data)
else:
data = self._apply_same_padding(self.dim, data, done_mask)
if first_val is not None:
# Aggregate reset along last dim
reset_any = reset.any(-1, False)
rexp = expand_right(
reset_any, (*reset_any.shape, *data.shape[data.ndim + self.dim :])
)
rexp = torch.cat(
[
torch.zeros_like(
data0[0].repeat_interleave(
len(data0), dim=tensordict.ndim - 1
),
dtype=torch.bool,
),
rexp,
],
tensordict.ndim - 1,
)
rexp = rexp.unfold(tensordict.ndim - 1, self.N, 1)
rexp_orig = rexp
rexp = torch.cat([rexp[..., 1:], torch.zeros_like(rexp[..., -1:])], -1)
if self.padding == "same":
rexp_orig = rexp_orig.flip(-1).cumsum(-1).flip(-1).bool()
rexp = rexp.flip(-1).cumsum(-1).flip(-1).bool()
rexp_orig = torch.cat(
[torch.zeros_like(rexp_orig[..., -1:]), rexp_orig[..., 1:]], -1
)
rexp = rexp.permute(
*range(0, rexp.ndim + self.dim - 1),
-1,
*range(rexp.ndim + self.dim - 1, rexp.ndim - 1),
)
rexp_orig = rexp_orig.permute(
*range(0, rexp_orig.ndim + self.dim - 1),
-1,
*range(rexp_orig.ndim + self.dim - 1, rexp_orig.ndim - 1),
)
data[rexp] = first_val[rexp_orig]
data = data.flatten(data.ndim + self.dim - 1, data.ndim + self.dim)
tensordict.set(out_key, data)
if tensordict_orig is not tensordict:
tensordict_orig = tensordict.transpose(tensordict.ndim - 1, i)
return tensordict_orig
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}(N={self.N}, dim"
f"={self.dim}, keys={self.in_keys})"
)
[docs]class RewardScaling(Transform):
"""Affine transform of the reward.
The reward is transformed according to:
.. math::
reward = reward * scale + loc
Args:
loc (number or torch.Tensor): location of the affine transform
scale (number or torch.Tensor): scale of the affine transform
standard_normal (bool, optional): if ``True``, the transform will be
.. math::
reward = (reward-loc)/scale
as it is done for standardization. Default is `False`.
"""
def __init__(
self,
loc: Union[float, torch.Tensor],
scale: Union[float, torch.Tensor],
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
standard_normal: bool = False,
):
if in_keys is None:
in_keys = ["reward"]
if out_keys is None:
out_keys = copy(in_keys)
super().__init__(in_keys=in_keys, out_keys=out_keys)
if not isinstance(standard_normal, torch.Tensor):
standard_normal = torch.tensor(standard_normal)
self.register_buffer("standard_normal", standard_normal)
if not isinstance(loc, torch.Tensor):
loc = torch.tensor(loc)
if not isinstance(scale, torch.Tensor):
scale = torch.tensor(scale)
self.register_buffer("loc", loc)
self.register_buffer("scale", scale.clamp_min(1e-6))
def _apply_transform(self, reward: torch.Tensor) -> torch.Tensor:
if self.standard_normal:
loc = self.loc
scale = self.scale
reward = (reward - loc) / scale
return reward
else:
scale = self.scale
loc = self.loc
reward = reward * scale + loc
return reward
[docs] @_apply_to_composite
def transform_reward_spec(self, reward_spec: TensorSpec) -> TensorSpec:
if isinstance(reward_spec, Unbounded):
return reward_spec
else:
raise NotImplementedError(
f"{self.__class__.__name__}.transform_reward_spec not "
f"implemented for tensor spec of type"
f" {type(reward_spec).__name__}"
)
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}("
f"loc={self.loc.item():4.4f}, scale={self.scale.item():4.4f}, "
f"keys={self.in_keys})"
)
[docs]class FiniteTensorDictCheck(Transform):
"""This transform will check that all the items of the tensordict are finite, and raise an exception if they are not."""
def __init__(self):
super().__init__(in_keys=[])
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
tensordict.apply(check_finite, filter_empty=True)
return tensordict
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
tensordict_reset = self._call(tensordict_reset)
return tensordict_reset
forward = _call
[docs]class DTypeCastTransform(Transform):
"""Casts one dtype to another for selected keys.
Depending on whether the ``in_keys`` or ``in_keys_inv`` are provided
during construction, the class behavior will change:
* If the keys are provided, those entries and those entries only will be
transformed from ``dtype_in`` to ``dtype_out`` entries;
* If the keys are not provided and the object is within an environment
register of transforms, the input and output specs that have a dtype
set to ``dtype_in`` will be used as in_keys_inv / in_keys respectively.
* If the keys are not provided and the object is used without an
environment, the ``forward`` / ``inverse`` pass will scan through the
input tensordict for all ``dtype_in`` values and map them to a ``dtype_out``
tensor. For large data structures, this can impact performance as this
scanning doesn't come for free. The keys to be
transformed will not be cached.
Note that, in this case, the out_keys (resp.
out_keys_inv) cannot be passed as the order on which the keys are processed
cannot be anticipated precisely.
Args:
dtype_in (torch.dtype): the input dtype (from the env).
dtype_out (torch.dtype): the output dtype (for model training).
in_keys (sequence of NestedKey, optional): list of ``dtype_in`` keys to be converted to
``dtype_out`` before being exposed to external objects and functions.
out_keys (sequence of NestedKey, optional): list of destination keys.
Defaults to ``in_keys`` if not provided.
in_keys_inv (sequence of NestedKey, optional): list of ``dtype_out`` keys to be converted to
``dtype_in`` before being passed to the contained base_env or storage.
out_keys_inv (sequence of NestedKey, optional): list of destination keys for inverse
transform.
Defaults to ``in_keys_inv`` if not provided.
Examples:
>>> td = TensorDict(
... {'obs': torch.ones(1, dtype=torch.double),
... 'not_transformed': torch.ones(1, dtype=torch.double),
... }, [])
>>> transform = DTypeCastTransform(torch.double, torch.float, in_keys=["obs"])
>>> _ = transform(td)
>>> print(td.get("obs").dtype)
torch.float32
>>> print(td.get("not_transformed").dtype)
torch.float64
In "automatic" mode, all float64 entries are transformed:
Examples:
>>> td = TensorDict(
... {'obs': torch.ones(1, dtype=torch.double),
... 'not_transformed': torch.ones(1, dtype=torch.double),
... }, [])
>>> transform = DTypeCastTransform(torch.double, torch.float)
>>> _ = transform(td)
>>> print(td.get("obs").dtype)
torch.float32
>>> print(td.get("not_transformed").dtype)
torch.float32
The same behavior is the rule when environments are constructedw without
specifying the transform keys:
Examples:
>>> class MyEnv(EnvBase):
... def __init__(self):
... super().__init__()
... self.observation_spec = Composite(obs=Unbounded((), dtype=torch.float64))
... self.action_spec = Unbounded((), dtype=torch.float64)
... self.reward_spec = Unbounded((1,), dtype=torch.float64)
... self.done_spec = Unbounded((1,), dtype=torch.bool)
... def _reset(self, data=None):
... return TensorDict({"done": torch.zeros((1,), dtype=torch.bool), **self.observation_spec.rand()}, [])
... def _step(self, data):
... assert data["action"].dtype == torch.float64
... reward = self.reward_spec.rand()
... done = torch.zeros((1,), dtype=torch.bool)
... obs = self.observation_spec.rand()
... assert reward.dtype == torch.float64
... assert obs["obs"].dtype == torch.float64
... return obs.empty().set("next", obs.update({"reward": reward, "done": done}))
... def _set_seed(self, seed):
... pass
>>> env = TransformedEnv(MyEnv(), DTypeCastTransform(torch.double, torch.float))
>>> assert env.action_spec.dtype == torch.float32
>>> assert env.observation_spec["obs"].dtype == torch.float32
>>> assert env.reward_spec.dtype == torch.float32, env.reward_spec.dtype
>>> print(env.rollout(2))
TensorDict(
fields={
action: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False),
done: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
obs: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False),
reward: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([2]),
device=cpu,
is_shared=False),
obs: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([2]),
device=cpu,
is_shared=False)
>>> assert env.transform.in_keys == ["obs", "reward"]
>>> assert env.transform.in_keys_inv == ["action"]
"""
invertible = True
def __init__(
self,
dtype_in: torch.dtype,
dtype_out: torch.dtype,
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
in_keys_inv: Sequence[NestedKey] | None = None,
out_keys_inv: Sequence[NestedKey] | None = None,
):
if in_keys is not None and in_keys_inv is None:
in_keys_inv = []
self.dtype_in = dtype_in
self.dtype_out = dtype_out
super().__init__(
in_keys=in_keys,
out_keys=out_keys,
in_keys_inv=in_keys_inv,
out_keys_inv=out_keys_inv,
)
@property
def in_keys(self):
in_keys = self.__dict__.get("_in_keys", None)
if in_keys is None:
parent = self.parent
if parent is None:
# in_keys=None means all entries of dtype_in will be mapped to dtype_out
return None
in_keys = []
for key, spec in parent.observation_spec.items(True, True):
if spec.dtype == self.dtype_in:
in_keys.append(unravel_key(key))
for key, spec in parent.full_reward_spec.items(True, True):
if spec.dtype == self.dtype_in:
in_keys.append(unravel_key(key))
self._in_keys = in_keys
if self.__dict__.get("_out_keys", None) is None:
self.out_keys = copy(in_keys)
return in_keys
@in_keys.setter
def in_keys(self, value):
if value is not None:
if isinstance(value, (str, tuple)):
value = [value]
value = [unravel_key(val) for val in value]
self._in_keys = value
@property
def out_keys(self):
out_keys = self.__dict__.get("_out_keys", None)
if out_keys is None:
out_keys = self._out_keys = copy(self.in_keys)
return out_keys
@out_keys.setter
def out_keys(self, value):
if value is not None:
if isinstance(value, (str, tuple)):
value = [value]
value = [unravel_key(val) for val in value]
self._out_keys = value
@property
def in_keys_inv(self):
in_keys_inv = self.__dict__.get("_in_keys_inv", None)
if in_keys_inv is None:
parent = self.parent
if parent is None:
# in_keys_inv=None means all entries of dtype_out will be mapped to dtype_in
return None
in_keys_inv = []
for key, spec in parent.full_action_spec.items(True, True):
if spec.dtype == self.dtype_in:
in_keys_inv.append(unravel_key(key))
for key, spec in parent.full_state_spec.items(True, True):
if spec.dtype == self.dtype_in:
in_keys_inv.append(unravel_key(key))
self._in_keys_inv = in_keys_inv
if self.__dict__.get("_out_keys_inv", None) is None:
self.out_keys_inv = copy(in_keys_inv)
return in_keys_inv
@in_keys_inv.setter
def in_keys_inv(self, value):
if value is not None:
if isinstance(value, (str, tuple)):
value = [value]
value = [unravel_key(val) for val in value]
self._in_keys_inv = value
@property
def out_keys_inv(self):
out_keys_inv = self.__dict__.get("_out_keys_inv", None)
if out_keys_inv is None:
out_keys_inv = self._out_keys_inv = copy(self.in_keys_inv)
return out_keys_inv
@out_keys_inv.setter
def out_keys_inv(self, value):
if value is not None:
if isinstance(value, (str, tuple)):
value = [value]
value = [unravel_key(val) for val in value]
self._out_keys_inv = value
[docs] @dispatch(source="in_keys", dest="out_keys")
def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
"""Reads the input tensordict, and for the selected keys, applies the transform."""
in_keys = self.in_keys
out_keys = self.out_keys
if in_keys is None:
if out_keys is not None:
raise ValueError(
"in_keys wasn't provided and couldn't be retrieved. However, "
"out_keys was passed to the constructor. Since the order of the "
"entries mapped from dtype_in to dtype_out cannot be guaranteed, "
"this functionality is not covered. Consider passing the in_keys "
"or not passing any out_keys."
)
def func(name, item):
if item.dtype == self.dtype_in:
item = self._apply_transform(item)
tensordict.set(name, item)
tensordict._fast_apply(
func, named=True, nested_keys=True, filter_empty=True
)
return tensordict
else:
# we made sure that if in_keys is not None, out_keys is not None either
for in_key, out_key in _zip_strict(in_keys, out_keys):
item = self._apply_transform(tensordict.get(in_key))
tensordict.set(out_key, item)
return tensordict
def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase:
in_keys_inv = self.in_keys_inv
out_keys_inv = self.out_keys_inv
if in_keys_inv is None:
if out_keys_inv is not None:
raise ValueError(
"in_keys_inv wasn't provided and couldn't be retrieved. However, "
"out_keys_inv was passed to the constructor. Since the order of the "
"entries mapped from dtype_in to dtype_out cannot be guaranteed, "
"this functionality is not covered. Consider passing the in_keys_inv "
"or not passing any out_keys_inv."
)
for in_key_inv, item in list(tensordict.items(True, True)):
if item.dtype == self.dtype_out:
item = self._inv_apply_transform(item)
tensordict.set(in_key_inv, item)
return tensordict
else:
return super()._inv_call(tensordict)
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
with _set_missing_tolerance(self, True):
tensordict_reset = self._call(tensordict_reset)
return tensordict_reset
def _apply_transform(self, obs: torch.Tensor) -> torch.Tensor:
return obs.to(self.dtype_out)
def _inv_apply_transform(self, state: torch.Tensor) -> torch.Tensor:
return state.to(self.dtype_in)
def _transform_spec(self, spec: TensorSpec) -> None:
if isinstance(spec, Composite):
for key in spec:
self._transform_spec(spec[key])
else:
spec = spec.clone()
spec.dtype = self.dtype_out
space = spec.space
if isinstance(space, ContinuousBox):
space.low = space.low.to(self.dtype_out)
space.high = space.high.to(self.dtype_out)
return spec
[docs] def transform_input_spec(self, input_spec: TensorSpec) -> TensorSpec:
full_action_spec = input_spec["full_action_spec"]
full_state_spec = input_spec["full_state_spec"]
# if this method is called, then it must have a parent and in_keys_inv will be defined
if self.in_keys_inv is None:
raise NotImplementedError(
f"Calling transform_input_spec without a parent environment isn't supported yet for {type(self)}."
)
for in_key_inv, out_key_inv in _zip_strict(self.in_keys_inv, self.out_keys_inv):
if in_key_inv in full_action_spec.keys(True):
_spec = full_action_spec[in_key_inv]
target = "action"
elif in_key_inv in full_state_spec.keys(True):
_spec = full_state_spec[in_key_inv]
target = "state"
else:
raise KeyError(
f"Key {in_key_inv} not found in state_spec and action_spec."
)
if _spec.dtype != self.dtype_in:
raise TypeError(
f"input_spec[{in_key_inv}].dtype is not {self.dtype_in}: {in_key_inv.dtype}"
)
_spec = self._transform_spec(_spec)
if target == "action":
full_action_spec[out_key_inv] = _spec
elif target == "state":
full_state_spec[out_key_inv] = _spec
else:
# unreachable
raise RuntimeError
return input_spec
[docs] def transform_output_spec(self, output_spec: Composite) -> Composite:
if self.in_keys is None:
raise NotImplementedError(
f"Calling transform_reward_spec without a parent environment isn't supported yet for {type(self)}."
)
full_reward_spec = output_spec["full_reward_spec"]
full_observation_spec = output_spec["full_observation_spec"]
for reward_key, reward_spec in list(full_reward_spec.items(True, True)):
# find out_key that match the in_key
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys):
if reward_key == in_key:
if reward_spec.dtype != self.dtype_in:
raise TypeError(f"reward_spec.dtype is not {self.dtype_in}")
full_reward_spec[out_key] = self._transform_spec(reward_spec)
output_spec["full_observation_spec"] = self.transform_observation_spec(
full_observation_spec
)
return output_spec
[docs] def transform_observation_spec(self, observation_spec):
full_observation_spec = observation_spec
for observation_key, observation_spec in list(
full_observation_spec.items(True, True)
):
# find out_key that match the in_key
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys):
if observation_key == in_key:
if observation_spec.dtype != self.dtype_in:
raise TypeError(
f"observation_spec.dtype is not {self.dtype_in}"
)
full_observation_spec[out_key] = self._transform_spec(
observation_spec
)
return full_observation_spec
def __repr__(self) -> str:
s = (
f"{self.__class__.__name__}(in_keys={self.in_keys}, out_keys={self.out_keys}, "
f"in_keys_inv={self.in_keys_inv}, out_keys_inv={self.out_keys_inv})"
)
return s
[docs]class DoubleToFloat(DTypeCastTransform):
"""Casts one dtype to another for selected keys.
Depending on whether the ``in_keys`` or ``in_keys_inv`` are provided
during construction, the class behavior will change:
* If the keys are provided, those entries and those entries only will be
transformed from ``float64`` to ``float32`` entries;
* If the keys are not provided and the object is within an environment
register of transforms, the input and output specs that have a dtype
set to ``float64`` will be used as in_keys_inv / in_keys respectively.
* If the keys are not provided and the object is used without an
environment, the ``forward`` / ``inverse`` pass will scan through the
input tensordict for all float64 values and map them to a float32
tensor. For large data structures, this can impact performance as this
scanning doesn't come for free. The keys to be
transformed will not be cached.
Note that, in this case, the out_keys (resp.
out_keys_inv) cannot be passed as the order on which the keys are processed
cannot be anticipated precisely.
Args:
in_keys (sequence of NestedKey, optional): list of double keys to be converted to
float before being exposed to external objects and functions.
out_keys (sequence of NestedKey, optional): list of destination keys.
Defaults to ``in_keys`` if not provided.
in_keys_inv (sequence of NestedKey, optional): list of float keys to be converted to
double before being passed to the contained base_env or storage.
out_keys_inv (sequence of NestedKey, optional): list of destination keys for inverse
transform.
Defaults to ``in_keys_inv`` if not provided.
Examples:
>>> td = TensorDict(
... {'obs': torch.ones(1, dtype=torch.double),
... 'not_transformed': torch.ones(1, dtype=torch.double),
... }, [])
>>> transform = DoubleToFloat(in_keys=["obs"])
>>> _ = transform(td)
>>> print(td.get("obs").dtype)
torch.float32
>>> print(td.get("not_transformed").dtype)
torch.float64
In "automatic" mode, all float64 entries are transformed:
Examples:
>>> td = TensorDict(
... {'obs': torch.ones(1, dtype=torch.double),
... 'not_transformed': torch.ones(1, dtype=torch.double),
... }, [])
>>> transform = DoubleToFloat()
>>> _ = transform(td)
>>> print(td.get("obs").dtype)
torch.float32
>>> print(td.get("not_transformed").dtype)
torch.float32
The same behavior is the rule when environments are constructedw without
specifying the transform keys:
Examples:
>>> class MyEnv(EnvBase):
... def __init__(self):
... super().__init__()
... self.observation_spec = Composite(obs=Unbounded((), dtype=torch.float64))
... self.action_spec = Unbounded((), dtype=torch.float64)
... self.reward_spec = Unbounded((1,), dtype=torch.float64)
... self.done_spec = Unbounded((1,), dtype=torch.bool)
... def _reset(self, data=None):
... return TensorDict({"done": torch.zeros((1,), dtype=torch.bool), **self.observation_spec.rand()}, [])
... def _step(self, data):
... assert data["action"].dtype == torch.float64
... reward = self.reward_spec.rand()
... done = torch.zeros((1,), dtype=torch.bool)
... obs = self.observation_spec.rand()
... assert reward.dtype == torch.float64
... assert obs["obs"].dtype == torch.float64
... return obs.empty().set("next", obs.update({"reward": reward, "done": done}))
... def _set_seed(self, seed):
... pass
>>> env = TransformedEnv(MyEnv(), DoubleToFloat())
>>> assert env.action_spec.dtype == torch.float32
>>> assert env.observation_spec["obs"].dtype == torch.float32
>>> assert env.reward_spec.dtype == torch.float32, env.reward_spec.dtype
>>> print(env.rollout(2))
TensorDict(
fields={
action: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False),
done: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
obs: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False),
reward: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([2]),
device=cpu,
is_shared=False),
obs: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([2]),
device=cpu,
is_shared=False)
>>> assert env.transform.in_keys == ["obs", "reward"]
>>> assert env.transform.in_keys_inv == ["action"]
"""
invertible = True
def __init__(
self,
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
in_keys_inv: Sequence[NestedKey] | None = None,
out_keys_inv: Sequence[NestedKey] | None = None,
):
super().__init__(
dtype_in=torch.double,
dtype_out=torch.float,
in_keys=in_keys,
in_keys_inv=in_keys_inv,
out_keys=out_keys,
out_keys_inv=out_keys_inv,
)
[docs]class DeviceCastTransform(Transform):
"""Moves data from one device to another.
Args:
device (torch.device or equivalent): the destination device.
orig_device (torch.device or equivalent): the origin device. If not specified and
a parent environment exists, it it retrieved from it. In all other cases,
it remains unspecified.
Keyword Args:
in_keys (list of NestedKey): the list of entries to map to a different device.
Defaults to ``None``.
out_keys (list of NestedKey): the output names of the entries mapped onto a device.
Defaults to the values of ``in_keys``.
in_keys_inv (list of NestedKey): the list of entries to map to a different device.
``in_keys_inv`` are the names expected by the base environment.
Defaults to ``None``.
out_keys_inv (list of NestedKey): the output names of the entries mapped onto a device.
``out_keys_inv`` are the names of the keys as seen from outside the transformed env.
Defaults to the values of ``in_keys_inv``.
