Source code for torchrl.data.tensor_specs
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
import abc
import enum
import math
import warnings
from collections.abc import Iterable
from copy import deepcopy
from dataclasses import dataclass
from functools import wraps
from textwrap import indent
from typing import (
Any,
Callable,
Dict,
Generic,
List,
Optional,
overload,
Sequence,
Tuple,
TypeVar,
Union,
)
import numpy as np
import tensordict
import torch
from tensordict import (
is_tensor_collection,
LazyStackedTensorDict,
NonTensorData,
TensorDict,
TensorDictBase,
unravel_key,
)
from tensordict.base import NO_DEFAULT
from tensordict.utils import _getitem_batch_size, NestedKey
from torchrl._utils import _make_ordinal_device, get_binary_env_var, implement_for
DEVICE_TYPING = Union[torch.device, str, int]
INDEX_TYPING = Union[int, torch.Tensor, np.ndarray, slice, List]
SHAPE_INDEX_TYPING = Union[
int,
range,
List[int],
np.ndarray,
slice,
None,
torch.Tensor,
type(...),
Tuple[
int,
range,
List[int],
np.ndarray,
slice,
None,
torch.Tensor,
type(...),
Tuple[Any],
],
]
# By default, we do not check that an obs is in the domain. THis should be done when validating the env beforehand
_CHECK_SPEC_ENCODE = get_binary_env_var("CHECK_SPEC_ENCODE")
_DEFAULT_SHAPE = torch.Size((1,))
DEVICE_ERR_MSG = "device of empty Composite is not defined."
NOT_IMPLEMENTED_ERROR = NotImplementedError(
"method is not currently implemented."
" If you are interested in this feature please submit"
" an issue at https://github.com/pytorch/rl/issues"
)
def _size(list_of_ints):
# ensures that np int64 elements don't slip through Size
# see https://github.com/pytorch/pytorch/issues/127194
return torch.Size([int(i) for i in list_of_ints])
# Akin to TD's NO_DEFAULT but won't raise a KeyError when found in a TD or used as default
class _NoDefault(enum.IntEnum):
ZERO = 0
ONE = 1
NO_DEFAULT_RL = _NoDefault.ONE
def _default_dtype_and_device(
dtype: Union[None, torch.dtype],
device: Union[None, str, int, torch.device],
allow_none_device: bool = False,
) -> Tuple[torch.dtype, torch.device | None]:
if dtype is None:
dtype = torch.get_default_dtype()
if device is not None:
device = _make_ordinal_device(torch.device(device))
elif not allow_none_device:
device = torch.zeros(()).device
return dtype, device
def _validate_idx(shape: list[int], idx: int, axis: int = 0):
"""Raise an IndexError if idx is out of bounds for shape[axis].
Args:
shape (list[int]): Input shape
idx (int): Index, may be negative
axis (int): Shape axis to check
"""
if shape[axis] >= 0 and (idx >= shape[axis] or idx < 0 and -idx > shape[axis]):
raise IndexError(
f"index {idx} is out of bounds for axis {axis} with size {shape[axis]}"
)
def _validate_iterable(
idx: Iterable[Any], expected_type: type, iterable_classname: str
):
"""Raise an IndexError if the iterable contains a type different from the expected type or Iterable.
Args:
idx (Iterable[Any]): Iterable, may contain nested iterables
expected_type (type): Required item type in the Iterable (e.g. int)
iterable_classname (str): Iterable type as a string (e.g. 'List'). Logging purpose only.
"""
for item in idx:
if isinstance(item, Iterable):
_validate_iterable(item, expected_type, iterable_classname)
else:
if not isinstance(item, expected_type):
raise IndexError(
f"{iterable_classname} indexing expects {expected_type} indices"
)
def _slice_indexing(shape: list[int], idx: slice) -> List[int]:
"""Given an input shape and a slice index, returns the new indexed shape.
Args:
shape (list[int]): Input shape
idx (slice): Index
Returns:
Indexed shape
Examples:
>>> _slice_indexing([3, 4], slice(None, 2))
[2, 4]
>>> list(torch.