Examples:
>>> td = TensorDict(
... {'obs': torch.ones(1, dtype=torch.double),
... }, [], device="cpu:0")
>>> transform = DeviceCastTransform(device=torch.device("cpu:2"))
>>> td = transform(td)
>>> print(td.device)
cpu:2
"""
invertible = True
def __init__(
self,
device,
orig_device=None,
*,
in_keys=None,
out_keys=None,
in_keys_inv=None,
out_keys_inv=None,
):
device = self.device = _make_ordinal_device(torch.device(device))
self.orig_device = (
torch.device(orig_device) if orig_device is not None else orig_device
)
if out_keys is None:
out_keys = copy(in_keys)
if out_keys_inv is None:
out_keys_inv = copy(in_keys_inv)
super().__init__(
in_keys=in_keys,
out_keys=out_keys,
in_keys_inv=in_keys_inv,
out_keys_inv=out_keys_inv,
)
self._map_env_device = not self.in_keys and not self.in_keys_inv
self._rename_keys = self.in_keys != self.out_keys
self._rename_keys_inv = self.in_keys_inv != self.out_keys_inv
if device.type != "cuda":
if torch.cuda.is_available():
self._sync_device = torch.cuda.synchronize
elif torch.backends.mps.is_available():
self._sync_device = torch.mps.synchronize
elif device.type == "cpu":
self._sync_device = _do_nothing
else:
self._sync_device = _do_nothing
def set_container(self, container: Union[Transform, EnvBase]) -> None:
if self.orig_device is None:
if isinstance(container, EnvBase):
device = container.device
else:
parent = container.parent
if parent is not None:
device = parent.device
else:
device = torch.device("cpu")
self.orig_device = device
return super().set_container(container)
def _to(self, name, tensor):
if name in self.in_keys:
return tensor.to(self.device, non_blocking=True)
return tensor
def _to_inv(self, name, tensor, device):
if name in self.in_keys_inv:
return tensor.to(device, non_blocking=True)
return tensor
[docs] @dispatch(source="in_keys", dest="out_keys")
def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
if self._map_env_device:
result = tensordict.to(self.device, non_blocking=True)
self._sync_device()
return result
tensordict_t = tensordict.named_apply(self._to, nested_keys=True, device=None)
if self._rename_keys:
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys):
if out_key != in_key:
tensordict_t.rename_key_(in_key, out_key)
tensordict_t.set(in_key, tensordict.get(in_key))
self._sync_device()
return tensordict_t
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
if self._map_env_device:
result = tensordict.to(self.device, non_blocking=True)
self._sync_device()
return result
tensordict_t = tensordict.named_apply(self._to, nested_keys=True, device=None)
if self._rename_keys:
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys):
if out_key != in_key:
tensordict_t.rename_key_(in_key, out_key)
tensordict_t.set(in_key, tensordict.get(in_key))
self._sync_device()
return tensordict_t
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
tensordict_reset = self._call(tensordict_reset)
return tensordict_reset
def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase:
parent = self.parent
device = self.orig_device if parent is None else parent.device
if device is None:
return tensordict
if self._map_env_device:
result = tensordict.to(device, non_blocking=True)
self._sync_orig_device()
return result
tensordict_t = tensordict.named_apply(
functools.partial(self._to_inv, device=device),
nested_keys=True,
device=None,
)
if self._rename_keys_inv:
for in_key, out_key in _zip_strict(self.in_keys_inv, self.out_keys_inv):
if out_key != in_key:
tensordict_t.rename_key_(in_key, out_key)
tensordict_t.set(in_key, tensordict.get(in_key))
self._sync_orig_device()
return tensordict_t
@property
def _sync_orig_device(self):
sync_func = self.__dict__.get("_sync_orig_device_val", None)
if sync_func is None:
parent = self.parent
device = self.orig_device if parent is None else parent.device
if device.type != "cuda":
if torch.cuda.is_available():
self._sync_orig_device_val = torch.cuda.synchronize
elif torch.backends.mps.is_available():
self._sync_orig_device_val = torch.mps.synchronize
elif device.type == "cpu":
self._sync_orig_device_val = _do_nothing
else:
self._sync_orig_device_val = _do_nothing
return self._sync_orig_device
return sync_func
[docs] def transform_input_spec(self, input_spec: Composite) -> Composite:
if self._map_env_device:
return input_spec.to(self.device)
else:
input_spec.clear_device_()
return super().transform_input_spec(input_spec)
def transform_action_spec(self, full_action_spec: Composite) -> Composite:
full_action_spec = full_action_spec.clear_device_()
for in_key, out_key in _zip_strict(self.in_keys_inv, self.out_keys_inv):
local_action_spec = full_action_spec.get(in_key, None)
if local_action_spec is not None:
full_action_spec[out_key] = local_action_spec.to(self.device)
return full_action_spec
def transform_state_spec(self, full_state_spec: Composite) -> Composite:
full_state_spec = full_state_spec.clear_device_()
for in_key, out_key in _zip_strict(self.in_keys_inv, self.out_keys_inv):
local_state_spec = full_state_spec.get(in_key, None)
if local_state_spec is not None:
full_state_spec[out_key] = local_state_spec.to(self.device)
return full_state_spec
[docs] def transform_output_spec(self, output_spec: Composite) -> Composite:
if self._map_env_device:
return output_spec.to(self.device)
else:
output_spec.clear_device_()
return super().transform_output_spec(output_spec)
[docs] def transform_observation_spec(self, observation_spec: Composite) -> Composite:
observation_spec = observation_spec.clear_device_()
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys):
local_obs_spec = observation_spec.get(in_key, None)
if local_obs_spec is not None:
observation_spec[out_key] = local_obs_spec.to(self.device)
return observation_spec
[docs] def transform_done_spec(self, full_done_spec: Composite) -> Composite:
full_done_spec = full_done_spec.clear_device_()
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys):
local_done_spec = full_done_spec.get(in_key, None)
if local_done_spec is not None:
full_done_spec[out_key] = local_done_spec.to(self.device)
return full_done_spec
[docs] def transform_reward_spec(self, full_reward_spec: Composite) -> Composite:
full_reward_spec = full_reward_spec.clear_device_()
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys):
local_reward_spec = full_reward_spec.get(in_key, None)
if local_reward_spec is not None:
full_reward_spec[out_key] = local_reward_spec.to(self.device)
return full_reward_spec
[docs] def transform_env_device(self, device):
if self._map_env_device:
return self.device
# In all other cases the device is not defined
return None
def __repr__(self) -> str:
if self._map_env_device:
return f"{self.__class__.__name__}(device={self.device}, orig_device={self.orig_device})"
device = indent(4 * " ", f"device={self.device}")
orig_device = indent(4 * " ", f"orig_device={self.orig_device}")
in_keys = indent(4 * " ", f"in_keys={self.in_keys}")
out_keys = indent(4 * " ", f"out_keys={self.out_keys}")
in_keys_inv = indent(4 * " ", f"in_keys_inv={self.in_keys_inv}")
out_keys_inv = indent(4 * " ", f"out_keys_inv={self.out_keys_inv}")
return f"{self.__class__.__name__}(\n{device},\n{orig_device},\n{in_keys},\n{out_keys},\n{in_keys_inv},\n{out_keys_inv})"
[docs]class CatTensors(Transform):
"""Concatenates several keys in a single tensor.
This is especially useful if multiple keys describe a single state (e.g.
"observation_position" and
"observation_velocity")
Args:
in_keys (sequence of NestedKey): keys to be concatenated. If `None` (or not provided)
the keys will be retrieved from the parent environment the first time
the transform is used. This behavior will only work if a parent is set.
out_key (NestedKey): key of the resulting tensor.
dim (int, optional): dimension along which the concatenation will occur.
Default is ``-1``.
Keyword Args:
del_keys (bool, optional): if ``True``, the input values will be deleted after
concatenation. Default is ``True``.
unsqueeze_if_oor (bool, optional): if ``True``, CatTensor will check that
the dimension indicated exist for the tensors to concatenate. If not,
the tensors will be unsqueezed along that dimension.
Default is ``False``.
sort (bool, optional): if ``True``, the keys will be sorted in the
transform. Otherwise, the order provided by the user will prevail.
Defaults to ``True``.
Examples:
>>> transform = CatTensors(in_keys=["key1", "key2"])
>>> td = TensorDict({"key1": torch.zeros(1, 1),
... "key2": torch.ones(1, 1)}, [1])
>>> _ = transform(td)
>>> print(td.get("observation_vector"))
tensor([[0., 1.]])
>>> transform = CatTensors(in_keys=["key1", "key2"], dim=-2, unsqueeze_if_oor=True)
>>> td = TensorDict({"key1": torch.zeros(1),
... "key2": torch.ones(1)}, [])
>>> _ = transform(td)
>>> print(td.get("observation_vector").shape)
torch.Size([2, 1])
"""
invertible = False
def __init__(
self,
in_keys: Sequence[NestedKey] | None = None,
out_key: NestedKey = "observation_vector",
dim: int = -1,
*,
del_keys: bool = True,
unsqueeze_if_oor: bool = False,
sort: bool = True,
):
self._initialized = in_keys is not None
if not self._initialized:
if dim != -1:
raise ValueError(
"Lazy call to CatTensors is only supported when `dim=-1`."
)
elif sort:
in_keys = sorted(in_keys, key=_sort_keys)
if not isinstance(out_key, (str, tuple)):
raise Exception("CatTensors requires out_key to be of type NestedKey")
super(CatTensors, self).__init__(in_keys=in_keys, out_keys=[out_key])
self.dim = dim
self._del_keys = del_keys
self._keys_to_exclude = None
self.unsqueeze_if_oor = unsqueeze_if_oor
@property
def keys_to_exclude(self):
if self._keys_to_exclude is None:
self._keys_to_exclude = [
key for key in self.in_keys if key != self.out_keys[0]
]
return self._keys_to_exclude
def _find_in_keys(self):
"""Gathers all the entries from observation spec which shape is 1d."""
parent = self.parent
obs_spec = parent.observation_spec
in_keys = []
for key, value in obs_spec.items(True, True):
if len(value.shape) == 1:
in_keys.append(key)
return sorted(in_keys, key=_sort_keys)
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
if not self._initialized:
self.in_keys = self._find_in_keys()
self._initialized = True
values = [tensordict.get(key, None) for key in self.in_keys]
if any(value is None for value in values):
raise Exception(
f"CatTensor failed, as it expected input keys ="
f" {sorted(self.in_keys, key=_sort_keys)} but got a TensorDict with keys"
f" {sorted(tensordict.keys(include_nested=True), key=_sort_keys)}"
)
if self.unsqueeze_if_oor:
pos_idx = self.dim > 0
abs_idx = self.dim if pos_idx else -self.dim - 1
values = [
v
if abs_idx < v.ndimension()
else v.unsqueeze(0)
if not pos_idx
else v.unsqueeze(-1)
for v in values
]
out_tensor = torch.cat(values, dim=self.dim)
tensordict.set(self.out_keys[0], out_tensor)
if self._del_keys:
tensordict.exclude(*self.keys_to_exclude, inplace=True)
return tensordict
forward = _call
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
with _set_missing_tolerance(self, True):
tensordict_reset = self._call(tensordict_reset)
return tensordict_reset
[docs] def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
if not self._initialized:
self.in_keys = self._find_in_keys()
self._initialized = True
# check that all keys are in observation_spec
if len(self.in_keys) > 1 and not isinstance(observation_spec, Composite):
raise ValueError(
"CatTensor cannot infer the output observation spec as there are multiple input keys but "
"only one observation_spec."
)
if isinstance(observation_spec, Composite) and len(
[key for key in self.in_keys if key not in observation_spec.keys(True)]
):
raise ValueError(
"CatTensor got a list of keys that does not match the keys in observation_spec. "
"Make sure the environment has an observation_spec attribute that includes all the specs needed for CatTensor."
)
if not isinstance(observation_spec, Composite):
# by def, there must be only one key
return observation_spec
keys = [key for key in observation_spec.keys(True, True) if key in self.in_keys]
sum_shape = sum(
[
observation_spec[key].shape[self.dim]
if observation_spec[key].shape
else 1
for key in keys
]
)
spec0 = observation_spec[keys[0]]
out_key = self.out_keys[0]
shape = list(spec0.shape)
device = spec0.device
shape[self.dim] = sum_shape
shape = torch.Size(shape)
observation_spec[out_key] = Unbounded(
shape=shape,
dtype=spec0.dtype,
device=device,
)
if self._del_keys:
for key in self.keys_to_exclude:
if key in observation_spec.keys(True):
del observation_spec[key]
return observation_spec
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}(in_keys={self.in_keys}, out_key"
f"={self.out_keys[0]})"
)
class Stack(Transform):
"""Stacks tensors and tensordicts.
Concatenates a sequence of tensors or tensordicts along a new dimension.
The tensordicts or tensors under ``in_keys`` must all have the same shapes.
This transform only stacks the inputs into one output key. Stacking multiple
groups of input keys into different output keys requires multiple
transforms.
This transform can be useful for environments that have multiple agents with
identical specs under different keys. The specs and tensordicts for the
agents can be stacked together under a shared key, in order to run MARL
algorithms that expect the tensors for observations, rewards, etc. to
contain batched data for all the agents.
Args:
in_keys (sequence of NestedKey): keys to be stacked.
out_key (NestedKey): key of the resulting stacked entry.
in_key_inv (NestedKey, optional): key to unstack during :meth:`~.inv`
calls. Default is ``None``.
out_keys_inv (sequence of NestedKey, optional): keys of the resulting
unstacked entries after :meth:`~.inv` calls. Default is ``None``.
dim (int, optional): dimension to insert. Default is ``-1``.
allow_positive_dim (bool, optional): if ``True``, positive dimensions
are accepted. Defaults to ``False``, ie. non-negative dimensions are
not permitted.
Keyword Args:
del_keys (bool, optional): if ``True``, the input values will be deleted
after stacking. Default is ``True``.
Examples:
>>> import torch
>>> from tensordict import TensorDict
>>> from torchrl.envs import Stack
>>> td = TensorDict({"key1": torch.zeros(3), "key2": torch.ones(3)}, [])
>>> td
TensorDict(
fields={
key1: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False),
key2: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([]),
device=None,
is_shared=False)
>>> transform = Stack(in_keys=["key1", "key2"], out_key="out", dim=-2)
>>> transform(td)
TensorDict(
fields={
out: Tensor(shape=torch.Size([2, 3]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([]),
device=None,
is_shared=False)
>>> td["out"]
tensor([[0., 0., 0.],
[1., 1., 1.]])
>>> agent_0 = TensorDict({"obs": torch.rand(4, 5), "reward": torch.zeros(1)})
>>> agent_1 = TensorDict({"obs": torch.rand(4, 5), "reward": torch.zeros(1)})
>>> td = TensorDict({"agent_0": agent_0, "agent_1": agent_1})
>>> transform = Stack(in_keys=["agent_0", "agent_1"], out_key="agents")
>>> transform(td)
TensorDict(
fields={
agents: TensorDict(
fields={
obs: Tensor(shape=torch.Size([2, 4, 5]), device=cpu, dtype=torch.float32, is_shared=False),
reward: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([2]),
device=None,
is_shared=False)},
batch_size=torch.Size([]),
device=None,
is_shared=False)
"""
invertible = True
def __init__(
self,
in_keys: Sequence[NestedKey],
out_key: NestedKey,
in_key_inv: NestedKey | None = None,
out_keys_inv: Sequence[NestedKey] | None = None,
dim: int = -1,
allow_positive_dim: bool = False,
*,
del_keys: bool = True,
):
if not allow_positive_dim and dim >= 0:
raise ValueError(
"dim should be negative to accommodate for envs of different "
"batch_sizes. If you need dim to be positive, set "
"allow_positive_dim=True."
)
if in_key_inv is None and out_keys_inv is not None:
raise ValueError("out_keys_inv was specified, but in_key_inv was not")
elif in_key_inv is not None and out_keys_inv is None:
raise ValueError("in_key_inv was specified, but out_keys_inv was not")
super(Stack, self).__init__(
in_keys=in_keys,
out_keys=[out_key],
in_keys_inv=None if in_key_inv is None else [in_key_inv],
out_keys_inv=out_keys_inv,
)
for in_key in self.in_keys:
if len(in_key) == len(self.out_keys[0]):
if all(k1 == k2 for k1, k2 in zip(in_key, self.out_keys[0])):
raise ValueError(f"{self}: out_key cannot be in in_keys")
parent_keys = []
for key in self.in_keys:
if isinstance(key, (list, tuple)):
for parent_level in range(1, len(key)):
parent_key = tuple(key[:-parent_level])
if parent_key not in parent_keys:
parent_keys.append(parent_key)
self._maybe_del_parent_keys = sorted(parent_keys, key=len, reverse=True)
self.dim = dim
self._del_keys = del_keys
self._keys_to_exclude = None
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
values = []
for in_key in self.in_keys:
value = tensordict.get(in_key, default=None)
if value is not None:
values.append(value)
elif not self.missing_tolerance:
raise KeyError(
f"{self}: '{in_key}' not found in tensordict {tensordict}"
)
out_tensor = torch.stack(values, dim=self.dim)
tensordict.set(self.out_keys[0], out_tensor)
if self._del_keys:
tensordict.exclude(*self.in_keys, inplace=True)
for parent_key in self._maybe_del_parent_keys:
if len(tensordict[parent_key].keys()) == 0:
tensordict.exclude(parent_key, inplace=True)
return tensordict
forward = _call
def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase:
if len(self.in_keys_inv) == 0:
return tensordict
if self.in_keys_inv[0] not in tensordict.keys(include_nested=True):
return tensordict
values = torch.unbind(tensordict[self.in_keys_inv[0]], dim=self.dim)
for value, out_key_inv in _zip_strict(values, self.out_keys_inv):
tensordict = tensordict.set(out_key_inv, value)
return tensordict.exclude(self.in_keys_inv[0])
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
with _set_missing_tolerance(self, True):
tensordict_reset = self._call(tensordict_reset)
return tensordict_reset
def _transform_spec(self, spec: TensorSpec) -> TensorSpec:
if not isinstance(spec, Composite):
raise TypeError(f"{self}: Only specs of type Composite can be transformed")
spec_keys = spec.keys(include_nested=True)
keys_to_stack = [key for key in spec_keys if key in self.in_keys]
specs_to_stack = [spec[key] for key in keys_to_stack]
if len(specs_to_stack) == 0:
return spec
stacked_specs = torch.stack(specs_to_stack, dim=self.dim)
spec.set(self.out_keys[0], stacked_specs)
if self._del_keys:
for key in keys_to_stack:
del spec[key]
for parent_key in self._maybe_del_parent_keys:
if len(spec[parent_key]) == 0:
del spec[parent_key]
return spec
def transform_input_spec(self, input_spec: TensorSpec) -> TensorSpec:
self._transform_spec(input_spec["full_state_spec"])
self._transform_spec(input_spec["full_action_spec"])
return input_spec
def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
return self._transform_spec(observation_spec)
def transform_reward_spec(self, reward_spec: TensorSpec) -> TensorSpec:
return self._transform_spec(reward_spec)
def transform_done_spec(self, done_spec: TensorSpec) -> TensorSpec:
return self._transform_spec(done_spec)
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}("
f"in_keys={self.in_keys}, "
f"out_key={self.out_keys[0]}, "
f"dim={self.dim}"
")"
)
[docs]class DiscreteActionProjection(Transform):
"""Projects discrete actions from a high dimensional space to a low dimensional space.
Given a discrete action (from 1 to N) encoded as a one-hot vector and a
maximum action index num_actions (with num_actions < N), transforms the action such that
action_out is at most num_actions.
If the input action is > num_actions, it is being replaced by a random value
between 0 and num_actions-1. Otherwise the same action is kept.
This is intended to be used with policies applied over multiple discrete
control environments with different action space.
A call to DiscreteActionProjection.forward (eg from a replay buffer or in a
sequence of nn.Modules) will call the transform num_actions_effective -> max_actions
on the :obj:`"in_keys"`, whereas a call to _call will be ignored. Indeed,
transformed envs are instructed to update the input keys only for the inner
base_env, but the original input keys will remain unchanged.
Args:
num_actions_effective (int): max number of action considered.
max_actions (int): maximum number of actions that this module can read.
action_key (NestedKey, optional): key name of the action. Defaults to "action".
include_forward (bool, optional): if ``True``, a call to forward will also
map the action from one domain to the other when the module is called
by a replay buffer or an nn.Module chain. Defaults to True.
Examples:
>>> torch.manual_seed(0)
>>> N = 3
>>> M = 2
>>> action = torch.zeros(N, dtype=torch.long)
>>> action[-1] = 1
>>> td = TensorDict({"action": action}, [])
>>> transform = DiscreteActionProjection(num_actions_effective=M, max_actions=N)
>>> _ = transform.inv(td)
>>> print(td.get("action"))
tensor([1])
"""
def __init__(
self,
num_actions_effective: int,
max_actions: int,
action_key: NestedKey = "action",
include_forward: bool = True,
):
in_keys_inv = [action_key]
if include_forward:
in_keys = in_keys_inv
else:
in_keys = []
if in_keys_inv is None:
in_keys_inv = []
super().__init__(
in_keys=in_keys,
out_keys=copy(in_keys),
in_keys_inv=in_keys_inv,
out_keys_inv=copy(in_keys_inv),
)
self.num_actions_effective = num_actions_effective
self.max_actions = max_actions
if max_actions < num_actions_effective:
raise RuntimeError(
"The `max_actions` int must be greater or equal to `num_actions_effective`."
)
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
# We don't do anything here because the action is modified by the inv
# method but we don't need to map it back as it won't be updated in the original
# tensordict
return tensordict
def _apply_transform(self, action: torch.Tensor) -> None:
# We still need to code the forward transform for replay buffers and models
action = action.argmax(-1) # bool to int
action = nn.functional.one_hot(action, self.max_actions)
return action
def _inv_apply_transform(self, action: torch.Tensor) -> torch.Tensor:
if action.shape[-1] != self.max_actions:
raise RuntimeError(
f"action.shape[-1]={action.shape[-1]} must match self.max_actions={self.max_actions}."
)
action = action.long().argmax(-1) # bool to int
idx = action >= self.num_actions_effective
if idx.any():
action[idx] = torch.randint(self.num_actions_effective, (idx.sum(),))
action = nn.functional.one_hot(action, self.num_actions_effective)
return action
[docs] def transform_input_spec(self, input_spec: Composite):
input_spec = input_spec.clone()
for key in input_spec["full_action_spec"].keys(True, True):
key = ("full_action_spec", key)
break
else:
raise KeyError("key not found in action_spec.")
input_spec[key] = OneHot(
self.max_actions,
shape=(*input_spec[key].shape[:-1], self.max_actions),
device=input_spec.device,
dtype=input_spec[key].dtype,
)
return input_spec
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}(num_actions_effective={self.num_actions_effective}, max_actions={self.max_actions}, "
f"in_keys_inv={self.in_keys_inv})"
)
[docs]class FrameSkipTransform(Transform):
"""A frame-skip transform.
This transform applies the same action repeatedly in the parent environment,
which improves stability on certain training sota-implementations.
Args:
frame_skip (int, optional): a positive integer representing the number
of frames during which the same action must be applied.
"""
def __init__(self, frame_skip: int = 1):
super().__init__()
if frame_skip < 1:
raise ValueError("frame_skip should have a value greater or equal to one.")
self.frame_skip = frame_skip
def _step(
self, tensordict: TensorDictBase, next_tensordict: TensorDictBase
) -> TensorDictBase:
parent = self.parent
if parent is None:
raise RuntimeError("parent not found for FrameSkipTransform")
reward_key = parent.reward_key
reward = next_tensordict.get(reward_key)
for _ in range(self.frame_skip - 1):
next_tensordict = parent._step(tensordict)
reward = reward + next_tensordict.get(reward_key)
return next_tensordict.set(reward_key, reward)
[docs] def forward(self, tensordict):
raise RuntimeError(
"FrameSkipTransform can only be used when appended to a transformed env."
)
[docs]class NoopResetEnv(Transform):
"""Runs a series of random actions when an environment is reset.
Args:
env (EnvBase): env on which the random actions have to be
performed. Can be the same env as the one provided to the
TransformedEnv class
noops (int, optional): upper-bound on the number of actions
performed after reset. Default is `30`.
If noops is too high such that it results in the env being
done or truncated before the all the noops are applied,
in multiple trials, the transform raises a RuntimeError.
random (bool, optional): if False, the number of random ops will
always be equal to the noops value. If True, the number of
random actions will be randomly selected between 0 and noops.
Default is `True`.
"""
def __init__(self, noops: int = 30, random: bool = True):
"""Sample initial states by taking random number of no-ops on reset."""
super().__init__()
self.noops = noops
self.random = random
@property
def base_env(self):
return self.parent
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
"""Do no-op action for a number of steps in [1, noop_max]."""
parent = self.parent
if parent is None:
raise RuntimeError(
"NoopResetEnv.parent not found. Make sure that the parent is set."
)
# Merge the two tensordicts
tensordict = parent._reset_proc_data(tensordict.clone(False), tensordict_reset)
# check that there is a single done state -- behavior is undefined for multiple dones
done_keys = parent.done_keys
reward_key = parent.reward_key
if parent.batch_size.numel() > 1:
raise ValueError(
"The parent environment batch-size is non-null. "
"NoopResetEnv is designed to work on single env instances, as partial reset "
"is currently not supported. If you feel like this is a missing feature, submit "
"an issue on TorchRL github repo. "
"In case you are trying to use NoopResetEnv over a batch of environments, know "
"that you can have a transformed batch of transformed envs, such as: "
"`TransformedEnv(ParallelEnv(3, lambda: TransformedEnv(MyEnv(), NoopResetEnv(3))), OtherTransform())`."
)
noops = (
self.noops if not self.random else torch.randint(self.noops, (1,)).item()
)
trial = 0
while trial <= _MAX_NOOPS_TRIALS:
i = 0
while i < noops:
i += 1
tensordict = parent.rand_step(tensordict)
reset = False
# if any of the done_keys is True, we break
for done_key in done_keys:
done = tensordict.get(("next", done_key))
if done.numel() > 1:
raise ValueError(
f"{type(self)} only supports scalar done states."
)
if done:
reset = True
break
tensordict = step_mdp(tensordict, exclude_done=False)
if reset:
tensordict = parent.reset(tensordict.clone(False))
break
else:
break
trial += 1
else:
raise RuntimeError(
f"Parent env was repeatedly done or truncated"
f" before the sampled number of noops (={noops}) could be applied. "
)
tensordict_reset = tensordict
return tensordict_reset.exclude(reward_key, inplace=True)
def __repr__(self) -> str:
random = self.random
noops = self.noops
class_name = self.__class__.__name__
return f"{class_name}(noops={noops}, random={random})"
[docs]class TensorDictPrimer(Transform):
"""A primer for TensorDict initialization at reset time.
This transform will populate the tensordict at reset with values drawn from
the relative tensorspecs provided at initialization.
If the transform is used out of the env context (e.g. as an nn.Module or
appended to a replay buffer), a call to `forward` will also populate the
tensordict with the desired features.
Args:
primers (dict or Composite, optional): a dictionary containing
key-spec pairs which will be used to populate the input tensordict.
:class:`~torchrl.data.Composite` instances are supported too.
random (bool, optional): if ``True``, the values will be drawn randomly from
the TensorSpec domain (or a unit Gaussian if unbounded). Otherwise a fixed value will be assumed.
Defaults to `False`.
default_value (float, Callable, Dict[NestedKey, float], Dict[NestedKey, Callable], optional): If non-random
filling is chosen, `default_value` will be used to populate the tensors. If `default_value` is a float,
all elements of the tensors will be set to that value. If it is a callable, this callable is expected to
return a tensor fitting the specs, and it will be used to generate the tensors. Finally, if `default_value`
is a dictionary of tensors or a dictionary of callables with keys matching those of the specs, these will
be used to generate the corresponding tensors. Defaults to `0.0`.
reset_key (NestedKey, optional): the reset key to be used as partial
reset indicator. Must be unique. If not provided, defaults to the
only reset key of the parent environment (if it has only one)
and raises an exception otherwise.
**kwargs: each keyword argument corresponds to a key in the tensordict.
The corresponding value has to be a TensorSpec instance indicating
what the value must be.
When used in a `TransformedEnv`, the spec shapes must match the environment's shape if
the parent environment is batch-locked (`env.batch_locked=True`). If the spec shapes and
parent shapes do not match, the spec shapes are modified in-place to match the leading
dimensions of the parent's batch size. This adjustment is made for cases where the parent
batch size dimension is not known during instantiation.
Examples:
>>> from torchrl.envs.libs.gym import GymEnv
>>> from torchrl.envs import SerialEnv
>>> base_env = SerialEnv(2, lambda: GymEnv("Pendulum-v1"))
>>> env = TransformedEnv(base_env)
>>> # the env is batch-locked, so the leading dims of the spec must match those of the env
>>> env.append_transform(TensorDictPrimer(mykey=Unbounded([2, 3])))
>>> td = env.reset()
>>> print(td)
TensorDict(
fields={
done: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
mykey: Tensor(shape=torch.Size([2, 3]), device=cpu, dtype=torch.float32, is_shared=False),
observation: Tensor(shape=torch.Size([2, 3]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([2]),
device=cpu,
is_shared=False)
>>> # the entry is populated with 0s
>>> print(td.get("mykey"))
tensor([[0., 0., 0.],
[0., 0., 0.]])
When calling ``env.step()``, the current value of the key will be carried
in the ``"next"`` tensordict __unless it already exists__.
Examples:
>>> td = env.rand_step(td)
>>> print(td.get(("next", "mykey")))
tensor([[0., 0., 0.],
[0., 0., 0.]])
>>> # with another value for "mykey", the previous value is not carried on
>>> td = env.reset()
>>> td = td.set(("next", "mykey"), torch.ones(2, 3))
>>> td = env.rand_step(td)
>>> print(td.get(("next", "mykey")))
tensor([[1., 1., 1.],
[1., 1., 1.]])
Examples:
>>> from torchrl.envs.libs.gym import GymEnv
>>> from torchrl.envs import SerialEnv, TransformedEnv
>>> from torchrl.modules.utils import get_primers_from_module
>>> from torchrl.modules import GRUModule
>>> base_env = SerialEnv(2, lambda: GymEnv("Pendulum-v1"))
>>> env = TransformedEnv(base_env)
>>> model = GRUModule(input_size=2, hidden_size=2, in_key="observation", out_key="action")
>>> primers = get_primers_from_module(model)
>>> print(primers) # Primers shape is independent of the env batch size
TensorDictPrimer(primers=Composite(
recurrent_state: UnboundedContinuous(
shape=torch.Size([1, 2]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([1, 2]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([1, 2]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous),
device=None,
shape=torch.Size([])), default_value={'recurrent_state': 0.0}, random=None)
>>> env.append_transform(primers)
>>> print(env.reset()) # The primers are automatically expanded to match the env batch size
TensorDict(
fields={
done: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
observation: Tensor(shape=torch.Size([2, 3]), device=cpu, dtype=torch.float32, is_shared=False),
recurrent_state: Tensor(shape=torch.Size([2, 1, 2]), device=cpu, dtype=torch.float32, is_shared=False),
terminated: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
truncated: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
batch_size=torch.Size([2]),
device=None,
is_shared=False)
.. note:: Some TorchRL modules rely on specific keys being present in the environment TensorDicts,
like :class:`~torchrl.modules.models.LSTM` or :class:`~torchrl.modules.models.GRU`.
To facilitate this process, the method :func:`~torchrl.modules.utils.get_primers_from_module`
automatically checks for required primer transforms in a module and its submodules and
generates them.
"""
def __init__(
self,
primers: dict | Composite = None,
random: bool | None = None,
default_value: float
| Callable
| Dict[NestedKey, float]
| Dict[NestedKey, Callable] = None,
reset_key: NestedKey | None = None,
expand_specs: bool = None,
**kwargs,
):
self.device = kwargs.pop("device", None)
if primers is not None:
if kwargs:
raise RuntimeError(
"providing the primers as a dictionary is incompatible with extra keys provided "
"as kwargs."
)
kwargs = primers
if not isinstance(kwargs, Composite):
shape = kwargs.pop("shape", None)
device = kwargs.pop("device", None)
if "batch_size" in kwargs.keys():
extra_kwargs = {"batch_size": kwargs.pop("batch_size")}
else:
extra_kwargs = {}
primers = Composite(kwargs, device=device, shape=shape, **extra_kwargs)
self.primers = primers
self.expand_specs = expand_specs
if random and default_value:
raise ValueError(
"Setting random to True and providing a default_value are incompatible."
)
default_value = (
default_value or 0.0
) # if not random and no default value, use 0.0
self.random = random
if isinstance(default_value, dict):
default_value = TensorDict(default_value, [])
default_value_keys = default_value.keys(
True,
True,
is_leaf=lambda x: issubclass(x, (NonTensorData, torch.Tensor)),
)
if set(default_value_keys) != set(self.primers.keys(True, True)):
raise ValueError(
"If a default_value dictionary is provided, it must match the primers keys."
)
else:
default_value = {
key: default_value for key in self.primers.keys(True, True)
}
self.default_value = default_value
self._validated = False
self.reset_key = reset_key
# sanity check
for spec in self.primers.values(True, True):
if not isinstance(spec, TensorSpec):
raise ValueError(
"The values of the primers must be a subtype of the TensorSpec class. "
f"Got {type(spec)} instead."
)
super().__init__()
@property
def reset_key(self):
reset_key = self.__dict__.get("_reset_key", None)
if reset_key is None:
if self.parent is None:
raise RuntimeError(
"Missing parent, cannot infer reset_key automatically."
)
reset_keys = self.parent.reset_keys
if len(reset_keys) > 1:
raise RuntimeError(
f"Got more than one reset key in env {self.container}, cannot infer which one to use. "
f"Consider providing the reset key in the {type(self)} constructor."
)
reset_key = self._reset_key = reset_keys[0]
return reset_key
@reset_key.setter
def reset_key(self, value):
self._reset_key = value
@property
def device(self):
device = self._device
if device is None and self.parent is not None:
device = self.parent.device
self._device = device
return device
@device.setter
def device(self, value):
if value is None:
self._device = None
return
self._device = torch.device(value)
[docs] def to(self, *args, **kwargs):
device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(
*args, **kwargs
)
if device is not None:
self.device = device
self.empty_cache()
self.primers = self.primers.to(device)
return super().to(*args, **kwargs)
def _expand_shape(self, spec):
return spec.expand((*self.parent.batch_size, *spec.shape))
[docs] def transform_observation_spec(self, observation_spec: Composite) -> Composite:
if not isinstance(observation_spec, Composite):
raise ValueError(
f"observation_spec was expected to be of type Composite. Got {type(observation_spec)} instead."
)
if self.primers.shape != observation_spec.shape:
if self.expand_specs:
self.primers = self._expand_shape(self.primers)
elif self.expand_specs is None:
warnings.warn(
f"expand_specs wasn't specified in the {type(self).__name__} constructor. "
f"The current behaviour is that the transform will attempt to set the shape of the composite "
f"spec, and if this can't be done it will be expanded. "
f"From v0.8, a mismatched shape between the spec of the transform and the env's batch_size "
f"will raise an exception.",
category=FutureWarning,
)
try:
# We try to set the primer shape to the observation spec shape
self.primers.shape = observation_spec.shape
except ValueError:
# If we fail, we expand them to that shape
self.primers = self._expand_shape(self.primers)
else:
self.primers.shape = observation_spec.shape
device = observation_spec.device
observation_spec.update(self.primers.clone().to(device))
return observation_spec
[docs] def transform_input_spec(self, input_spec: TensorSpec) -> TensorSpec:
new_state_spec = self.transform_observation_spec(input_spec["full_state_spec"])
for action_key in list(input_spec["full_action_spec"].keys(True, True)):
if action_key in new_state_spec.keys(True, True):
input_spec["full_action_spec", action_key] = new_state_spec[action_key]
del new_state_spec[action_key]
input_spec["full_state_spec"] = new_state_spec
return input_spec
@property
def _batch_size(self):
return self.parent.batch_size
def _validate_value_tensor(self, value, spec):
if not spec.is_in(value):
raise RuntimeError(f"Value ({value}) is not in the spec domain ({spec}).")
return True
[docs] def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
for key, spec in self.primers.items(True, True):
if spec.shape[: len(tensordict.shape)] != tensordict.shape:
raise RuntimeError(
"The leading shape of the spec must match the tensordict's, "
"but it does not: got "
f"tensordict.shape={tensordict.shape} whereas {key} spec's shape is "
f"{spec.shape}."
)
if self.random:
value = spec.rand()
else:
value = self.default_value[key]
if callable(value):
value = value()
if not self._validated:
self._validate_value_tensor(value, spec)
else:
value = torch.full(
spec.shape,
value,
device=spec.device,
)
tensordict.set(key, value)
if not self._validated:
self._validated = True
return tensordict
def _step(
self, tensordict: TensorDictBase, next_tensordict: TensorDictBase
) -> TensorDictBase:
for key in self.primers.keys(True, True):
if key not in next_tensordict.keys(True, True):
prev_val = tensordict.get(key)
next_tensordict.set(key, prev_val)
return next_tensordict
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
"""Sets the default values in the input tensordict.
If the parent is batch-locked, we make sure the specs have the appropriate leading
shape. We allow for execution when the parent is missing, in which case the
spec shape is assumed to match the tensordict's.
"""
_reset = _get_reset(self.reset_key, tensordict)
if (
self.parent
and self.parent.batch_locked
and self.primers.shape[: len(self.parent.shape)] != self.parent.batch_size
):
self.primers = self._expand_shape(self.primers)
if _reset.any():
for key, spec in self.primers.items(True, True):
if self.random:
shape = (
()
if (not self.parent or self.parent.batch_locked)
else tensordict.batch_size
)
value = spec.rand(shape)
else:
value = self.default_value[key]
if callable(value):
value = value()
if not self._validated:
self._validate_value_tensor(value, spec)
else:
value = torch.full(
spec.shape,
value,
device=spec.device,
)
prev_val = tensordict.get(key, 0.0)
value = torch.where(
expand_as_right(_reset, value), value, prev_val
)
tensordict_reset.set(key, value)
self._validated = True
return tensordict_reset
def __repr__(self) -> str:
class_name = self.__class__.__name__
default_value = {
key: value if isinstance(value, float) else "Callable"
for key, value in self.default_value.items()
}
return f"{class_name}(primers={self.primers}, default_value={default_value}, random={self.random})"
[docs]class PinMemoryTransform(Transform):
"""Calls pin_memory on the tensordict to facilitate writing on CUDA devices."""
def __init__(self):
super().__init__()
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
return tensordict.pin_memory()
forward = _call
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
with _set_missing_tolerance(self, True):
tensordict_reset = self._call(tensordict_reset)
return tensordict_reset
def _sum_left(val, dest):
while val.ndimension() > dest.ndimension():
val = val.sum(0)
return val
[docs]class gSDENoise(TensorDictPrimer):
"""A gSDE noise initializer.
See the :func:`~torchrl.modules.models.exploration.gSDEModule' for more info.
"""
def __init__(
self,
state_dim=None,
action_dim=None,
shape=None,
**kwargs,
) -> None:
self.state_dim = state_dim
self.action_dim = action_dim
if shape is None:
shape = ()
tail_dim = (
(1,) if state_dim is None or action_dim is None else (action_dim, state_dim)
)
random = state_dim is not None and action_dim is not None
shape = tuple(shape) + tail_dim
primers = {"_eps_gSDE": Unbounded(shape=shape)}
super().__init__(primers=primers, random=random, **kwargs)
[docs]class VecNorm(Transform):
"""Moving average normalization layer for torchrl environments.
VecNorm keeps track of the summary statistics of a dataset to standardize
it on-the-fly. If the transform is in 'eval' mode, the running
statistics are not updated.
If multiple processes are running a similar environment, one can pass a
TensorDictBase instance that is placed in shared memory: if so, every time
the normalization layer is queried it will update the values for all
processes that share the same reference.
To use VecNorm at inference time and avoid updating the values with the new
observations, one should substitute this layer by :meth:`~.to_observation_norm`.
This will provide a static version of `VecNorm` which will not be updated
when the source transform is updated.
To get a frozen copy of the VecNorm layer, see :meth:`~.frozen_copy`.
Args:
in_keys (sequence of NestedKey, optional): keys to be updated.
default: ["observation", "reward"]
out_keys (sequence of NestedKey, optional): destination keys.
Defaults to ``in_keys``.
shared_td (TensorDictBase, optional): A shared tensordict containing the
keys of the transform.
lock (mp.Lock): a lock to prevent race conditions between processes.
Defaults to None (lock created during init).
decay (number, optional): decay rate of the moving average.
default: 0.99
eps (number, optional): lower bound of the running standard
deviation (for numerical underflow). Default is 1e-4.
shapes (List[torch.Size], optional): if provided, represents the shape
of each in_keys. Its length must match the one of ``in_keys``.
Each shape must match the trailing dimension of the corresponding
entry.
If not, the feature dimensions of the entry (ie all dims that do
not belong to the tensordict batch-size) will be considered as
feature dimension.
Examples:
>>> from torchrl.envs.libs.gym import GymEnv
>>> t = VecNorm(decay=0.9)
>>> env = GymEnv("Pendulum-v0")
>>> env = TransformedEnv(env, t)
>>> tds = []
>>> for _ in range(1000):
... td = env.rand_step()
... if td.get("done"):
... _ = env.reset()
... tds += [td]
>>> tds = torch.stack(tds, 0)
>>> print((abs(tds.get(("next", "observation")).mean(0))<0.2).all())
tensor(True)
>>> print((abs(tds.get(("next", "observation")).std(0)-1)<0.2).all())
tensor(True)
"""
def __init__(
self,
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
shared_td: Optional[TensorDictBase] = None,
lock: mp.Lock = None,
decay: float = 0.9999,
eps: float = 1e-4,
shapes: List[torch.Size] = None,
) -> None:
if lock is None:
lock = mp.Lock()
if in_keys is None:
in_keys = ["observation", "reward"]
if out_keys is None:
out_keys = copy(in_keys)
super().__init__(in_keys=in_keys, out_keys=out_keys)
self._td = shared_td
if shared_td is not None and not (
shared_td.is_shared() or shared_td.is_memmap()
):
raise RuntimeError(
"shared_td must be either in shared memory or a memmap " "tensordict."
)
if shared_td is not None:
for key in in_keys:
if (
(_append_last(key, "_sum") not in shared_td.keys())
or (_append_last(key, "_ssq") not in shared_td.keys())
or (_append_last(key, "_count") not in shared_td.keys())
):
raise KeyError(
f"key {key} not present in the shared tensordict "
f"with keys {shared_td.keys()}"
)
self.lock = lock
self.decay = decay
self.shapes = shapes
self.eps = eps
self.frozen = False
[docs] def freeze(self) -> VecNorm:
"""Freezes the VecNorm, avoiding the stats to be updated when called.
See :meth:`~.unfreeze`.
"""
self.frozen = True
return self
[docs] def unfreeze(self) -> VecNorm:
"""Unfreezes the VecNorm.
See :meth:`~.freeze`.
"""
self.frozen = False
return self
[docs] def frozen_copy(self):
"""Returns a copy of the Transform that keeps track of the stats but does not update them."""
if self._td is None:
raise RuntimeError(
"Make sure the VecNorm has been initialized before creating a frozen copy."
)
clone = self.clone()
# replace values
clone._td = self._td.copy()
# freeze
return clone.freeze()
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
# TODO: remove this decorator when trackers are in data
with _set_missing_tolerance(self, True):
return self._call(tensordict_reset)
return tensordict_reset
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
if self.lock is not None:
self.lock.acquire()
for key, key_out in _zip_strict(self.in_keys, self.out_keys):
if key not in tensordict.keys(include_nested=True):
# TODO: init missing rewards with this
# for key_suffix in [_append_last(key, suffix) for suffix in ("_sum", "_ssq", "_count")]:
# tensordict.set(key_suffix, self.container.observation_spec[key_suffix].zero())
continue
self._init(tensordict, key)
# update and standardize
new_val = self._update(
key, tensordict.get(key), N=max(1, tensordict.numel())
)
tensordict.set(key_out, new_val)
if self.lock is not None:
self.lock.release()
return tensordict
forward = _call
def _init(self, tensordict: TensorDictBase, key: str) -> None:
if self._td is None or _append_last(key, "_sum") not in self._td.keys(True):
if key is not key and key in tensordict.keys():
raise RuntimeError(
f"Conflicting key names: {key} from VecNorm and input tensordict keys."
)
if self.shapes is None:
td_view = tensordict.view(-1)
td_select = td_view[0]
item = td_select.get(key)
d = {_append_last(key, "_sum"): torch.zeros_like(item)}
d.update({_append_last(key, "_ssq"): torch.zeros_like(item)})
else:
idx = 0
for in_key in self.in_keys:
if in_key != key:
idx += 1
else:
break
shape = self.shapes[idx]
item = tensordict.get(key)
d = {
_append_last(key, "_sum"): torch.zeros(
shape, device=item.device, dtype=item.dtype
)
}
d.update(
{
_append_last(key, "_ssq"): torch.zeros(
shape, device=item.device, dtype=item.dtype
)
}
)
d.update(
{
_append_last(key, "_count"): torch.zeros(
1, device=item.device, dtype=torch.float
)
}
)
if self._td is None:
self._td = TensorDict(d, batch_size=[])
else:
self._td.update(d)
else:
pass
def _update(self, key, value, N) -> torch.Tensor:
# TODO: we should revert this and have _td be like: TensorDict{"sum": ..., "ssq": ..., "count"...})
# to facilitate the computation of the stats using TD internals.
# Moreover, _td can be locked so these ops will be very fast on CUDA.
_sum = self._td.get(_append_last(key, "_sum"))
_ssq = self._td.get(_append_last(key, "_ssq"))
_count = self._td.get(_append_last(key, "_count"))
value_sum = _sum_left(value, _sum)
if not self.frozen:
_sum *= self.decay
_sum += value_sum
self._td.set_(
_append_last(key, "_sum"),
_sum,
)
_ssq = self._td.get(_append_last(key, "_ssq"))
value_ssq = _sum_left(value.pow(2), _ssq)
if not self.frozen:
_ssq *= self.decay
_ssq += value_ssq
self._td.set_(
_append_last(key, "_ssq"),
_ssq,
)
_count = self._td.get(_append_last(key, "_count"))
if not self.frozen:
_count *= self.decay
_count += N
self._td.set_(
_append_last(key, "_count"),
_count,
)
mean = _sum / _count
std = (_ssq / _count - mean.pow(2)).clamp_min(self.eps).sqrt()
return (value - mean) / std.clamp_min(self.eps)
[docs] def to_observation_norm(self) -> Union[Compose, ObservationNorm]:
"""Converts VecNorm into an ObservationNorm class that can be used at inference time.
The :class:`~torchrl.envs.ObservationNorm` layer can be updated using the :meth:`~torch.nn.Module.state_dict`
API.
Examples:
>>> from torchrl.envs import GymEnv, VecNorm
>>> vecnorm = VecNorm(in_keys=["observation"])
>>> train_env = GymEnv("CartPole-v1", device=None).append_transform(
... vecnorm)
>>>
>>> r = train_env.rollout(4)
>>>
>>> eval_env = GymEnv("CartPole-v1").append_transform(
... vecnorm.to_observation_norm())
>>> print(eval_env.transform.loc, eval_env.transform.scale)
>>>
>>> r = train_env.rollout(4)
>>> # Update entries with state_dict
>>> eval_env.transform.load_state_dict(
... vecnorm.to_observation_norm().state_dict())
>>> print(eval_env.transform.loc, eval_env.transform.scale)
"""
out = []
loc = self.loc
scale = self.scale
for key, key_out in _zip_strict(self.in_keys, self.out_keys):
_out = ObservationNorm(
loc=loc.get(key),
scale=scale.get(key),
standard_normal=True,
in_keys=key,
out_keys=key_out,
)
out += [_out]
if len(self.in_keys) > 1:
return Compose(*out)
return _out
def _get_loc_scale(self, loc_only=False, scale_only=False):
loc = {}
scale = {}
for key in self.in_keys:
_sum = self._td.get(_append_last(key, "_sum"))
_ssq = self._td.get(_append_last(key, "_ssq"))
_count = self._td.get(_append_last(key, "_count"))
loc[key] = _sum / _count
scale[key] = (_ssq / _count - loc[key].pow(2)).clamp_min(self.eps).sqrt()
if not scale_only:
loc = TensorDict(loc)
else:
loc = None
if not loc_only:
scale = TensorDict(scale)
else:
scale = None
return loc, scale
@property
def standard_normal(self):
"""Whether the affine transform given by `loc` and `scale` follows the standard normal equation.
Similar to :class:`~torchrl.envs.ObservationNorm` standard_normal attribute.
Always returns ``True``.
"""
return True
@property
def loc(self):
"""Returns a TensorDict with the loc to be used for an affine transform."""
# We can't cache that value bc the summary stats could be updated by a different process
loc, _ = self._get_loc_scale(loc_only=True)
return loc
@property
def scale(self):
"""Returns a TensorDict with the scale to be used for an affine transform."""
# We can't cache that value bc the summary stats could be updated by a different process
_, scale = self._get_loc_scale(scale_only=True)
return scale
# We use a different separator to ensure that keys can have points within them.
SEP = "-<.>-"
[docs] def get_extra_state(self) -> OrderedDict:
if self._td is None:
warnings.warn(
"Querying state_dict on an uninitialized VecNorm transform will "
"return a `None` value for the summary statistics. "
"Loading such a state_dict on an initialized VecNorm will result in "
"an error."
)
return
return self._td.flatten_keys(self.SEP).to_dict()
[docs] def set_extra_state(self, state: OrderedDict) -> None:
if state is not None:
td = TensorDict(state).unflatten_keys(self.SEP)
if self._td is None and not td.is_shared():
warnings.warn(
"VecNorm wasn't initialized and the tensordict is not shared. In single "
"process settings, this is ok, but if you need to share the statistics "
"between workers this should require some attention. "
"Make sure that the content of VecNorm is transmitted to the workers "
"after calling load_state_dict and not before, as other workers "
"may not have access to the loaded TensorDict."
)
td.share_memory_()
if self._td is not None:
self._td.update_(td)
else:
self._td = td
elif self._td is not None:
raise KeyError("Could not find a tensordict in the state_dict.")
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}(decay={self.decay:4.4f},"
f"eps={self.eps:4.4f}, in_keys={self.in_keys}, out_keys={self.out_keys})"
)
def __getstate__(self) -> Dict[str, Any]:
state = self.__dict__.copy()
_lock = state.pop("lock", None)
if _lock is not None:
state["lock_placeholder"] = None
return state
def __setstate__(self, state: Dict[str, Any]):
if "lock_placeholder" in state:
state.pop("lock_placeholder")
_lock = mp.Lock()
state["lock"] = _lock
self.__dict__.update(state)
[docs] @_apply_to_composite
def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
if isinstance(observation_spec, Bounded):
return Unbounded(
shape=observation_spec.shape,
dtype=observation_spec.dtype,
device=observation_spec.device,
)
return observation_spec
# TODO: incorporate this when trackers are part of the data
# def transform_output_spec(self, output_spec: TensorSpec) -> TensorSpec:
# observation_spec = output_spec["full_observation_spec"]
# reward_spec = output_spec["full_reward_spec"]
# for key in list(observation_spec.keys(True, True)):
# if key in self.in_keys:
# observation_spec[_append_last(key, "_sum")] = observation_spec[key].clone()
# observation_spec[_append_last(key, "_ssq")] = observation_spec[key].clone()
# observation_spec[_append_last(key, "_count")] = observation_spec[key].clone()
# for key in list(reward_spec.keys(True, True)):
# if key in self.in_keys:
# observation_spec[_append_last(key, "_sum")] = reward_spec[key].clone()
# observation_spec[_append_last(key, "_ssq")] = reward_spec[key].clone()
# observation_spec[_append_last(key, "_count")] = reward_spec[key].clone()
# return output_spec
[docs]class RewardSum(Transform):
"""Tracks episode cumulative rewards.
This transform accepts a list of tensordict reward keys (i.e. ´in_keys´) and tracks their cumulative
value along the time dimension for each episode.
When called, the transform writes a new tensordict entry for each ``in_key`` named
``episode_{in_key}`` where the cumulative values are written.
Args:
in_keys (list of NestedKeys, optional): Input reward keys.
All ´in_keys´ should be part of the environment reward_spec.
If no ``in_keys`` are specified, this transform assumes ``"reward"`` to be the input key.
However, multiple rewards (e.g. ``"reward1"`` and ``"reward2""``) can also be specified.
out_keys (list of NestedKeys, optional): The output sum keys, should be one per each input key.
reset_keys (list of NestedKeys, optional): the list of reset_keys to be
used, if the parent environment cannot be found. If provided, this
value will prevail over the environment ``reset_keys``.
Keyword Args:
reward_spec (bool, optional): if ``True``, the new reward entry will be registered in the
reward specs. Defaults to ``False`` (registered in ``observation_specs``).
Examples:
>>> from torchrl.envs.transforms import RewardSum, TransformedEnv
>>> from torchrl.envs.libs.gym import GymEnv
>>> env = TransformedEnv(GymEnv("CartPole-v1"), RewardSum())
>>> env.set_seed(0)
>>> torch.manual_seed(0)
>>> td = env.reset()
>>> print(td["episode_reward"])
tensor([0.])
>>> td = env.rollout(3)
>>> print(td["next", "episode_reward"])
tensor([[1.],
[2.],
[3.]])
"""
def __init__(
self,
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
reset_keys: Sequence[NestedKey] | None = None,
*,
reward_spec: bool = False,
):
"""Initialises the transform. Filters out non-reward input keys and defines output keys."""
super().__init__(in_keys=in_keys, out_keys=out_keys)
self._reset_keys = reset_keys
self._keys_checked = False
self.reward_spec = reward_spec
@property
def in_keys(self):
in_keys = self.__dict__.get("_in_keys", None)
if in_keys in (None, []):
# retrieve rewards from parent env
parent = self.parent
if parent is None:
in_keys = ["reward"]
else:
in_keys = copy(parent.reward_keys)
self._in_keys = in_keys
return in_keys
@in_keys.setter
def in_keys(self, value):
if value is not None:
if isinstance(value, (str, tuple)):
value = [value]
value = [unravel_key(val) for val in value]
self._in_keys = value
@property
def out_keys(self):
out_keys = self.__dict__.get("_out_keys", None)
if out_keys in (None, []):
out_keys = [
_replace_last(in_key, f"episode_{_unravel_key_to_tuple(in_key)[-1]}")
for in_key in self.in_keys
]
self._out_keys = out_keys
return out_keys
@out_keys.setter
def out_keys(self, value):
# we must access the private attribute because this check occurs before
# the parent env is defined
if value is not None and len(self._in_keys) != len(value):
raise ValueError(
"RewardSum expects the same number of input and output keys"
)
if value is not None:
if isinstance(value, (str, tuple)):
value = [value]
value = [unravel_key(val) for val in value]
self._out_keys = value
@property
def reset_keys(self):
reset_keys = self.__dict__.get("_reset_keys", None)
if reset_keys is None:
parent = self.parent
if parent is None:
raise TypeError(
"reset_keys not provided but parent env not found. "
"Make sure that the reset_keys are provided during "
"construction if the transform does not have a container env."
)
# let's try to match the reset keys with the in_keys.
# We take the filtered reset keys, which are the only keys that really
# matter when calling reset, and check that they match the in_keys root.
reset_keys = parent._filtered_reset_keys
if len(reset_keys) == 1:
reset_keys = list(reset_keys) * len(self.in_keys)
def _check_match(reset_keys, in_keys):
# if this is called, the length of reset_keys and in_keys must match
for reset_key, in_key in _zip_strict(reset_keys, in_keys):
# having _reset at the root and the reward_key ("agent", "reward") is allowed
# but having ("agent", "_reset") and "reward" isn't
if isinstance(reset_key, tuple) and isinstance(in_key, str):
return False
if (
isinstance(reset_key, tuple)
and isinstance(in_key, tuple)
and in_key[: (len(reset_key) - 1)] != reset_key[:-1]
):
return False
return True
if not _check_match(reset_keys, self.in_keys):
raise ValueError(
f"Could not match the env reset_keys {reset_keys} with the {type(self)} in_keys {self.in_keys}. "
f"Please provide the reset_keys manually. Reset entries can be "
f"non-unique and must be right-expandable to the shape of "
f"the input entries."
)
reset_keys = copy(reset_keys)
self._reset_keys = reset_keys
if not self._keys_checked and len(reset_keys) != len(self.in_keys):
raise ValueError(
f"Could not match the env reset_keys {reset_keys} with the in_keys {self.in_keys}. "
"Please make sure that these have the same length."
)
self._keys_checked = True
return reset_keys
@reset_keys.setter
def reset_keys(self, value):
if value is not None:
if isinstance(value, (str, tuple)):
value = [value]
value = [unravel_key(val) for val in value]
self._reset_keys = value
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
"""Resets episode rewards."""
for in_key, reset_key, out_key in _zip_strict(
self.in_keys, self.reset_keys, self.out_keys
):
_reset = _get_reset(reset_key, tensordict)
value = tensordict.get(out_key, default=None)
if value is None:
value = self.parent.full_reward_spec[in_key].zero()
else:
value = torch.where(expand_as_right(~_reset, value), value, 0.0)
tensordict_reset.set(out_key, value)
return tensordict_reset
def _step(
self, tensordict: TensorDictBase, next_tensordict: TensorDictBase
) -> TensorDictBase:
"""Updates the episode rewards with the step rewards."""
# Update episode rewards
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys):
if in_key in next_tensordict.keys(include_nested=True):
reward = next_tensordict.get(in_key)
prev_reward = tensordict.get(out_key, 0.0)
next_tensordict.set(out_key, prev_reward + reward)
elif not self.missing_tolerance:
raise KeyError(f"'{in_key}' not found in tensordict {tensordict}")
return next_tensordict
[docs] def transform_input_spec(self, input_spec: TensorSpec) -> TensorSpec:
state_spec = input_spec["full_state_spec"]
if state_spec is None:
state_spec = Composite(shape=input_spec.shape, device=input_spec.device)
state_spec.update(self._generate_episode_reward_spec())
input_spec["full_state_spec"] = state_spec
return input_spec
def _generate_episode_reward_spec(self) -> Composite:
episode_reward_spec = Composite()
reward_spec = self.parent.full_reward_spec
reward_spec_keys = self.parent.reward_keys
# Define episode specs for all out_keys
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys):
if (
in_key in reward_spec_keys
): # if this out_key has a corresponding key in reward_spec
out_key = _unravel_key_to_tuple(out_key)
temp_episode_reward_spec = episode_reward_spec
temp_rew_spec = reward_spec
for sub_key in out_key[:-1]:
if (
not isinstance(temp_rew_spec, Composite)
or sub_key not in temp_rew_spec.keys()
):
break
if sub_key not in temp_episode_reward_spec.keys():
temp_episode_reward_spec[sub_key] = temp_rew_spec[
sub_key
].empty()
temp_rew_spec = temp_rew_spec[sub_key]
temp_episode_reward_spec = temp_episode_reward_spec[sub_key]
episode_reward_spec[out_key] = reward_spec[in_key].clone()
else:
raise ValueError(
f"The in_key: {in_key} is not present in the reward spec {reward_spec}."
)
return episode_reward_spec
[docs] def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
"""Transforms the observation spec, adding the new keys generated by RewardSum."""
if self.reward_spec:
return observation_spec
if not isinstance(observation_spec, Composite):
observation_spec = Composite(
observation=observation_spec, shape=self.parent.batch_size
)
observation_spec.update(self._generate_episode_reward_spec())
return observation_spec
[docs] def transform_reward_spec(self, reward_spec: TensorSpec) -> TensorSpec:
if not self.reward_spec:
return reward_spec
reward_spec.update(self._generate_episode_reward_spec())
return reward_spec
[docs] def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
time_dim = [i for i, name in enumerate(tensordict.names) if name == "time"]
if not time_dim:
raise ValueError(
"At least one dimension of the tensordict must be named 'time' in offline mode"
)
time_dim = time_dim[0] - 1
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys):
reward = tensordict[in_key]
cumsum = reward.cumsum(time_dim)
tensordict.set(out_key, cumsum)
return tensordict
[docs]class StepCounter(Transform):
"""Counts the steps from a reset and optionally sets the truncated state to ``True`` after a certain number of steps.
The ``"done"`` state is also adapted accordingly (as done is the disjunction
of task completion and early truncation).
Args:
max_steps (int, optional): a positive integer that indicates the
maximum number of steps to take before setting the ``truncated_key``
entry to ``True``.
truncated_key (str, optional): the key where the truncated entries
should be written. Defaults to ``"truncated"``, which is recognised by
data collectors as a reset signal.
This argument can only be a string (not a nested key) as it will be
matched to each of the leaf done key in the parent environment
(eg, a ``("agent", "done")`` key will be accompanied by a
``("agent", "truncated")`` if the ``"truncated"`` key name is used).
step_count_key (str, optional): the key where the step count entries
should be written. Defaults to ``"step_count"``.
This argument can only be a string (not a nested key) as it will be
matched to each of the leaf done key in the parent environment
(eg, a ``("agent", "done")`` key will be accompanied by a
``("agent", "step_count")`` if the ``"step_count"`` key name is used).
update_done (bool, optional): if ``True``, the ``"done"`` boolean tensor
at the level of ``"truncated"``
will be updated.
This signal indicates that the trajectory has reached its ends,
either because the task is completed (``"completed"`` entry is
``True``) or because it has been truncated (``"truncated"`` entry
is ``True``).
Defaults to ``True``.
.. note:: To ensure compatibility with environments that have multiple
done_key(s), this transform will write a step_count entry for
every done entry within the tensordict.
Examples:
>>> import gymnasium
>>> from torchrl.envs import GymWrapper
>>> base_env = GymWrapper(gymnasium.make("Pendulum-v1"))
>>> env = TransformedEnv(base_env,
... StepCounter(max_steps=5))
>>> rollout = env.rollout(100)
>>> print(rollout)
TensorDict(
fields={
action: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.float32, is_shared=False),
done: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False),
completed: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False),
completed: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
observation: Tensor(shape=torch.Size([5, 3]), device=cpu, dtype=torch.float32, is_shared=False),
reward: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.float32, is_shared=False),
step_count: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.int64, is_shared=False),
truncated: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
batch_size=torch.Size([5]),
device=cpu,
is_shared=False),
observation: Tensor(shape=torch.Size([5, 3]), device=cpu, dtype=torch.float32, is_shared=False),
step_count: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.int64, is_shared=False),
truncated: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
batch_size=torch.Size([5]),
device=cpu,
is_shared=False)
>>> print(rollout["next", "step_count"])
tensor([[1],
[2],
[3],
[4],
[5]])
"""
invertible = False
def __init__(
self,
max_steps: Optional[int] = None,
truncated_key: str | None = "truncated",
step_count_key: str | None = "step_count",
update_done: bool = True,
):
if max_steps is not None and max_steps < 1:
raise ValueError("max_steps should have a value greater or equal to one.")
if not isinstance(truncated_key, str):
raise ValueError("truncated_key must be a string.")
if not isinstance(step_count_key, str):
raise ValueError("step_count_key must be a string.")
self.max_steps = max_steps
self.truncated_key = truncated_key
self.step_count_key = step_count_key
self.update_done = update_done
super().__init__()
@property
def truncated_keys(self):
truncated_keys = self.__dict__.get("_truncated_keys", None)
if truncated_keys is None:
# make the default truncated keys
truncated_keys = []
for reset_key in self.parent._filtered_reset_keys:
if isinstance(reset_key, str):
key = self.truncated_key
else:
key = (*reset_key[:-1], self.truncated_key)
truncated_keys.append(key)
self._truncated_keys = truncated_keys
return truncated_keys
@property
def done_keys(self):
done_keys = self.__dict__.get("_done_keys", None)
if done_keys is None:
# make the default done keys
done_keys = []
for reset_key in self.parent._filtered_reset_keys:
if isinstance(reset_key, str):
key = "done"
else:
key = (*reset_key[:-1], "done")
done_keys.append(key)
self.__dict__["_done_keys"] = done_keys
return done_keys
@property
def terminated_keys(self):
terminated_keys = self.__dict__.get("_terminated_keys", None)
if terminated_keys is None:
# make the default terminated keys
terminated_keys = []
for reset_key in self.parent._filtered_reset_keys:
if isinstance(reset_key, str):
key = "terminated"
else:
key = (*reset_key[:-1], "terminated")
terminated_keys.append(key)
self.__dict__["_terminated_keys"] = terminated_keys
return terminated_keys
@property
def step_count_keys(self):
step_count_keys = self.__dict__.get("_step_count_keys", None)
if step_count_keys is None:
# make the default step_count keys
step_count_keys = []
for reset_key in self.parent._filtered_reset_keys:
if isinstance(reset_key, str):
key = self.step_count_key
else:
key = (*reset_key[:-1], self.step_count_key)
step_count_keys.append(key)
self.__dict__["_step_count_keys"] = step_count_keys
return step_count_keys
@property
def reset_keys(self):
if self.parent is not None:
return self.parent._filtered_reset_keys
# fallback on default "_reset"
return ["_reset"]
@property
def full_done_spec(self):
return self.parent.output_spec["full_done_spec"] if self.parent else None
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
# get reset signal
for (
step_count_key,
truncated_key,
terminated_key,
reset_key,
done_key,
) in _zip_strict(
self.step_count_keys,
self.truncated_keys,
self.terminated_keys,
self.reset_keys,
self.done_keys,
):
reset = tensordict.get(reset_key, default=None)
if reset is None:
# get done status, just to inform the reset shape, dtype and device
for entry_name in (terminated_key, truncated_key, done_key):
done = tensordict.get(entry_name, default=None)
if done is not None:
break
else:
# It may be the case that reset did not provide a done state, in which case
# we fall back on the spec
done = self.parent.output_spec["full_done_spec", entry_name].zero()
reset = torch.ones_like(done)
step_count = tensordict.get(step_count_key, default=None)
if step_count is None:
step_count = self.container.observation_spec[step_count_key].zero()
if step_count.device != reset.device:
step_count = step_count.to(reset.device, non_blocking=True)
# zero the step count if reset is needed
step_count = torch.where(~expand_as_right(reset, step_count), step_count, 0)
tensordict_reset.set(step_count_key, step_count)
if self.max_steps is not None:
truncated = step_count >= self.max_steps
truncated = truncated | tensordict_reset.get(truncated_key, False)
if self.update_done:
# we assume no done after reset
tensordict_reset.set(done_key, truncated)
tensordict_reset.set(truncated_key, truncated)
return tensordict_reset
def _step(
self, tensordict: TensorDictBase, next_tensordict: TensorDictBase
) -> TensorDictBase:
for step_count_key, truncated_key, done_key in _zip_strict(
self.step_count_keys, self.truncated_keys, self.done_keys
):
step_count = tensordict.get(step_count_key)
next_step_count = step_count + 1
next_tensordict.set(step_count_key, next_step_count)
if self.max_steps is not None:
truncated = next_step_count >= self.max_steps
truncated = truncated | next_tensordict.get(truncated_key, False)
if self.update_done:
done = next_tensordict.get(done_key, None)
# we can have terminated and truncated
# terminated = next_tensordict.get(terminated_key, None)
# if terminated is not None:
# truncated = truncated & ~terminated
done = truncated | done # we assume no done after reset
next_tensordict.set(done_key, done)
next_tensordict.set(truncated_key, truncated)
return next_tensordict
[docs] def transform_observation_spec(self, observation_spec: Composite) -> Composite:
if not isinstance(observation_spec, Composite):
raise ValueError(
f"observation_spec was expected to be of type Composite. Got {type(observation_spec)} instead."
)
full_done_spec = self.parent.output_spec["full_done_spec"]
for step_count_key in self.step_count_keys:
step_count_key = unravel_key(step_count_key)
# find a matching done key (there might be more than one)
for done_key in self.done_keys:
# check root
if type(done_key) != type(step_count_key):
continue
if isinstance(done_key, tuple):
if done_key[:-1] == step_count_key[:-1]:
shape = full_done_spec[done_key].shape
break
if isinstance(done_key, str):
shape = full_done_spec[done_key].shape
break
else:
raise KeyError(
f"Could not find root of step_count_key {step_count_key} in done keys {self.done_keys}."
)
observation_spec[step_count_key] = Bounded(
shape=shape,
dtype=torch.int64,
device=observation_spec.device,
low=0,
high=torch.iinfo(torch.int64).max,
)
return super().transform_observation_spec(observation_spec)
[docs] def transform_output_spec(self, output_spec: Composite) -> Composite:
if self.max_steps:
full_done_spec = self.parent.output_spec["full_done_spec"]
for truncated_key in self.truncated_keys:
truncated_key = unravel_key(truncated_key)
# find a matching done key (there might be more than one)
for done_key in self.done_keys:
# check root
if type(done_key) != type(truncated_key):
continue
if isinstance(done_key, tuple):
if done_key[:-1] == truncated_key[:-1]:
shape = full_done_spec[done_key].shape
break
if isinstance(done_key, str):
shape = full_done_spec[done_key].shape
break
else:
raise KeyError(
f"Could not find root of truncated_key {truncated_key} in done keys {self.done_keys}."
)
full_done_spec[truncated_key] = Categorical(
2, dtype=torch.bool, device=output_spec.device, shape=shape
)
if self.update_done:
for done_key in self.done_keys:
done_key = unravel_key(done_key)
# find a matching done key (there might be more than one)
for done_key in self.done_keys:
# check root
if type(done_key) != type(done_key):
continue
if isinstance(done_key, tuple):
if done_key[:-1] == done_key[:-1]:
shape = full_done_spec[done_key].shape
break
if isinstance(done_key, str):
shape = full_done_spec[done_key].shape
break
else:
raise KeyError(
f"Could not find root of stop_key {done_key} in done keys {self.done_keys}."
)
full_done_spec[done_key] = Categorical(
2, dtype=torch.bool, device=output_spec.device, shape=shape
)
output_spec["full_done_spec"] = full_done_spec
return super().transform_output_spec(output_spec)
[docs] def transform_input_spec(self, input_spec: Composite) -> Composite:
if not isinstance(input_spec, Composite):
raise ValueError(
f"input_spec was expected to be of type Composite. Got {type(input_spec)} instead."
)
if input_spec["full_state_spec"] is None:
input_spec["full_state_spec"] = Composite(
shape=input_spec.shape, device=input_spec.device
)
full_done_spec = self.parent.output_spec["full_done_spec"]
for step_count_key in self.step_count_keys:
step_count_key = unravel_key(step_count_key)
# find a matching done key (there might be more than one)
for done_key in self.done_keys:
# check root
if type(done_key) != type(step_count_key):
continue
if isinstance(done_key, tuple):
if done_key[:-1] == step_count_key[:-1]:
shape = full_done_spec[done_key].shape
break
if isinstance(done_key, str):
shape = full_done_spec[done_key].shape
break
else:
raise KeyError(
f"Could not find root of step_count_key {step_count_key} in done keys {self.done_keys}."
)
input_spec[unravel_key(("full_state_spec", step_count_key))] = Bounded(
shape=shape,
dtype=torch.int64,
device=input_spec.device,
low=0,
high=torch.iinfo(torch.int64).max,
)
return input_spec
[docs] def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
raise NotImplementedError(
"StepCounter cannot be called independently, only its step and reset methods "
"are functional. The reason for this is that it is hard to consider using "
"StepCounter with non-sequential data, such as those collected by a replay buffer "
"or a dataset. If you need StepCounter to work on a batch of sequential data "
"(ie as LSTM would work over a whole sequence of data), file an issue on "
"TorchRL requesting that feature."
)
[docs]class ExcludeTransform(Transform):
"""Excludes keys from the data.
Args:
*excluded_keys (iterable of NestedKey): The name of the keys to exclude. If the key is
not present, it is simply ignored.
inverse (bool, optional): if ``True``, the exclusion will occur during the ``inv`` call.
Defaults to ``False``.
Examples:
>>> import gymnasium
>>> from torchrl.envs import GymWrapper
>>> env = TransformedEnv(
... GymWrapper(gymnasium.make("Pendulum-v1")),
... ExcludeTransform("truncated")
... )
>>> env.rollout(3)
TensorDict(
fields={
action: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False),
done: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False),
observation: Tensor(shape=torch.Size([3, 3]), device=cpu, dtype=torch.float32, is_shared=False),
reward: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([3]),
device=cpu,
is_shared=False),
observation: Tensor(shape=torch.Size([3, 3]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([3]),
device=cpu,
is_shared=False)
"""
def __init__(self, *excluded_keys, inverse: bool = False):
super().__init__()
try:
excluded_keys = unravel_key_list(excluded_keys)
except TypeError:
raise TypeError(
"excluded keys must be a list or tuple of strings or tuples of strings."
)
self.excluded_keys = excluded_keys
self.inverse = inverse
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
if not self.inverse:
return tensordict.exclude(*self.excluded_keys)
return tensordict
def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase:
if self.inverse:
return tensordict.exclude(*self.excluded_keys)
return tensordict
forward = _call
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
if not self.inverse:
return tensordict_reset.exclude(*self.excluded_keys)
return tensordict
[docs] def transform_output_spec(self, output_spec: Composite) -> Composite:
if not self.inverse:
full_done_spec = output_spec["full_done_spec"]
full_reward_spec = output_spec["full_reward_spec"]
full_observation_spec = output_spec["full_observation_spec"]
for key in self.excluded_keys:
# done_spec
if unravel_key(key) in list(full_done_spec.keys(True, True)):
del full_done_spec[key]
continue
# reward_spec
if unravel_key(key) in list(full_reward_spec.keys(True, True)):
del full_reward_spec[key]
continue
# observation_spec
if unravel_key(key) in list(full_observation_spec.keys(True, True)):
del full_observation_spec[key]
continue
raise KeyError(f"Key {key} not found in the environment outputs.")
return output_spec
[docs]class SelectTransform(Transform):
"""Select keys from the input tensordict.
In general, the :obj:`ExcludeTransform` should be preferred: this transforms also
selects the "action" (or other keys from input_spec), "done" and "reward"
keys but other may be necessary.
Args:
*selected_keys (iterable of NestedKey): The name of the keys to select. If the key is
not present, it is simply ignored.
Keyword Args:
keep_rewards (bool, optional): if ``False``, the reward keys must be provided
if they should be kept. Defaults to ``True``.
keep_dones (bool, optional): if ``False``, the done keys must be provided
if they should be kept. Defaults to ``True``.
Examples:
>>> import gymnasium
>>> from torchrl.envs import GymWrapper
>>> env = TransformedEnv(
... GymWrapper(gymnasium.make("Pendulum-v1")),
... SelectTransform("observation", "reward", "done", keep_dones=False), # we leave done behind
... )
>>> env.rollout(3) # the truncated key is now absent
TensorDict(
fields={
action: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False),
done: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False),
observation: Tensor(shape=torch.Size([3, 3]), device=cpu, dtype=torch.float32, is_shared=False),
reward: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([3]),
device=cpu,
is_shared=False),
observation: Tensor(shape=torch.Size([3, 3]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([3]),
device=cpu,
is_shared=False)
"""
def __init__(
self,
*selected_keys: NestedKey,
keep_rewards: bool = True,
keep_dones: bool = True,
):
super().__init__()
try:
selected_keys = unravel_key_list(selected_keys)
except TypeError:
raise TypeError(
"selected keys must be a list or tuple of strings or tuples of strings."
)
self.selected_keys = selected_keys
self.keep_done_keys = keep_dones
self.keep_reward_keys = keep_rewards
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
if self.parent is not None:
input_keys = self.parent.state_spec.keys(True, True)
else:
input_keys = []
if self.keep_reward_keys:
reward_keys = self.parent.reward_keys if self.parent else ["reward"]
else:
reward_keys = []
if self.keep_done_keys:
done_keys = self.parent.done_keys if self.parent else ["done"]
else:
done_keys = []
return tensordict.select(
*self.selected_keys, *reward_keys, *done_keys, *input_keys, strict=False
)
forward = _call
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
if self.parent is not None:
input_keys = self.parent.state_spec.keys(True, True)
else:
input_keys = []
if self.keep_reward_keys:
reward_keys = self.parent.reward_keys if self.parent else ["reward"]
else:
reward_keys = []
if self.keep_done_keys:
done_keys = self.parent.done_keys if self.parent else ["done"]
else:
done_keys = []
return tensordict_reset.select(
*self.selected_keys, *reward_keys, *done_keys, *input_keys, strict=False
)
[docs] def transform_output_spec(self, output_spec: Composite) -> Composite:
full_done_spec = output_spec["full_done_spec"]
full_reward_spec = output_spec["full_reward_spec"]
full_observation_spec = output_spec["full_observation_spec"]
if not self.keep_done_keys:
for key in list(full_done_spec.keys(True, True)):
if unravel_key(key) not in self.selected_keys:
del full_done_spec[key]
for key in list(full_observation_spec.keys(True, True)):
if unravel_key(key) not in self.selected_keys:
del full_observation_spec[key]
if not self.keep_reward_keys:
for key in list(full_reward_spec.keys(True, True)):
if unravel_key(key) not in self.selected_keys:
del full_reward_spec[key]
return output_spec
[docs]class TimeMaxPool(Transform):
"""Take the maximum value in each position over the last T observations.
This transform take the maximum value in each position for all in_keys tensors over the last T time steps.
Args:
in_keys (sequence of NestedKey, optional): input keys on which the max pool will be applied. Defaults to "observation" if left empty.
out_keys (sequence of NestedKey, optional): output keys where the output will be written. Defaults to `in_keys` if left empty.
T (int, optional): Number of time steps over which to apply max pooling.
reset_key (NestedKey, optional): the reset key to be used as partial
reset indicator. Must be unique. If not provided, defaults to the
only reset key of the parent environment (if it has only one)
and raises an exception otherwise.
Examples:
>>> from torchrl.envs import GymEnv
>>> base_env = GymEnv("Pendulum-v1")
>>> env = TransformedEnv(base_env, TimeMaxPool(in_keys=["observation"], T=10))
>>> torch.manual_seed(0)
>>> env.set_seed(0)
>>> rollout = env.rollout(10)
>>> print(rollout["observation"]) # values should be increasing up until the 10th step
tensor([[ 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0216, 0.0000],
[ 0.0000, 0.1149, 0.0000],
[ 0.0000, 0.1990, 0.0000],
[ 0.0000, 0.2749, 0.0000],
[ 0.0000, 0.3281, 0.0000],
[-0.9290, 0.3702, -0.8978]])
.. note:: :class:`~TimeMaxPool` currently only supports ``done`` signal at the root.
Nested ``done``, such as those found in MARL settings, are currently not supported.
If this feature is needed, please raise an issue on TorchRL repo.
"""
invertible = False
def __init__(
self,
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
T: int = 1,
reset_key: NestedKey | None = None,
):
if in_keys is None:
in_keys = ["observation"]
if out_keys is None:
out_keys = copy(in_keys)
super().__init__(in_keys=in_keys, out_keys=out_keys)
if T < 1:
raise ValueError(
"TimeMaxPoolTransform T parameter should have a value greater or equal to one."
)
if len(self.in_keys) != len(self.out_keys):
raise ValueError(
"TimeMaxPoolTransform in_keys and out_keys don't have the same number of elements"
)
self.buffer_size = T
for in_key in self.in_keys:
buffer_name = self._buffer_name(in_key)
setattr(
self,
buffer_name,
torch.nn.parameter.UninitializedBuffer(
device=torch.device("cpu"), dtype=torch.get_default_dtype()
),
)
self.reset_key = reset_key
@staticmethod
def _buffer_name(in_key):
in_key_str = "_".join(in_key) if isinstance(in_key, tuple) else in_key
buffer_name = f"_maxpool_buffer_{in_key_str}"
return buffer_name
@property
def reset_key(self):
reset_key = self.__dict__.get("_reset_key", None)
if reset_key is None:
reset_keys = self.parent.reset_keys
if len(reset_keys) > 1:
raise RuntimeError(
f"Got more than one reset key in env {self.container}, cannot infer which one to use. Consider providing the reset key in the {type(self)} constructor."
)
reset_key = self._reset_key = reset_keys[0]
return reset_key
@reset_key.setter
def reset_key(self, value):
self._reset_key = value
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
_reset = _get_reset(self.reset_key, tensordict)
for in_key in self.in_keys:
buffer_name = self._buffer_name(in_key)
buffer = getattr(self, buffer_name)
if isinstance(buffer, torch.nn.parameter.UninitializedBuffer):
continue
if not _reset.all():
_reset_exp = _reset.expand(buffer.shape[0], *_reset.shape)
buffer[_reset_exp] = 0.0
else:
buffer.fill_(0.0)
with _set_missing_tolerance(self, True):
for in_key in self.in_keys:
val_reset = tensordict_reset.get(in_key, None)
val_prev = tensordict.get(in_key, None)
# if an in_key is missing, we try to copy it from the previous step
if val_reset is None and val_prev is not None:
tensordict_reset.set(in_key, val_prev)
elif val_prev is None and val_reset is None:
raise KeyError(f"Could not find {in_key} in the reset data.")
return self._call(tensordict_reset, _reset=_reset)
def _make_missing_buffer(self, tensordict, in_key, buffer_name):
buffer = getattr(self, buffer_name)
data = tensordict.get(in_key)
size = list(data.shape)
size.insert(0, self.buffer_size)
buffer.materialize(size)
buffer = buffer.to(dtype=data.dtype, device=data.device).zero_()
setattr(self, buffer_name, buffer)
return buffer
def _call(self, tensordict: TensorDictBase, _reset=None) -> TensorDictBase:
"""Update the episode tensordict with max pooled keys."""
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys):
# Lazy init of buffers
buffer_name = self._buffer_name(in_key)
buffer = getattr(self, buffer_name)
if isinstance(buffer, torch.nn.parameter.UninitializedBuffer):
buffer = self._make_missing_buffer(tensordict, in_key, buffer_name)
if _reset is not None:
# we must use only the reset data
buffer[:, _reset] = torch.roll(buffer[:, _reset], shifts=1, dims=0)
# add new obs
data = tensordict.get(in_key)
buffer[0, _reset] = data[_reset]
# apply max pooling
pooled_tensor, _ = buffer[:, _reset].max(dim=0)
pooled_tensor = torch.zeros_like(data).masked_scatter_(
expand_as_right(_reset, data), pooled_tensor
)
# add to tensordict
tensordict.set(out_key, pooled_tensor)
continue
# shift obs 1 position to the right
buffer.copy_(torch.roll(buffer, shifts=1, dims=0))
# add new obs
buffer[0].copy_(tensordict.get(in_key))
# apply max pooling
pooled_tensor, _ = buffer.max(dim=0)
# add to tensordict
tensordict.set(out_key, pooled_tensor)
return tensordict
[docs] @_apply_to_composite
def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
return observation_spec
[docs] def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
raise NotImplementedError(
"TimeMaxPool cannot be called independently, only its step and reset methods "
"are functional. The reason for this is that it is hard to consider using "
"TimeMaxPool with non-sequential data, such as those collected by a replay buffer "
"or a dataset. If you need TimeMaxPool to work on a batch of sequential data "
"(ie as LSTM would work over a whole sequence of data), file an issue on "
"TorchRL requesting that feature."
)
[docs]class RandomCropTensorDict(Transform):
"""A trajectory sub-sampler for ReplayBuffer and modules.
Gathers a sub-sequence of a defined length along the last dimension of the input
tensordict.
This can be used to get cropped trajectories from trajectories sampled
from a ReplayBuffer.
This transform is primarily designed to be used with replay buffers and modules.
Currently, it cannot be used as an environment transform.
Do not hesitate to request for this behavior through an issue if this is
desired.
Args:
sub_seq_len (int): the length of the sub-trajectory to sample
sample_dim (int, optional): the dimension along which the cropping
should occur. Negative dimensions should be preferred to make
the transform robust to tensordicts of varying batch dimensions.
Defaults to -1 (the default time dimension in TorchRL).
mask_key (NestedKey): If provided, this represents the mask key to be looked
for when doing the sampling. If provided, it only valid elements will
be returned. It is assumed that the mask is a boolean tensor with
first True values and then False values, not mixed together.
:class:`RandomCropTensorDict` will NOT check that this is respected
hence any error caused by an improper mask risks to go unnoticed.
Defaults: None (no mask key).
"""
def __init__(
self,
sub_seq_len: int,
sample_dim: int = -1,
mask_key: Optional[NestedKey] = None,
):
self.sub_seq_len = sub_seq_len
if sample_dim > 0:
warnings.warn(
"A positive shape has been passed to the RandomCropTensorDict "
"constructor. This may have unexpected behaviors when the "
"passed tensordicts have inconsistent batch dimensions. "
"For context, by convention, TorchRL concatenates time steps "
"along the last dimension of the tensordict."
)
self.sample_dim = sample_dim
self.mask_key = mask_key
super().__init__()
[docs] def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
shape = tensordict.shape
dim = self.sample_dim
# shape must have at least one dimension
if not len(shape):
raise RuntimeError(
"Cannot sub-sample from a tensordict with an empty shape."
)
if shape[dim] < self.sub_seq_len:
raise RuntimeError(
f"Cannot sample trajectories of length {self.sub_seq_len} along"
f" dimension {dim} given a tensordict of shape "
f"{tensordict.shape}. Consider reducing the sub_seq_len "
f"parameter or increase sample length."
)
max_idx_0 = shape[dim] - self.sub_seq_len
idx_shape = list(tensordict.shape)
idx_shape[dim] = 1
device = tensordict.device
if device is None:
device = torch.device("cpu")
if self.mask_key is None or self.mask_key not in tensordict.keys(
isinstance(self.mask_key, tuple)
):
idx_0 = torch.randint(max_idx_0, idx_shape, device=device)
else:
# get the traj length for each entry
mask = tensordict.get(self.mask_key)
if mask.shape != tensordict.shape:
raise ValueError(
"Expected a mask of the same shape as the tensordict. Got "
f"mask.shape={mask.shape} and tensordict.shape="
f"{tensordict.shape} instead."
)
traj_lengths = mask.cumsum(self.sample_dim).max(self.sample_dim, True)[0]
if (traj_lengths < self.sub_seq_len).any():
raise RuntimeError(
f"Cannot sample trajectories of length {self.sub_seq_len} when the minimum "
f"trajectory length is {traj_lengths.min()}."
)
# take a random number between 0 and traj_lengths - self.sub_seq_len
idx_0 = (
torch.rand(idx_shape, device=device) * (traj_lengths - self.sub_seq_len)
).to(torch.long)
arange = torch.arange(self.sub_seq_len, device=idx_0.device)
arange_shape = [1 for _ in range(tensordict.ndimension())]
arange_shape[dim] = len(arange)
arange = arange.view(arange_shape)
idx = idx_0 + arange
return tensordict.gather(dim=self.sample_dim, index=idx)
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
with _set_missing_tolerance(self, True):
tensordict_reset = self.forward(tensordict_reset)
return tensordict_reset
[docs]class InitTracker(Transform):
"""Reset tracker.
This transform populates the step/reset tensordict with a reset tracker entry
that is set to ``True`` whenever :meth:`~.reset` is called.
Args:
init_key (NestedKey, optional): the key to be used for the tracker entry.
In case of multiple _reset flags, this key is used as the leaf replacement for each.
Examples:
>>> from torchrl.envs.libs.gym import GymEnv
>>> env = TransformedEnv(GymEnv("Pendulum-v1"), InitTracker())
>>> td = env.reset()
>>> print(td["is_init"])
tensor(True)
>>> td = env.rand_step(td)
>>> print(td["next", "is_init"])
tensor(False)
"""
def __init__(self, init_key: str = "is_init"):
if not isinstance(init_key, str):
raise ValueError(
"init_key can only be of type str as it will be the leaf key associated to each reset flag."
)
self.init_key = init_key
super().__init__()
def set_container(self, container: Union[Transform, EnvBase]) -> None:
self._init_keys = None
return super().set_container(container)
@property
def out_keys(self):
return self.init_keys
@out_keys.setter
def out_keys(self, value):
if value in (None, []):
return
raise ValueError(
"Cannot set non-empty out-keys when out-keys are defined by the init_key value."
)
@property
def init_keys(self):
init_keys = self.__dict__.get("_init_keys", None)
if init_keys is not None:
return init_keys
init_keys = []
if self.parent is None:
raise NotImplementedError(
FORWARD_NOT_IMPLEMENTED.format(self.__class__.__name__)
)
for reset_key in self.parent._filtered_reset_keys:
if isinstance(reset_key, str):
init_key = self.init_key
else:
init_key = unravel_key((reset_key[:-1], self.init_key))
init_keys.append(init_key)
self._init_keys = init_keys
return self._init_keys
@property
def reset_keys(self):
return self.parent._filtered_reset_keys
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
for init_key in self.init_keys:
done_key = _replace_last(init_key, "done")
if init_key not in tensordict.keys(True, True):
device = tensordict.device
if device is None:
device = torch.device("cpu")
shape = self.parent.full_done_spec[done_key].shape
tensordict.set(
init_key,
torch.zeros(shape, device=device, dtype=torch.bool),
)
return tensordict
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
device = tensordict.device
if device is None:
device = torch.device("cpu")
for reset_key, init_key in _zip_strict(self.reset_keys, self.init_keys):
_reset = tensordict.get(reset_key, None)
if _reset is None:
done_key = _replace_last(init_key, "done")
shape = self.parent.full_done_spec[done_key].shape
tensordict_reset.set(
init_key,
torch.ones(
shape,
device=device,
dtype=torch.bool,
),
)
else:
init_val = _reset.clone()
parent_td = (
tensordict_reset
if isinstance(init_key, str)
else tensordict_reset.get(init_key[:-1])
)
if init_val.ndim == parent_td.ndim:
# unsqueeze, to match the done shape
init_val = init_val.unsqueeze(-1)
tensordict_reset.set(init_key, init_val)
return tensordict_reset
[docs] def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
full_done_spec = self.parent.output_spec["full_done_spec"]
for init_key in self.init_keys:
for done_key in self.parent.done_keys:
# check root
if type(done_key) != type(init_key):
continue
if isinstance(done_key, tuple):
if done_key[:-1] == init_key[:-1]:
shape = full_done_spec[done_key].shape
break
if isinstance(done_key, str):
shape = full_done_spec[done_key].shape
break
else:
raise KeyError(
f"Could not find root of init_key {init_key} within done_keys {self.parent.done_keys}."
)
observation_spec[init_key] = Categorical(
2,
dtype=torch.bool,
device=self.parent.device,
shape=shape,
)
return observation_spec
[docs] def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
raise NotImplementedError(
FORWARD_NOT_IMPLEMENTED.format(self.__class__.__name__)
)
[docs]class RenameTransform(Transform):
"""A transform to rename entries in the output tensordict (or input tensordict via the inverse keys).
Args:
in_keys (sequence of NestedKey): the entries to rename.
out_keys (sequence of NestedKey): the name of the entries after renaming.
in_keys_inv (sequence of NestedKey, optional): the entries to rename
in the input tensordict, which will be passed to :meth:`EnvBase._step`.
out_keys_inv (sequence of NestedKey, optional): the names of the entries
in the input tensordict after renaming.
create_copy (bool, optional): if ``True``, the entries will be copied
with a different name rather than being renamed. This allows for
renaming immutable entries such as ``"reward"`` and ``"done"``.
Examples:
>>> from torchrl.envs.libs.gym import GymEnv
>>> env = TransformedEnv(
... GymEnv("Pendulum-v1"),
... RenameTransform(["observation", ], ["stuff",], create_copy=False),
... )
>>> tensordict = env.rollout(3)
>>> print(tensordict)
TensorDict(
fields={
action: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False),
done: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False),
reward: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False),
stuff: Tensor(shape=torch.Size([3, 3]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([3]),
device=cpu,
is_shared=False),
stuff: Tensor(shape=torch.Size([3, 3]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([3]),
device=cpu,
is_shared=False)
>>> # if the output is also an input, we need to rename if both ways:
>>> from torchrl.envs.libs.brax import BraxEnv
>>> env = TransformedEnv(
... BraxEnv("fast"),
... RenameTransform(["state"], ["newname"], ["state"], ["newname"])
... )
>>> _ = env.set_seed(1)
>>> tensordict = env.rollout(3)
>>> assert "newname" in tensordict.keys()
>>> assert "state" not in tensordict.keys()
"""
def __init__(
self, in_keys, out_keys, in_keys_inv=None, out_keys_inv=None, create_copy=False
):
if in_keys_inv is None:
in_keys_inv = []
if out_keys_inv is None:
out_keys_inv = copy(in_keys_inv)
self.create_copy = create_copy
super().__init__(in_keys, out_keys, in_keys_inv, out_keys_inv)
if len(self.in_keys) != len(self.out_keys):
raise ValueError(
f"The number of in_keys ({len(self.in_keys)}) should match the number of out_keys ({len(self.out_keys)})."
)
if len(self.in_keys_inv) != len(self.out_keys_inv):
raise ValueError(
f"The number of in_keys_inv ({len(self.in_keys_inv)}) should match the number of out_keys_inv ({len(self.out_keys)})."
)
if len(set(out_keys).intersection(in_keys)):
raise ValueError(
f"Cannot have matching in and out_keys because order is unclear. "
f"Please use separated transforms. "
f"Got in_keys={in_keys} and out_keys={out_keys}."
)
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
if self.create_copy:
out = tensordict.select(*self.in_keys, strict=not self._missing_tolerance)
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys):
try:
out.rename_key_(in_key, out_key)
except KeyError:
if not self._missing_tolerance:
raise
tensordict = tensordict.update(out)
else:
for in_key, out_key in _zip_strict(self.in_keys, self.out_keys):
try:
tensordict.rename_key_(in_key, out_key)
except KeyError:
if not self._missing_tolerance:
raise
return tensordict
forward = _call
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
with _set_missing_tolerance(self, True):
return self._call(tensordict_reset)
def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase:
# no in-place modif
if self.create_copy:
out = tensordict.select(
*self.out_keys_inv, strict=not self._missing_tolerance
)
for in_key, out_key in _zip_strict(self.in_keys_inv, self.out_keys_inv):
try:
out.rename_key_(out_key, in_key)
except KeyError:
if not self._missing_tolerance:
raise
tensordict = tensordict.update(out)
else:
for in_key, out_key in _zip_strict(self.in_keys_inv, self.out_keys_inv):
try:
tensordict.rename_key_(out_key, in_key)
except KeyError:
if not self._missing_tolerance:
raise
return tensordict
[docs] def transform_output_spec(self, output_spec: Composite) -> Composite:
for done_key in self.parent.done_keys:
if done_key in self.in_keys:
for i, out_key in enumerate(self.out_keys): # noqa: B007
if self.in_keys[i] == done_key:
break
else:
# unreachable
raise RuntimeError
output_spec["full_done_spec"][out_key] = output_spec["full_done_spec"][
done_key
].clone()
if not self.create_copy:
del output_spec["full_done_spec"][done_key]
for reward_key in self.parent.reward_keys:
if reward_key in self.in_keys:
for i, out_key in enumerate(self.out_keys): # noqa: B007
if self.in_keys[i] == reward_key:
break
else:
# unreachable
raise RuntimeError
output_spec["full_reward_spec"][out_key] = output_spec[
"full_reward_spec"
][reward_key].clone()
if not self.create_copy:
del output_spec["full_reward_spec"][reward_key]
for observation_key in self.parent.full_observation_spec.keys(True):
if observation_key in self.in_keys:
for i, out_key in enumerate(self.out_keys): # noqa: B007
if self.in_keys[i] == observation_key:
break
else:
# unreachable
raise RuntimeError
output_spec["full_observation_spec"][out_key] = output_spec[
"full_observation_spec"
][observation_key].clone()
if not self.create_copy:
del output_spec["full_observation_spec"][observation_key]
return output_spec
[docs] def transform_input_spec(self, input_spec: Composite) -> Composite:
for action_key in self.parent.action_keys:
if action_key in self.in_keys_inv:
for i, out_key in enumerate(self.out_keys_inv): # noqa: B007
if self.in_keys_inv[i] == action_key:
break
else:
# unreachable
raise RuntimeError
input_spec["full_action_spec"][out_key] = input_spec[
"full_action_spec"
][action_key].clone()
if not self.create_copy:
del input_spec["full_action_spec"][action_key]
for state_key in self.parent.full_state_spec.keys(True):
if state_key in self.in_keys_inv:
for i, out_key in enumerate(self.out_keys_inv): # noqa: B007
if self.in_keys_inv[i] == state_key:
break
else:
# unreachable
raise RuntimeError
input_spec["full_state_spec"][out_key] = input_spec["full_state_spec"][
state_key
].clone()
if not self.create_copy:
del input_spec["full_state_spec"][state_key]
return input_spec
[docs]class Reward2GoTransform(Transform):
"""Calculates the reward to go based on the episode reward and a discount factor.
As the :class:`~.Reward2GoTransform` is only an inverse transform the ``in_keys`` will be directly used for the ``in_keys_inv``.
The reward-to-go can be only calculated once the episode is finished. Therefore, the transform should be applied to the replay buffer
and not to the collector or within an environment.
Args:
gamma (float or torch.Tensor): the discount factor. Defaults to 1.0.
in_keys (sequence of NestedKey): the entries to rename. Defaults to
``("next", "reward")`` if none is provided.
out_keys (sequence of NestedKey): the entries to rename. Defaults to
the values of ``in_keys`` if none is provided.
done_key (NestedKey): the done entry. Defaults to ``"done"``.
truncated_key (NestedKey): the truncated entry. Defaults to ``"truncated"``.
If no truncated entry is found, only the ``"done"`` will be used.
Examples:
>>> # Using this transform as part of a replay buffer
>>> from torchrl.data import ReplayBuffer, LazyTensorStorage
>>> torch.manual_seed(0)
>>> r2g = Reward2GoTransform(gamma=0.99, out_keys=["reward_to_go"])
>>> rb = ReplayBuffer(storage=LazyTensorStorage(100), transform=r2g)
>>> batch, timesteps = 4, 5
>>> done = torch.zeros(batch, timesteps, 1, dtype=torch.bool)
>>> for i in range(batch):
... while not done[i].any():
... done[i] = done[i].bernoulli_(0.1)
>>> reward = torch.ones(batch, timesteps, 1)
>>> td = TensorDict(
... {"next": {"done": done, "reward": reward}},
... [batch, timesteps],
... )
>>> rb.extend(td)
>>> sample = rb.sample(1)
>>> print(sample["next", "reward"])
tensor([[[1.],
[1.],
[1.],
[1.],
[1.]]])
>>> print(sample["reward_to_go"])
tensor([[[4.9010],
[3.9404],
[2.9701],
[1.9900],
[1.0000]]])
One can also use this transform directly with a collector: make sure to
append the `inv` method of the transform.
Examples:
>>> from torchrl.envs.utils import RandomPolicy >>> from torchrl.collectors import SyncDataCollector
>>> from torchrl.envs.libs.gym import GymEnv
>>> t = Reward2GoTransform(gamma=0.99, out_keys=["reward_to_go"])
>>> env = GymEnv("Pendulum-v1")
>>> collector = SyncDataCollector(
... env,
... RandomPolicy(env.action_spec),
... frames_per_batch=200,
... total_frames=-1,
... postproc=t.inv
... )
>>> for data in collector:
... break
>>> print(data)
TensorDict(
fields={
action: Tensor(shape=torch.Size([200, 1]), device=cpu, dtype=torch.float32, is_shared=False),
collector: TensorDict(
fields={
traj_ids: Tensor(shape=torch.Size([200]), device=cpu, dtype=torch.int64, is_shared=False)},
batch_size=torch.Size([200]),
device=cpu,
is_shared=False),
done: Tensor(shape=torch.Size([200, 1]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([200, 1]), device=cpu, dtype=torch.bool, is_shared=False),
observation: Tensor(shape=torch.Size([200, 3]), device=cpu, dtype=torch.float32, is_shared=False),
reward: Tensor(shape=torch.Size([200, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([200]),
device=cpu,
is_shared=False),
observation: Tensor(shape=torch.Size([200, 3]), device=cpu, dtype=torch.float32, is_shared=False),
reward: Tensor(shape=torch.Size([200, 1]), device=cpu, dtype=torch.float32, is_shared=False),
reward_to_go: Tensor(shape=torch.Size([200, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([200]),
device=cpu,
is_shared=False)
Using this transform as part of an env will raise an exception
Examples:
>>> t = Reward2GoTransform(gamma=0.99)
>>> TransformedEnv(GymEnv("Pendulum-v1"), t) # crashes
.. note:: In settings where multiple done entries are present, one should build
a single :class:`~Reward2GoTransform` for each done-reward pair.
"""
ENV_ERR = (
"The Reward2GoTransform is only an inverse transform and can "
"only be applied to the replay buffer."
)
def __init__(
self,
gamma: Optional[Union[float, torch.Tensor]] = 1.0,
in_keys: Sequence[NestedKey] | None = None,
out_keys: Sequence[NestedKey] | None = None,
done_key: Optional[NestedKey] = "done",
):
if in_keys is None:
in_keys = [("next", "reward")]
if out_keys is None:
out_keys = copy(in_keys)
# out_keys = ["reward_to_go"]
super().__init__(
in_keys=in_keys,
in_keys_inv=in_keys,
out_keys_inv=out_keys,
)
self.done_key = done_key
if not isinstance(gamma, torch.Tensor):
gamma = torch.tensor(gamma)
self.register_buffer("gamma", gamma)
def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase:
if self.parent is not None:
raise ValueError(self.ENV_ERR)
done = tensordict.get(("next", self.done_key))
if not done.any(-2).all():
raise RuntimeError(
"No episode ends found to calculate the reward to go. Make sure that the number of frames_per_batch is larger than number of steps per episode."
)
found = False
for in_key, out_key in _zip_strict(self.in_keys_inv, self.out_keys_inv):
if in_key in tensordict.keys(include_nested=True):
found = True
item = self._inv_apply_transform(tensordict.get(in_key), done)
tensordict.set(out_key, item)
if not found:
raise KeyError(f"Could not find any of the input keys {self.in_keys}.")
return tensordict
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
raise ValueError(self.ENV_ERR)
def _inv_apply_transform(
self, reward: torch.Tensor, done: torch.Tensor
) -> torch.Tensor:
return reward2go(reward, done, self.gamma)
def set_container(self, container):
if isinstance(container, EnvBase) or container.parent is not None:
raise ValueError(self.ENV_ERR)
[docs]class ActionMask(Transform):
"""An adaptive action masker.
This transform reads the mask from the input tensordict after the step is executed,
and adapts the mask of the one-hot / categorical action spec.
.. note:: This transform will fail when used without an environment.
Args:
action_key (NestedKey, optional): the key where the action tensor can be found.
Defaults to ``"action"``.
mask_key (NestedKey, optional): the key where the action mask can be found.
Defaults to ``"action_mask"``.
Examples:
>>> import torch
>>> from torchrl.data.tensor_specs import Categorical, Binary, Unbounded, Composite
>>> from torchrl.envs.transforms import ActionMask, TransformedEnv
>>> from torchrl.envs.common import EnvBase
>>> class MaskedEnv(EnvBase):
... def __init__(self, *args, **kwargs):
... super().__init__(*args, **kwargs)
... self.action_spec = Categorical(4)
... self.state_spec = Composite(action_mask=Binary(4, dtype=torch.bool))
... self.observation_spec = Composite(obs=Unbounded(3))
... self.reward_spec = Unbounded(1)
...
... def _reset(self, tensordict=None):
... td = self.observation_spec.rand()
... td.update(torch.ones_like(self.state_spec.rand()))
... return td
...
... def _step(self, data):
... td = self.observation_spec.rand()
... mask = data.get("action_mask")
... action = data.get("action")
... mask = mask.scatter(-1, action.unsqueeze(-1), 0)
...
... td.set("action_mask", mask)
... td.set("reward", self.reward_spec.rand())
... td.set("done", ~mask.any().view(1))
... return td
...
... def _set_seed(self, seed):
... return seed
...
>>> torch.manual_seed(0)
>>> base_env = MaskedEnv()
>>> env = TransformedEnv(base_env, ActionMask())
>>> r = env.rollout(10)
>>> env = TransformedEnv(base_env, ActionMask())
>>> r = env.rollout(10)
>>> r["action_mask"]
tensor([[ True, True, True, True],
[ True, True, False, True],
[ True, True, False, False],
[ True, False, False, False]])
"""
ACCEPTED_SPECS = (
OneHot,
Categorical,
MultiOneHot,
MultiCategorical,
)
SPEC_TYPE_ERROR = "The action spec must be one of {}. Got {} instead."
def __init__(
self, action_key: NestedKey = "action", mask_key: NestedKey = "action_mask"
):
if not isinstance(action_key, (tuple, str)):
raise ValueError(
f"The action key must be a nested key. Got {type(action_key)} instead."
)
if not isinstance(mask_key, (tuple, str)):
raise ValueError(
f"The mask key must be a nested key. Got {type(mask_key)} instead."
)
super().__init__(
in_keys=[action_key, mask_key], out_keys=[], in_keys_inv=[], out_keys_inv=[]
)
[docs] def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
raise RuntimeError(FORWARD_NOT_IMPLEMENTED.format(type(self)))
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
parent = self.parent
if parent is None:
raise RuntimeError(
f"{type(self)}.parent cannot be None: make sure this transform is executed within an environment."
)
mask = tensordict.get(self.in_keys[1])
action_spec = self.container.action_spec
if not isinstance(action_spec, self.ACCEPTED_SPECS):
raise ValueError(
self.SPEC_TYPE_ERROR.format(self.ACCEPTED_SPECS, type(action_spec))
)
action_spec.update_mask(mask.to(action_spec.device))
return tensordict
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
action_spec = self.container.action_spec
if not isinstance(action_spec, self.ACCEPTED_SPECS):
raise ValueError(
self.SPEC_TYPE_ERROR.format(self.ACCEPTED_SPECS, type(action_spec))
)
mask = tensordict.get(self.in_keys[1], None)
if mask is not None:
mask = mask.to(action_spec.device)
action_spec.update_mask(mask)
# TODO: Check that this makes sense
with _set_missing_tolerance(self, True):
tensordict_reset = self._call(tensordict_reset)
return tensordict_reset
[docs]class VecGymEnvTransform(Transform):
"""A transform for GymWrapper subclasses that handles the auto-reset in a consistent way.
Gym, gymnasium and SB3 provide vectorized (read, parallel or batched) environments
that are automatically reset. When this occurs, the actual observation resulting
from the action is saved within a key in the info.
The class :class:`torchrl.envs.libs.gym.terminal_obs_reader` reads that observation
and stores it in a ``"final"`` key within the output tensordict.
In turn, this transform reads that final data, swaps it with the observation
written in its place that results from the actual reset, and saves the
reset output in a private container. The resulting data truly reflects
the output of the step.
This class works from gym 0.13 till the most recent gymnasium version.
.. note:: Gym versions < 0.22 did not return the final observations. For these,
we simply fill the next observations with NaN (because it is lost) and
do the swap at the next step.
Then, when calling `env.reset`, the saved data is written back where it belongs
(and the `reset` is a no-op).
This transform is automatically appended to the gym env whenever the wrapper
is created with an async env.
Args:
final_name (str, optional): the name of the final observation in the dict.
Defaults to `"final"`.
.. note:: In general, this class should not be handled directly. It is
created whenever a vectorized environment is placed within a :class:`GymWrapper`.
"""
def __init__(self, final_name="final"):
self.final_name = final_name
super().__init__()
self._memo = {}
def set_container(self, container: Union[Transform, EnvBase]) -> None:
out = super().set_container(container)
self._done_keys = None
self._obs_keys = None
return out
def _step(
self, tensordict: TensorDictBase, next_tensordict: TensorDictBase
) -> TensorDictBase:
# save the final info
done = False
for done_key in self.done_keys:
# we assume dones can be broadcast
done = done | next_tensordict.get(done_key)
if done is False:
raise RuntimeError(
f"Could not find any done signal in tensordict:\n{tensordict}"
)
self._memo["done"] = done
final = next_tensordict.pop(self.final_name, None)
# if anything's done, we need to swap the final obs
if done.any():
done = done.squeeze(-1)
if final is not None:
saved_next = next_tensordict.select(*final.keys(True, True)).clone()
next_tensordict[done] = final[done]
else:
saved_next = next_tensordict.select(*self.obs_keys).clone()
for obs_key in self.obs_keys:
next_tensordict[obs_key][done] = torch.tensor(np.nan)
self._memo["saved_next"] = saved_next
else:
self._memo["saved_next"] = None
return next_tensordict
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
done = self._memo.get("done", None)
reset = tensordict.get("_reset", done)
if done is not None:
done = done.view_as(reset)
if (
reset is not done
and (reset != done).any()
# it can happen that all are reset, in which case
# it's fine (doesn't need to match done)
and not reset.all()
):
raise RuntimeError(
"Cannot partially reset a gym(nasium) async env with a "
"reset mask that does not match the done mask. "
f"Got reset={reset}\nand done={done}"
)
# if not reset.any(), we don't need to do anything.
# if reset.all(), we don't either (bc GymWrapper will call a plain reset).
if reset is not None and reset.any():
if reset.all():
# We're fine: this means that a full reset was passed and the
# env was manually reset
tensordict_reset.pop(self.final_name, None)
return tensordict_reset
saved_next = self._memo["saved_next"]
if saved_next is None:
raise RuntimeError(
"Did not find a saved tensordict while the reset mask was "
f"not empty: reset={reset}. Done was {done}."
)
# reset = reset.view(tensordict.shape)
# we have a data container from the previous call to step
# that contains part of the observation we need.
# We can safely place them back in the reset result tensordict:
# in env.rollout(), the result of reset() is assumed to be just
# the td from previous step with updated values from reset.
# In our case, it will always be the case that all these values
# are properly set.
# collectors even take care of doing an extra masking so it's even
# safer.
tensordict_reset.update(saved_next)
for done_key in self.done_keys:
# Make sure that all done are False
done = tensordict.get(done_key, None)
if done is not None:
done = done.clone().fill_(0)
else:
done = torch.zeros(
(*tensordict.batch_size, 1),
device=tensordict.device,
dtype=torch.bool,
)
tensordict.set(done_key, done)
tensordict_reset.pop(self.final_name, None)
return tensordict_reset
@property
def done_keys(self) -> List[NestedKey]:
keys = self.__dict__.get("_done_keys", None)
if keys is None:
keys = self.parent.done_keys
# we just want the "done" key
_done_keys = []
for key in keys:
if not isinstance(key, tuple):
key = (key,)
if key[-1] == "done":
_done_keys.append(unravel_key(key))
if not len(_done_keys):
raise RuntimeError("Could not find a 'done' key in the env specs.")
self._done_keys = _done_keys
return keys
@property
def obs_keys(self) -> List[NestedKey]:
keys = self.__dict__.get("_obs_keys", None)
if keys is None:
keys = list(self.parent.observation_spec.keys(True, True))
self._obs_keys = keys
return keys
[docs] def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
if self.final_name in observation_spec.keys(True):
del observation_spec[self.final_name]
return observation_spec
[docs] def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
raise RuntimeError(FORWARD_NOT_IMPLEMENTED.format(type(self)))
[docs]class BurnInTransform(Transform):
"""Transform to partially burn-in data sequences.
This transform is useful to obtain up-to-date recurrent states when
they are not available. It burns-in a number of steps along the time dimension
from sampled sequential data slices and returs the remaining data sequence with
the burnt-in data in its initial time step. This transform is intended to be used as a
replay buffer transform, not as an environment transform.
Args:
modules (sequence of TensorDictModule): A list of modules used to burn-in data sequences.
burn_in (int): The number of time steps to burn in.
out_keys (sequence of NestedKey, optional): destination keys. Defaults to
all the modules `out_keys` that point to the next time step (e.g. `"hidden"` if `
("next", "hidden")` is part of the `out_keys` of a module).
.. note::
This transform expects as inputs TensorDicts with its last dimension being the
time dimension. It also assumes that all provided modules can process
sequential data.
Examples:
>>> import torch
>>> from tensordict import TensorDict
>>> from torchrl.envs.transforms import BurnInTransform
>>> from torchrl.modules import GRUModule
>>> gru_module = GRUModule(
... input_size=10,
... hidden_size=10,
... in_keys=["observation", "hidden"],
... out_keys=["intermediate", ("next", "hidden")],
... default_recurrent_mode=True,
... )
>>> burn_in_transform = BurnInTransform(
... modules=[gru_module],
... burn_in=5,
... )
>>> td = TensorDict({
... "observation": torch.randn(2, 10, 10),
... "hidden": torch.randn(2, 10, gru_module.gru.num_layers, 10),
... "is_init": torch.zeros(2, 10, 1),
... }, batch_size=[2, 10])
>>> td = burn_in_transform(td)
>>> td.shape
torch.Size([2, 5])
>>> td.get("hidden").abs().sum()
tensor(86.3008)
>>> from torchrl.data import LazyMemmapStorage, TensorDictReplayBuffer
>>> buffer = TensorDictReplayBuffer(
... storage=LazyMemmapStorage(2),
... batch_size=1,
... )
>>> buffer.append_transform(burn_in_transform)
>>> td = TensorDict({
... "observation": torch.randn(2, 10, 10),
... "hidden": torch.randn(2, 10, gru_module.gru.num_layers, 10),
... "is_init": torch.zeros(2, 10, 1),
... }, batch_size=[2, 10])
>>> buffer.extend(td)
>>> td = buffer.sample(1)
>>> td.shape
torch.Size([1, 5])
>>> td.get("hidden").abs().sum()
tensor(37.0344)
"""
invertible = False
def __init__(
self,
modules: Sequence[TensorDictModuleBase],
burn_in: int,
out_keys: Sequence[NestedKey] | None = None,
):
if not isinstance(modules, Sequence):
modules = [modules]
for module in modules:
if not isinstance(module, TensorDictModuleBase):
raise ValueError(
f"All modules must be TensorDictModules, but a {type(module)} was provided."
)
in_keys = set()
for module in modules:
in_keys.update(module.in_keys)
if out_keys is None:
out_keys = set()
for module in modules:
for key in module.out_keys:
if key[0] == "next":
out_keys.add(key[1])
else:
out_keys_ = set()
for key in out_keys:
if isinstance(key, tuple) and key[0] == "next":
key = key[1]
warnings.warn(
f"The 'next' key is not needed in the BurnInTransform `out_key` {key} and "
f"will be ignored. This transform already assumes that `out_keys` will be "
f"retrieved from the next time step of the burnt-in data."
)
out_keys_.add(key)
out_keys = out_keys_
super().__init__(in_keys=in_keys, out_keys=out_keys)
self.modules = modules
self.burn_in = burn_in
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
raise RuntimeError("BurnInTransform can only be appended to a ReplayBuffer")
def _step(
self, tensordict: TensorDictBase, next_tensordict: TensorDictBase
) -> TensorDictBase:
raise RuntimeError("BurnInTransform can only be appended to a ReplayBuffer.")
[docs] def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
if self.burn_in == 0:
return tensordict
td_device = tensordict.device
B, T, *extra_dims = tensordict.batch_size
# Split the tensor dict into burn-in data and the rest.
td_burn_in = tensordict[..., : self.burn_in]
td_out = tensordict[..., self.burn_in :]
# Burn in the recurrent state.
with torch.no_grad():
for module in self.modules:
module_device = next(module.parameters()).device or None
td_burn_in = td_burn_in.to(module_device)
td_burn_in = module(td_burn_in)
td_burn_in = td_burn_in.to(td_device)
# Update out TensorDict with the burnt-in data.
for out_key in self.out_keys:
if out_key not in td_out.keys(include_nested=True):
td_out.set(
out_key,
torch.zeros(
B, T - self.burn_in, *tensordict.get(out_key).shape[2:]
),
)
td_out[..., 0][out_key].copy_(td_burn_in["next"][..., -1][out_key])
return td_out
def __repr__(self) -> str:
return f"{self.__class__.__name__}(burn_in={self.burn_in}, in_keys={self.in_keys}, out_keys={self.out_keys})"
[docs]class SignTransform(Transform):
"""A transform to compute the signs of TensorDict values.
This transform reads the tensors in ``in_keys`` and ``in_keys_inv``, computes the
signs of their elements and writes the resulting sign tensors to ``out_keys`` and
``out_keys_inv`` respectively.
Args:
in_keys (list of NestedKeys): input entries (read)
out_keys (list of NestedKeys): input entries (write)
in_keys_inv (list of NestedKeys): input entries (read) during :meth:`~.inv` calls.
out_keys_inv (list of NestedKeys): input entries (write) during :meth:`~.inv` calls.
Examples:
>>> from torchrl.envs import GymEnv, TransformedEnv, SignTransform
>>> base_env = GymEnv("Pendulum-v1")
>>> env = TransformedEnv(base_env, SignTransform(in_keys=['observation']))
>>> r = env.rollout(100)
>>> obs = r["observation"]
>>> assert (torch.logical_or(torch.logical_or(obs == -1, obs == 1), obs == 0.0)).all()
"""
def __init__(
self,
in_keys=None,
out_keys=None,
in_keys_inv=None,
out_keys_inv=None,
):
if in_keys is None:
in_keys = []
if out_keys is None:
out_keys = copy(in_keys)
if in_keys_inv is None:
in_keys_inv = []
if out_keys_inv is None:
out_keys_inv = copy(in_keys_inv)
super().__init__(in_keys, out_keys, in_keys_inv, out_keys_inv)
def _apply_transform(self, obs: torch.Tensor) -> torch.Tensor:
return obs.sign()
def _inv_apply_transform(self, state: torch.Tensor) -> torch.Tensor:
return state.sign()
[docs] @_apply_to_composite
def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
return Bounded(
shape=observation_spec.shape,
device=observation_spec.device,
dtype=observation_spec.dtype,
high=1.0,
low=-1.0,
)
[docs] def transform_reward_spec(self, reward_spec: TensorSpec) -> TensorSpec:
for key in self.in_keys:
if key in self.parent.reward_keys:
spec = self.parent.output_spec["full_reward_spec"][key]
self.parent.output_spec["full_reward_spec"][key] = Bounded(
shape=spec.shape,
device=spec.device,
dtype=spec.dtype,
high=1.0,
low=-1.0,
)
return self.parent.output_spec["full_reward_spec"]
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
with _set_missing_tolerance(self, True):
tensordict_reset = self._call(tensordict_reset)
return tensordict_reset
[docs]class RemoveEmptySpecs(Transform):
"""Removes empty specs and content from an environment.
Examples:
>>> import torch
>>> from tensordict import TensorDict
>>> from torchrl.data import Unbounded, Composite, \
... Categorical
>>> from torchrl.envs import EnvBase, TransformedEnv, RemoveEmptySpecs
>>>
>>>
>>> class DummyEnv(EnvBase):
... def __init__(self, *args, **kwargs):
... super().__init__(*args, **kwargs)
... self.observation_spec = Composite(
... observation=UnboundedContinuous((*self.batch_size, 3)),
... other=Composite(
... another_other=Composite(shape=self.batch_size),
... shape=self.batch_size,
... ),
... shape=self.batch_size,
... )
... self.action_spec = UnboundedContinuous((*self.batch_size, 3))
... self.done_spec = Categorical(
... 2, (*self.batch_size, 1), dtype=torch.bool
... )
... self.full_done_spec["truncated"] = self.full_done_spec[
... "terminated"].clone()
... self.reward_spec = Composite(
... reward=UnboundedContinuous(*self.batch_size, 1),
... other_reward=Composite(shape=self.batch_size),
... shape=self.batch_size
... )
...
... def _reset(self, tensordict):
... return self.observation_spec.rand().update(self.full_done_spec.zero())
...
... def _step(self, tensordict):
... return TensorDict(
... {},
... batch_size=[]
... ).update(self.observation_spec.rand()).update(
... self.full_done_spec.zero()
... ).update(self.full_reward_spec.rand())
...
... def _set_seed(self, seed):
... return seed + 1
>>>
>>>
>>> base_env = DummyEnv()
>>> print(base_env.rollout(2))
TensorDict(
fields={
action: Tensor(shape=torch.Size([2, 3]), device=cpu, dtype=torch.float32, is_shared=False),
done: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
observation: Tensor(shape=torch.Size([2, 3]), device=cpu, dtype=torch.float32, is_shared=False),
other: TensorDict(
fields={
another_other: TensorDict(
fields={
},
batch_size=torch.Size([2]),
device=cpu,
is_shared=False)},
batch_size=torch.Size([2]),
device=cpu,
is_shared=False),
other_reward: TensorDict(
fields={
},
batch_size=torch.Size([2]),
device=cpu,
is_shared=False),
reward: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.float32, is_shared=False),
terminated: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
truncated: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
batch_size=torch.Size([2]),
device=cpu,
is_shared=False),
observation: Tensor(shape=torch.Size([2, 3]), device=cpu, dtype=torch.float32, is_shared=False),
terminated: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
truncated: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
batch_size=torch.Size([2]),
device=cpu,
is_shared=False)
>>> check_env_specs(base_env)
>>> env = TransformedEnv(base_env, RemoveEmptySpecs())
>>> print(env.rollout(2))
TensorDict(
fields={
action: Tensor(shape=torch.Size([2, 3]), device=cpu, dtype=torch.float32, is_shared=False),
done: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
observation: Tensor(shape=torch.Size([2, 3]), device=cpu, dtype=torch.float32, is_shared=False),
reward: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.float32, is_shared=False),
terminated: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
truncated: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
batch_size=torch.Size([2]),
device=cpu,
is_shared=False),
observation: Tensor(shape=torch.Size([2, 3]), device=cpu, dtype=torch.float32, is_shared=False),
terminated: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
truncated: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
batch_size=torch.Size([2]),
device=cpu,
is_shared=False)
check_env_specs(env)
"""
_has_empty_input = True
@staticmethod
def _sorter(key_val):
key, _ = key_val
if isinstance(key, str):
return 0
return len(key)
[docs] def transform_output_spec(self, output_spec: Composite) -> Composite:
full_done_spec = output_spec["full_done_spec"]
full_reward_spec = output_spec["full_reward_spec"]
full_observation_spec = output_spec["full_observation_spec"]
# we reverse things to make sure we delete things from the back
for key, spec in sorted(
full_done_spec.items(True), key=self._sorter, reverse=True
):
if isinstance(spec, Composite) and spec.is_empty():
del full_done_spec[key]
for key, spec in sorted(
full_observation_spec.items(True), key=self._sorter, reverse=True
):
if isinstance(spec, Composite) and spec.is_empty():
del full_observation_spec[key]
for key, spec in sorted(
full_reward_spec.items(True), key=self._sorter, reverse=True
):
if isinstance(spec, Composite) and spec.is_empty():
del full_reward_spec[key]
return output_spec
[docs] def transform_input_spec(self, input_spec: TensorSpec) -> TensorSpec:
full_action_spec = input_spec["full_action_spec"]
full_state_spec = input_spec["full_state_spec"]
# we reverse things to make sure we delete things from the back
self._has_empty_input = False
for key, spec in sorted(
full_action_spec.items(True), key=self._sorter, reverse=True
):
if isinstance(spec, Composite) and spec.is_empty():
self._has_empty_input = True
del full_action_spec[key]
for key, spec in sorted(
full_state_spec.items(True), key=self._sorter, reverse=True
):
if isinstance(spec, Composite) and spec.is_empty():
self._has_empty_input = True
del full_state_spec[key]
return input_spec
def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase:
if self._has_empty_input:
input_spec = getattr(self.parent, "input_spec", None)
if input_spec is None:
return tensordict
full_action_spec = input_spec["full_action_spec"]
full_state_spec = input_spec["full_state_spec"]
# we reverse things to make sure we delete things from the back
for key, spec in sorted(
full_action_spec.items(True), key=self._sorter, reverse=True
):
if (
isinstance(spec, Composite)
and spec.is_empty()
and key not in tensordict.keys(True)
):
tensordict.create_nested(key)
for key, spec in sorted(
full_state_spec.items(True), key=self._sorter, reverse=True
):
if (
isinstance(spec, Composite)
and spec.is_empty()
and key not in tensordict.keys(True)
):
tensordict.create_nested(key)
return tensordict
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
for key, value in sorted(
tensordict.items(True), key=self._sorter, reverse=True
):
if (
is_tensor_collection(value)
and not isinstance(value, NonTensorData)
and value.is_empty()
):
del tensordict[key]
return tensordict
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
"""Resets a transform if it is stateful."""
return self._call(tensordict_reset)
forward = _call
class _InvertTransform(Transform):
_MISSING_TRANSFORM_ERROR = (
"There is not generic rule to invert a spec transform. "
"Please file an issue on github to get help."
)
def __init__(self, transform: Transform):
super().__init__()
self.transform = transform
@property
def in_keys(self):
return self.transform.in_keys_inv
@in_keys.setter
def in_keys(self, value):
if value is not None:
raise RuntimeError("Cannot set non-null value in in_keys.")
@property
def in_keys_inv(self):
return self.transform.in_keys
@in_keys_inv.setter
def in_keys_inv(self, value):
if value is not None:
raise RuntimeError("Cannot set non-null value in in_keys_inv.")
@property
def out_keys(self):
return self.transform.out_keys_inv
@out_keys.setter
def out_keys(self, value):
if value is not None:
raise RuntimeError("Cannot set non-null value in out_keys.")
@property
def out_keys_inv(self):
return self.transform.out_keys
@out_keys_inv.setter
def out_keys_inv(self, value):
if value is not None:
raise RuntimeError("Cannot set non-null value in out_keys_inv.")
def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
return self.transform.inv(tensordict)
def inv(self, tensordict: TensorDictBase) -> TensorDictBase:
return self.transform.forward(tensordict)
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
return self.transform._inv_call(tensordict)
def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase:
return self.transform._call(tensordict)
def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec:
raise RuntimeError(self._MISSING_TRANSFORM_ERROR)
def transform_state_spec(self, state_spec: TensorSpec) -> TensorSpec:
raise RuntimeError(self._MISSING_TRANSFORM_ERROR)
def transform_reward_spec(self, reward_spec: TensorSpec) -> TensorSpec:
raise RuntimeError(self._MISSING_TRANSFORM_ERROR)
def transform_action_spec(self, action_spec: TensorSpec) -> TensorSpec:
raise RuntimeError(self._MISSING_TRANSFORM_ERROR)
def transform_done_spec(self, done_spec: TensorSpec) -> TensorSpec:
raise RuntimeError(self._MISSING_TRANSFORM_ERROR)
class _CallableTransform(Transform):
# A wrapper around a custom callable to make it possible to transform any data type
def __init__(self, func):
super().__init__()
self.func = func
def forward(self, *args, **kwargs):
return self.func(*args, **kwargs)
def _call(self, tensordict: TensorDictBase):
return self.func(tensordict)
def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase:
return tensordict
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
return self._call(tensordict_reset)
[docs]class BatchSizeTransform(Transform):
"""A transform to modify the batch-size of an environmt.
This transform has two distinct usages: it can be used to set the
batch-size for non-batch-locked (e.g. stateless) environments to
enable data collection using data collectors. It can also be used
to modify the batch-size of an environment (e.g. squeeze, unsqueeze or
reshape).
This transform modifies the environment batch-size to match the one provided.
It expects the parent environment batch-size to be expandable to the
provided one.
Keyword Args:
batch_size (torch.Size or equivalent, optional): the new batch-size of the environment.
Exclusive with ``reshape_fn``.
reshape_fn (callable, optional): a callable to modify the environment batch-size.
Exclusive with ``batch_size``.
.. note:: Currently, transformations involving
``reshape``, ``flatten``, ``unflatten``, ``squeeze`` and ``unsqueeze``
are supported. If another reshape operation is required, please submit
a feature request on TorchRL github.
reset_func (callable, optional): a function that produces a reset tensordict.
The signature must match ``Callable[[TensorDictBase, TensorDictBase], TensorDictBase]``
where the first input argument is the optional tensordict passed to the
environment during the call to :meth:`~EnvBase.reset` and the second
is the output of ``TransformedEnv.base_env.reset``. It can also support an
optional ``env`` keyword argument if ``env_kwarg=True``.
env_kwarg (bool, optional): if ``True``, ``reset_func`` must support a
``env`` keyword argument. Defaults to ``False``. The env passed will
be the env accompanied by its transform.
Example:
>>> # Changing the batch-size with a function
>>> from torchrl.envs import GymEnv
>>> base_env = GymEnv("CartPole-v1")
>>> env = TransformedEnv(base_env, BatchSizeTransform(reshape_fn=lambda data: data.reshape(1, 1)))
>>> env.rollout(4)
>>> # Setting the shape of a stateless environment
>>> class MyEnv(EnvBase):
... batch_locked = False
... def __init__(self):
... super().__init__()
... self.observation_spec = Composite(observation=Unbounded(3))
... self.reward_spec = Unbounded(1)
... self.action_spec = Unbounded(1)
...
... def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase:
... tensordict_batch_size = tensordict.batch_size if tensordict is not None else torch.Size([])
... result = self.observation_spec.rand(tensordict_batch_size)
... result.update(self.full_done_spec.zero(tensordict_batch_size))
... return result
...
... def _step(
... self,
... tensordict: TensorDictBase,
... ) -> TensorDictBase:
... result = self.observation_spec.rand(tensordict.batch_size)
... result.update(self.full_done_spec.zero(tensordict.batch_size))
... result.update(self.full_reward_spec.zero(tensordict.batch_size))
... return result
...
... def _set_seed(self, seed: Optional[int]):
... pass
...
>>> env = TransformedEnv(MyEnv(), BatchSizeTransform([5]))
>>> assert env.batch_size == torch.Size([5])
>>> assert env.rollout(10).shape == torch.Size([5, 10])
The ``reset_func`` can create a tensordict with the desired batch-size, allowing for
a fine-grained reset call:
>>> def reset_func(tensordict, tensordict_reset, env):
... result = env.observation_spec.rand()
... result.update(env.full_done_spec.zero())
... assert result.batch_size != torch.Size([])
... return result
>>> env = TransformedEnv(MyEnv(), BatchSizeTransform([5], reset_func=reset_func, env_kwarg=True))
>>> print(env.rollout(2))
TensorDict(
fields={
action: Tensor(shape=torch.Size([5, 2, 1]), device=cpu, dtype=torch.float32, is_shared=False),
done: Tensor(shape=torch.Size([5, 2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([5, 2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
observation: Tensor(shape=torch.Size([5, 2, 3]), device=cpu, dtype=torch.float32, is_shared=False),
reward: Tensor(shape=torch.Size([5, 2, 1]), device=cpu, dtype=torch.float32, is_shared=False),
terminated: Tensor(shape=torch.Size([5, 2, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
batch_size=torch.Size([5, 2]),
device=None,
is_shared=False),
observation: Tensor(shape=torch.Size([5, 2, 3]), device=cpu, dtype=torch.float32, is_shared=False),
terminated: Tensor(shape=torch.Size([5, 2, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
batch_size=torch.Size([5, 2]),
device=None,
is_shared=False)
This transform can be used to deploy non-batch-locked environments within data
collectors:
>>> from torchrl.collectors import SyncDataCollector
>>> collector = SyncDataCollector(env, lambda td: env.rand_action(td), frames_per_batch=10, total_frames=-1)
>>> for data in collector:
... print(data)
... break
TensorDict(
fields={
action: Tensor(shape=torch.Size([5, 2, 1]), device=cpu, dtype=torch.float32, is_shared=False),
collector: TensorDict(
fields={
traj_ids: Tensor(shape=torch.Size([5, 2]), device=cpu, dtype=torch.int64, is_shared=False)},
batch_size=torch.Size([5, 2]),
device=None,
is_shared=False),
done: Tensor(shape=torch.Size([5, 2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([5, 2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
observation: Tensor(shape=torch.Size([5, 2, 3]), device=cpu, dtype=torch.float32, is_shared=False),
reward: Tensor(shape=torch.Size([5, 2, 1]), device=cpu, dtype=torch.float32, is_shared=False),
terminated: Tensor(shape=torch.Size([5, 2, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
batch_size=torch.Size([5, 2]),
device=None,
is_shared=False),
observation: Tensor(shape=torch.Size([5, 2, 3]), device=cpu, dtype=torch.float32, is_shared=False),
terminated: Tensor(shape=torch.Size([5, 2, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
batch_size=torch.Size([5, 2]),
device=None,
is_shared=False)
>>> collector.shutdown()
"""
_ENV_ERR = "BatchSizeTransform.{} requires a parent env."
def __init__(
self,
*,
batch_size: torch.Size | None = None,
reshape_fn: Callable[[TensorDictBase], TensorDictBase] | None = None,
reset_func: Callable[[TensorDictBase, TensorDictBase], TensorDictBase]
| None = None,
env_kwarg: bool = False,
):
super().__init__()
if not ((batch_size is None) ^ (reshape_fn is None)):
raise ValueError(
"One and only one of batch_size OR reshape_fn must be provided."
)
if batch_size is not None:
self.batch_size = torch.Size(batch_size)
self.reshape_fn = None
else:
self.reshape_fn = reshape_fn
self.batch_size = None
self.reshape_fn = reshape_fn
self.reset_func = reset_func
self.env_kwarg = env_kwarg
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
if self.reset_func is not None:
if self.env_kwarg:
tensordict_reset = self.reset_func(
tensordict, tensordict_reset, env=self.container
)
else:
tensordict_reset = self.reset_func(tensordict, tensordict_reset)
if self.batch_size is not None:
return tensordict_reset.expand(self.batch_size)
return self.reshape_fn(tensordict_reset)
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
if self.reshape_fn is not None:
tensordict = self.reshape_fn(tensordict)
return tensordict
forward = _call
def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase:
if self.reshape_fn is not None:
parent = self.parent
if parent is not None:
parent_batch_size = parent.batch_size
tensordict = tensordict.reshape(parent_batch_size)
return tensordict
[docs] def transform_env_batch_size(self, batch_size: torch.Size):
if self.batch_size is not None:
return self.batch_size
return self.reshape_fn(torch.zeros(batch_size, device="meta")).shape
[docs] def transform_output_spec(self, output_spec: Composite) -> Composite:
if self.batch_size is not None:
return output_spec.expand(self.batch_size)
return self.reshape_fn(output_spec)
[docs] def transform_input_spec(self, input_spec: Composite) -> Composite:
if self.batch_size is not None:
return input_spec.expand(self.batch_size)
return self.reshape_fn(input_spec)
[docs]class AutoResetEnv(TransformedEnv):
"""A subclass for auto-resetting envs."""
def _reset(self, tensordict: Optional[TensorDictBase] = None, **kwargs):
if tensordict is not None:
# We must avoid modifying the original tensordict so a shallow copy is necessary.
# We just select the input data and reset signal, which is all we need.
tensordict = tensordict.select(
*self.reset_keys, *self.state_spec.keys(True, True), strict=False
)
for reset_key in self.base_env.reset_keys:
if tensordict is not None and reset_key in tensordict.keys(True):
tensordict_reset = tensordict.exclude(*self.base_env.reset_keys)
else:
tensordict_reset = self.base_env._reset(tensordict, **kwargs)
break
if tensordict is None:
# make sure all transforms see a source tensordict
tensordict = tensordict_reset.empty()
self.base_env._complete_done(self.base_env.full_done_spec, tensordict_reset)
tensordict_reset = self.transform._reset(tensordict, tensordict_reset)
return tensordict_reset
[docs] def insert_transform(self, index: int, transform: Transform) -> None:
raise RuntimeError(f"Cannot insert a transform in {self.__class_.__name__}.")
[docs]class AutoResetTransform(Transform):
"""A transform for auto-resetting environments.
This transform can be appended to any auto-resetting environment, or automatically
appended using ``env = SomeEnvClass(..., auto_reset=True)``. If the transform is explicitly
appended to an env, a :class:`~torchrl.envs.transforms.AutoResetEnv` must be used.
An auto-reset environment must have the following properties (differences from this
description should be accounted for by subclassing this class):
- the reset function can be called once at the beginning (after instantiation) with
or without effect. Whether calls to `reset` are allowed after that is up to the
environment itself.
- During a rollout, any ``done`` state will result in a reset and produce an observation
that isn't the last observation of the current episode, but the first observation
of the next episode (this transform will extract and cache this observation
and fill the obs with some arbitrary value).
Keyword Args:
replace (bool, optional): if ``False``, values are just placed as they are in the
``"next"`` entry even if they are not valid. Defaults to ``True``. A value of
``False`` overrides any subsequent filling keyword argument.
This argumet can also be passed with the constructor method by passing a
``auto_reset_replace`` argument: ``env = FooEnv(..., auto_reset=True, auto_reset_replace=False)``.
fill_float (float or str, optional): The filling value for floating point tensors
that terminate an episode. A value of ``None`` means no replacement (values are just
placed as they are in the ``"next"`` entry even if they are not valid).
fill_int (int, optional): The filling value for signed integer tensors
that terminate an episode. A value of ``None`` means no replacement (values are just
placed as they are in the ``"next"`` entry even if they are not valid).
fill_bool (bool, optional): The filling value for boolean tensors
that terminate an episode. A value of ``None`` means no replacement (values are just
placed as they are in the ``"next"`` entry even if they are not valid).
Arguments are only available when the transform is explicitly instantiated (not through `EnvType(..., auto_reset=True)`).
Examples:
>>> from torchrl.envs import GymEnv
>>> from torchrl.envs import set_gym_backend
>>> import torch
>>> torch.manual_seed(0)
>>>
>>> class AutoResettingGymEnv(GymEnv):
... def _step(self, tensordict):
... tensordict = super()._step(tensordict)
... if tensordict["done"].any():
... td_reset = super().reset()
... tensordict.update(td_reset.exclude(*self.done_keys))
... return tensordict
...
... def _reset(self, tensordict=None):
... if tensordict is not None and "_reset" in tensordict:
... return tensordict.copy()
... return super()._reset(tensordict)
>>>
>>> with set_gym_backend("gym"):
... env = AutoResettingGymEnv("CartPole-v1", auto_reset=True, auto_reset_replace=True)
... env.set_seed(0)
... r = env.rollout(30, break_when_any_done=False)
>>> print(r["next", "done"].squeeze())
tensor([False, False, False, False, False, False, False, False, False, False,
False, False, False, True, False, False, False, False, False, False,
False, False, False, False, False, True, False, False, False, False])
>>> print("observation after reset are set as nan", r["next", "observation"])
observation after reset are set as nan tensor([[-4.3633e-02, -1.4877e-01, 1.2849e-02, 2.7584e-01],
[-4.6609e-02, 4.6166e-02, 1.8366e-02, -1.2761e-02],
[-4.5685e-02, 2.4102e-01, 1.8111e-02, -2.9959e-01],
[-4.0865e-02, 4.5644e-02, 1.2119e-02, -1.2542e-03],
[-3.9952e-02, 2.4059e-01, 1.2094e-02, -2.9009e-01],
[-3.5140e-02, 4.3554e-01, 6.2920e-03, -5.7893e-01],
[-2.6429e-02, 6.3057e-01, -5.2867e-03, -8.6963e-01],
[-1.3818e-02, 8.2576e-01, -2.2679e-02, -1.1640e+00],
[ 2.6972e-03, 1.0212e+00, -4.5959e-02, -1.4637e+00],
[ 2.3121e-02, 1.2168e+00, -7.5232e-02, -1.7704e+00],
[ 4.7457e-02, 1.4127e+00, -1.1064e-01, -2.0854e+00],
[ 7.5712e-02, 1.2189e+00, -1.5235e-01, -1.8289e+00],
[ 1.0009e-01, 1.0257e+00, -1.8893e-01, -1.5872e+00],
[ nan, nan, nan, nan],
[-3.9405e-02, -1.7766e-01, -1.0403e-02, 3.0626e-01],
[-4.2959e-02, -3.7263e-01, -4.2775e-03, 5.9564e-01],
[-5.0411e-02, -5.6769e-01, 7.6354e-03, 8.8698e-01],
[-6.1765e-02, -7.6292e-01, 2.5375e-02, 1.1820e+00],
[-7.7023e-02, -9.5836e-01, 4.9016e-02, 1.4826e+00],
[-9.6191e-02, -7.6387e-01, 7.8667e-02, 1.2056e+00],
[-1.1147e-01, -9.5991e-01, 1.0278e-01, 1.5219e+00],
[-1.3067e-01, -7.6617e-01, 1.3322e-01, 1.2629e+00],
[-1.4599e-01, -5.7298e-01, 1.5848e-01, 1.0148e+00],
[-1.5745e-01, -7.6982e-01, 1.7877e-01, 1.3527e+00],
[-1.7285e-01, -9.6668e-01, 2.0583e-01, 1.6956e+00],
[ nan, nan, nan, nan],
[-4.3962e-02, 1.9845e-01, -4.5015e-02, -2.5903e-01],
[-3.9993e-02, 3.9418e-01, -5.0196e-02, -5.6557e-01],
[-3.2109e-02, 5.8997e-01, -6.1507e-02, -8.7363e-01],
[-2.0310e-02, 3.9574e-01, -7.8980e-02, -6.0090e-01]])
"""
def __init__(
self,
*,
replace: bool | None = None,
fill_float="nan",
fill_int=-1,
fill_bool=False,
):
super().__init__()
if replace is False:
fill_float = fill_int = fill_bool = None
if fill_float == "nan":
fill_float = float("nan")
self.fill_float = fill_float
self.fill_int = fill_int
self.fill_bool = fill_bool
self._validated = False
def _validate_container(self):
if self._validated:
return
if type(self.container) is not AutoResetEnv:
raise RuntimeError(
f"The {self.__class__.__name__} container must be of type AutoResetEnv."
)
self._validated = True
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
self._validate_container()
return self._replace_auto_reset_vals(tensordict_reset=tensordict_reset)
def _step(
self, tensordict: TensorDictBase, next_tensordict: TensorDictBase
) -> TensorDictBase:
return self._correct_auto_reset_vals(next_tensordict)
@property
def _simple_done(self):
return self.parent._simple_done
def _correct_auto_reset_vals(self, tensordict):
# we need to move the data from tensordict to tensordict_
def replace_and_set(key, val, mask, saved_td_autoreset, agent=tensordict):
saved_td_autoreset.set(key, val)
if val.dtype.is_floating_point:
if self.fill_float is None:
val_set_nan = val.clone()
else:
val_set_nan = torch.where(
expand_as_right(mask, val),
torch.full_like(val, self.fill_float),
val,
)
elif val.dtype.is_signed:
if self.fill_int is None:
val_set_nan = val.clone()
else:
val_set_nan = torch.where(
expand_as_right(mask, val),
torch.full_like(val, self.fill_int),
val,
)
else:
if self.fill_bool is None:
val_set_nan = val.clone()
else:
val_set_nan = torch.where(
expand_as_right(mask, val),
torch.full_like(val, self.fill_bool),
val,
)
agent.set(key, val_set_nan)
if self._simple_done:
done = tensordict.get("done")
if done.any():
mask = done.squeeze(-1)
self._saved_td_autorest = TensorDict()
for key in self.parent.full_observation_spec.keys(True, True):
val = tensordict.get(key)
replace_and_set(
key, val, mask, saved_td_autoreset=self._saved_td_autorest
)
else:
parents = []
# Go through each "done" key and get the corresponding agent.
_saved_td_autorest = None
obs_keys = list(self.parent.full_observation_spec.keys(True, True))
for done_key in self.parent.done_keys:
if _ends_with(done_key, "done"):
if isinstance(done_key, str):
raise TypeError(
"A 'done' key was a string but a tuple was expected."
)
agent_key = done_key[:-1]
done = tensordict.get(done_key)
mask = done.squeeze(-1)
if done.any():
if _saved_td_autorest is None:
_saved_td_autorest = TensorDict()
agent = tensordict.get(agent_key)
if isinstance(agent, LazyStackedTensorDict):
agents = agent.tensordicts
masks = mask.unbind(agent.stack_dim)
saved_td_autorest_agent = LazyStackedTensorDict(
*[td.empty() for td in agents],
stack_dim=agent.stack_dim,
)
saved_td_autorest_agents = (
saved_td_autorest_agent.tensordicts
)
else:
agents = [agent]
masks = [mask]
saved_td_autorest_agent = _saved_td_autorest.setdefault(
agent_key, agent.empty()
)
saved_td_autorest_agents = [saved_td_autorest_agent]
for key in obs_keys:
if (
isinstance(key, tuple)
and key[: len(agent_key)] == agent_key
):
for _agent, _mask, _saved_td_autorest_agent in zip(
agents, masks, saved_td_autorest_agents
):
val = _agent.get(key[len(agent_key) :])
replace_and_set(
key[len(agent_key) :],
val,
_mask,
saved_td_autoreset=_saved_td_autorest_agent,
agent=_agent,
)
parents.append(done_key[:-1])
if _saved_td_autorest is not None:
self.__dict__["_saved_td_autorest"] = _saved_td_autorest
return tensordict
def _replace_auto_reset_vals(self, *, tensordict_reset):
_saved_td_autorest = self.__dict__.get("_saved_td_autorest", None)
if _saved_td_autorest is None:
return tensordict_reset
if self._simple_done:
for key, val in self._saved_td_autorest.items(True, True):
if _ends_with(key, "_reset"):
continue
val_set_reg = val
tensordict_reset.set(key, val_set_reg)
else:
for done_key in self.parent.done_keys:
if _ends_with(done_key, "done"):
agent_key = done_key[:-1]
mask = self._saved_td_autorest.pop(
_replace_last(done_key, "__mask__"), None
)
if mask is not None:
agent = self._saved_td_autorest.get(agent_key)
if isinstance(agent, LazyStackedTensorDict):
agents = agent.tensordicts
masks = mask.unbind(agent.stack_dim)
dests = tensordict_reset.setdefault(
agent_key,
LazyStackedTensorDict(
*[td.empty() for td in agents],
stack_dim=agent.stack_dim,
),
)
else:
agents = [agent]
masks = [mask]
dests = [
tensordict_reset.setdefault(agent_key, agent.empty())
]
for _agent, _mask, _dest in zip(agents, masks, dests):
for key, val in _agent.items(True, True):
if _ends_with(key, "_reset"):
continue
if not _mask.all():
val_not_reset = _dest.get(key)
val_set_reg = torch.where(
expand_as_right(mask, val), val, val_not_reset
)
else:
val_set_reg = val
_dest.set(key, val_set_reg)
delattr(self, "_saved_td_autorest")
return tensordict_reset
[docs]class ActionDiscretizer(Transform):
"""A transform to discretize a continuous action space.
This transform makes it possible to use an algorithm designed for discrete
action spaces such as DQN over environments with a continuous action space.
Args:
num_intervals (int or torch.Tensor): the number of discrete values
for each element of the action space. If a single integer is provided,
all action items are sliced with the same number of elements.
If a tensor is provided, it must have the same number of elements
as the action space (ie, the length of the ``num_intervals`` tensor
must match the last dimension of the action space).
action_key (NestedKey, optional): the action key to use. Points to
the action of the parent env (the floating point action).
Defaults to ``"action"``.
out_action_key (NestedKey, optional): the key where the discrete
action should be written. If ``None`` is provided, it defaults to
the value of ``action_key``. If both keys do not match, the
continuous action_spec is moved from the ``full_action_spec``
environment attribute to the ``full_state_spec`` container,
as only the discrete action should be sampled for an action to
be taken. Providing ``out_action_key`` can ensure that the
floating point action is available to be recorded.
sampling (ActionDiscretizer.SamplingStrategy, optinoal): an element
of the ``ActionDiscretizer.SamplingStrategy`` ``IntEnum`` object
(``MEDIAN``, ``LOW``, ``HIGH`` or ``RANDOM``). Indicates how the
continuous action should be sampled in the provided interval.
categorical (bool, optional): if ``False``, one-hot encoding is used.
Defaults to ``True``.
Examples:
>>> from torchrl.envs import GymEnv, check_env_specs
>>> import torch
>>> base_env = GymEnv("HalfCheetah-v4")
>>> num_intervals = torch.arange(5, 11)
>>> categorical = True
>>> sampling = ActionDiscretizer.SamplingStrategy.MEDIAN
>>> t = ActionDiscretizer(
... num_intervals=num_intervals,
... categorical=categorical,
... sampling=sampling,
... out_action_key="action_disc",
... )
>>> env = base_env.append_transform(t)
TransformedEnv(
env=GymEnv(env=HalfCheetah-v4, batch_size=torch.Size([]), device=cpu),
transform=ActionDiscretizer(
num_intervals=tensor([ 5, 6, 7, 8, 9, 10]),
action_key=action,
out_action_key=action_disc,,
sampling=0,
categorical=True))
>>> check_env_specs(env)
>>> # Produce a rollout
>>> r = env.rollout(4)
>>> print(r)
TensorDict(
fields={
action: Tensor(shape=torch.Size([4, 6]), device=cpu, dtype=torch.float32, is_shared=False),
action_disc: Tensor(shape=torch.Size([4, 6]), device=cpu, dtype=torch.int64, is_shared=False),
done: Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.bool, is_shared=False),
observation: Tensor(shape=torch.Size([4, 17]), device=cpu, dtype=torch.float64, is_shared=False),
reward: Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.float32, is_shared=False),
terminated: Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.bool, is_shared=False),
truncated: Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
batch_size=torch.Size([4]),
device=cpu,
is_shared=False),
observation: Tensor(shape=torch.Size([4, 17]), device=cpu, dtype=torch.float64, is_shared=False),
terminated: Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.bool, is_shared=False),
truncated: Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
batch_size=torch.Size([4]),
device=cpu,
is_shared=False)
>>> assert r["action"].dtype == torch.float
>>> assert r["action_disc"].dtype == torch.int64
>>> assert (r["action"] < base_env.action_spec.high).all()
>>> assert (r["action"] > base_env.action_spec.low).all()
"""
[docs] class SamplingStrategy(IntEnum):
"""The sampling strategies for ActionDiscretizer."""
MEDIAN = 0
LOW = 1
HIGH = 2
RANDOM = 3
def __init__(
self,
num_intervals: int | torch.Tensor,
action_key: NestedKey = "action",
out_action_key: NestedKey = None,
sampling=None,
categorical: bool = True,
):
if out_action_key is None:
out_action_key = action_key
super().__init__(in_keys_inv=[action_key], out_keys_inv=[out_action_key])
self.action_key = action_key
self.out_action_key = out_action_key
if not isinstance(num_intervals, torch.Tensor):
self.num_intervals = num_intervals
else:
self.register_buffer("num_intervals", num_intervals)
if sampling is None:
sampling = self.SamplingStrategy.MEDIAN
self.sampling = sampling
self.categorical = categorical
def __repr__(self):
def _indent(s):
return indent(s, 4 * " ")
num_intervals = f"num_intervals={self.num_intervals}"
action_key = f"action_key={self.action_key}"
out_action_key = f"out_action_key={self.out_action_key}"
sampling = f"sampling={self.sampling}"
categorical = f"categorical={self.categorical}"
return (
f"{type(self).__name__}(\n{_indent(num_intervals)},\n{_indent(action_key)},"
f"\n{_indent(out_action_key)},\n{_indent(sampling)},\n{_indent(categorical)})"
)
def _custom_arange(self, nint, device):
result = torch.arange(
start=0.0,
end=1.0,
step=1 / nint,
dtype=self.dtype,
device=device,
)
result_ = result
if self.sampling in (
self.SamplingStrategy.HIGH,
self.SamplingStrategy.MEDIAN,
):
result_ = (1 - result).flip(0)
if self.sampling == self.SamplingStrategy.MEDIAN:
result = (result + result_) / 2
else:
result = result_
return result
[docs] def transform_input_spec(self, input_spec):
try:
action_spec = self.parent.full_action_spec_unbatched[self.in_keys_inv[0]]
if not isinstance(action_spec, Bounded):
raise TypeError(
f"action spec type {type(action_spec)} is not supported. The action spec type must be Bounded."
)
n_act = action_spec.shape
if not n_act:
n_act = ()
empty_shape = True
else:
n_act = (n_act[-1],)
empty_shape = False
self.n_act = n_act
self.dtype = action_spec.dtype
interval = action_spec.high - action_spec.low
num_intervals = self.num_intervals
if not empty_shape:
interval = interval.unsqueeze(-1)
elif isinstance(num_intervals, torch.Tensor):
num_intervals = int(num_intervals.squeeze())
self.num_intervals = torch.as_tensor(num_intervals)
if isinstance(num_intervals, int):
arange = (
self._custom_arange(num_intervals, action_spec.device).expand(
(*n_act, num_intervals)
)
* interval
)
low = action_spec.low
if not empty_shape:
low = low.unsqueeze(-1)
self.register_buffer("intervals", low + arange)
else:
arange = [
self._custom_arange(_num_intervals, action_spec.device) * interval
for _num_intervals, interval in zip(
num_intervals.tolist(), interval.unbind(-2)
)
]
self.intervals = [
low + arange
for low, arange in zip(
action_spec.low.unsqueeze(-1).unbind(-2), arange
)
]
if not isinstance(num_intervals, torch.Tensor):
nvec = torch.as_tensor(num_intervals, device=action_spec.device)
else:
nvec = num_intervals
if nvec.ndim > 1:
raise RuntimeError(f"Cannot use num_intervals with shape {nvec.shape}")
if nvec.ndim == 0 or nvec.numel() == 1:
if not empty_shape:
nvec = nvec.expand(action_spec.shape[-1])
else:
nvec = nvec.squeeze()
self.register_buffer("nvec", nvec)
if self.sampling == self.SamplingStrategy.RANDOM:
# compute jitters
self.jitters = interval.squeeze(-1) / nvec
shape = (
action_spec.shape
if self.categorical
else (*action_spec.shape[:-1], nvec.sum())
)
if not empty_shape:
cls = (
functools.partial(MultiCategorical, remove_singleton=False)
if self.categorical
else MultiOneHot
)
action_spec = cls(nvec=nvec, shape=shape, device=action_spec.device)
else:
cls = Categorical if self.categorical else OneHot
action_spec = cls(n=int(nvec), shape=shape, device=action_spec.device)
batch_size = self.parent.batch_size
if batch_size:
action_spec = action_spec.expand(batch_size + action_spec.shape)
input_spec["full_action_spec", self.out_keys_inv[0]] = action_spec
if self.out_keys_inv[0] != self.in_keys_inv[0]:
input_spec["full_state_spec", self.in_keys_inv[0]] = input_spec[
"full_action_spec", self.in_keys_inv[0]
].clone()
del input_spec["full_action_spec", self.in_keys_inv[0]]
return input_spec
except AttributeError as err:
# To avoid silent AttributeErrors
raise RuntimeError(str(err))
def _init(self):
# We just need to access the action spec for everything to be initialized
try:
_ = self.container.full_action_spec
except AttributeError:
raise RuntimeError(
f"Cannot execute transform {type(self).__name__} without a parent env."
)
def inv(self, tensordict):
if self.out_keys_inv[0] == self.in_keys_inv[0]:
return super().inv(tensordict)
# We re-write this because we don't want to clone the TD here
return self._inv_call(tensordict)
def _inv_call(self, tensordict):
# action is categorical, map it to desired dtype
intervals = getattr(self, "intervals", None)
if intervals is None:
self._init()
return self._inv_call(tensordict)
action = tensordict.get(self.out_keys_inv[0])
if self.categorical:
action = action.unsqueeze(-1)
if isinstance(intervals, torch.Tensor):
shape = action.shape[: -intervals.ndim]
intervals = intervals.expand(shape + intervals.shape)
action = intervals.gather(index=action, dim=-1).squeeze(-1)
else:
action = torch.stack(
[
interval.gather(index=action, dim=-1).squeeze(-1)
for interval, action in zip(intervals, action.unbind(-2))
],
-1,
)
else:
nvec = self.nvec
empty_shape = not nvec.ndim
if not empty_shape:
nvec = nvec.tolist()
if isinstance(intervals, torch.Tensor):
shape = action.shape[: (-intervals.ndim + 1)]
intervals = intervals.expand(shape + intervals.shape)
intervals = intervals.unbind(-2)
action = action.split(nvec, dim=-1)
action = torch.stack(
[
intervals[action].view(action.shape[:-1])
for (intervals, action) in zip(intervals, action)
],
-1,
)
else:
shape = action.shape[: -intervals.ndim]
intervals = intervals.expand(shape + intervals.shape)
action = intervals[action].squeeze(-1)
if self.sampling == self.SamplingStrategy.RANDOM:
action = action + self.jitters * torch.rand_like(self.jitters)
return tensordict.set(self.in_keys_inv[0], action)
[docs]class TrajCounter(Transform):
"""Global trajectory counter transform.
TrajCounter can be used to count the number of trajectories (i.e., the number of times `reset` is called) in any
TorchRL environment.
This transform will work within a single node across multiple processes (see note below).
A single transform can only count the trajectories associated with a single done state, but nested done states are
accepted as long as their prefix matches the prefix of the counter key.
Args:
out_key (NestedKey, optional): The entry name of the trajectory counter. Defaults to ``"traj_count"``.
Examples:
>>> from torchrl.envs import GymEnv, StepCounter, TrajCounter
>>> env = GymEnv("Pendulum-v1").append_transform(StepCounter(6))
>>> env = env.append_transform(TrajCounter())
>>> r = env.rollout(18, break_when_any_done=False) # 18 // 6 = 3 trajectories
>>> r["next", "traj_count"]
tensor([[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[2],
[2],
[2],
[2],
[2],
[2]])
.. note::
Sharing a trajectory counter among workers can be done in multiple ways, but it will usually involve wrapping the environment in a :class:`~torchrl.envs.EnvCreator`. Not doing so may result in an error during serialization of the transform. The counter will be shared among the workers, meaning that at any point in time, it is guaranteed that there will not be two environments that will share the same trajectory count (and each (step-count, traj-count) pair will be unique).
Here are examples of valid ways of sharing a ``TrajCounter`` object between processes:
>>> # Option 1: Create the trajectory counter outside the environment.
>>> # This requires the counter to be cloned within the transformed env, as a single transform object cannot have two parents.
>>> t = TrajCounter()
>>> def make_env(max_steps=4, t=t):
... # See CountingEnv in torchrl.test.mocking_classes
... env = TransformedEnv(CountingEnv(max_steps=max_steps), t.clone())
... env.transform.transform_observation_spec(env.base_env.observation_spec)
... return env
>>> penv = ParallelEnv(
... 2,
... [EnvCreator(make_env, max_steps=4), EnvCreator(make_env, max_steps=5)],
... mp_start_method="spawn",
... )
>>> # Option 2: Create the transform within the constructor.
>>> # In this scenario, we still need to tell each sub-env what kwarg has to be used.
>>> # Both EnvCreator and ParallelEnv offer that possibility.
>>> def make_env(max_steps=4):
... t = TrajCounter()
... env = TransformedEnv(CountingEnv(max_steps=max_steps), t)
... env.transform.transform_observation_spec(env.base_env.observation_spec)
... return env
>>> make_env_c0 = EnvCreator(make_env)
>>> # Create a variant of the env with different kwargs
>>> make_env_c1 = make_env_c0.make_variant(max_steps=5)
>>> penv = ParallelEnv(
... 2,
... [make_env_c0, make_env_c1],
... mp_start_method="spawn",
... )
>>> # Alternatively, pass the kwargs to the ParallelEnv
>>> penv = ParallelEnv(
... 2,
... [make_env_c0, make_env_c0],
... create_env_kwargs=[{"max_steps": 5}, {"max_steps": 4}],
... mp_start_method="spawn",
... )
"""
def __init__(self, out_key: NestedKey = "traj_count"):
super().__init__(in_keys=[], out_keys=[out_key])
self._make_shared_value()
self._initialized = False
def _make_shared_value(self):
self._traj_count = mp.Value("i", 0)
@implement_for("torch", None, "2.1")
def __getstate__(self):
state = self.__dict__.copy()
state["_traj_count"] = None
return state
@implement_for("torch", "2.1")
def __getstate__(self): # noqa: F811
state = super().__getstate__()
state["_traj_count"] = None
return state
def clone(self):
clone = super().clone()
# All clones share the same _traj_count and lock
clone._traj_count = self._traj_count
return clone
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
if not self._initialized:
self._initialized = True
rk = self.parent.reset_keys
traj_count_key = self.out_keys[0]
is_str = isinstance(traj_count_key, str)
for _rk in rk:
if is_str and isinstance(_rk, str):
rk = _rk
break
elif (
not is_str
and isinstance(_rk, tuple)
and _rk[:-1] == traj_count_key[:-1]
):
rk = _rk
break
else:
raise RuntimeError(
f"Did not find reset key that matched the prefix of the traj counter key. Reset keys: {rk}, traj count: {traj_count_key}"
)
reset = None
if tensordict is not None:
reset = tensordict.get(rk, default=None)
if reset is None:
reset = torch.ones(
self.container.observation_spec[self.out_keys[0]].shape,
device=tensordict_reset.device,
dtype=torch.bool,
)
with (self._traj_count):
tc = int(self._traj_count.value)
self._traj_count.value = self._traj_count.value + reset.sum().item()
episodes = torch.arange(tc, tc + reset.sum(), device=self.parent.device)
episodes = torch.masked_scatter(
torch.zeros_like(reset, dtype=episodes.dtype), reset, episodes
)
tensordict_reset.set(traj_count_key, episodes)
return tensordict_reset
def _step(
self, tensordict: TensorDictBase, next_tensordict: TensorDictBase
) -> TensorDictBase:
if not self._initialized:
raise RuntimeError("_step was called before _reset was called.")
next_tensordict.set(self.out_keys[0], tensordict.get(self.out_keys[0]))
return next_tensordict
def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
raise RuntimeError(
f"{type(self).__name__} can only be called within an environment step or reset."
)
[docs] def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
raise RuntimeError(
f"{type(self).__name__} can only be called within an environment step or reset."
)
[docs] def state_dict(self, *args, destination=None, prefix="", keep_vars=False):
return {
"traj_count": int(self._traj_count.value),
}
[docs] def load_state_dict(
self, state_dict: Mapping[str, Any], strict: bool = True, assign: bool = False
):
self._traj_count.value *= 0
self._traj_count.value += state_dict["traj_count"]
[docs] def transform_observation_spec(self, observation_spec: Composite) -> Composite:
if not isinstance(observation_spec, Composite):
raise ValueError(
f"observation_spec was expected to be of type Composite. Got {type(observation_spec)} instead."
)
full_done_spec = self.parent.output_spec["full_done_spec"]
traj_count_key = self.out_keys[0]
# find a matching done key (there might be more than one)
for done_key in self.parent.done_keys:
# check root
if type(done_key) != type(traj_count_key):
continue
if isinstance(done_key, tuple):
if done_key[:-1] == traj_count_key[:-1]:
shape = full_done_spec[done_key].shape
break
if isinstance(done_key, str):
shape = full_done_spec[done_key].shape
break
else:
raise KeyError(
f"Could not find root of traj_count key {traj_count_key} in done keys {self.done_keys}."
)
observation_spec[traj_count_key] = Bounded(
shape=shape,
dtype=torch.int64,
device=observation_spec.device,
low=0,
high=torch.iinfo(torch.int64).max,
)
return super().transform_observation_spec(observation_spec)
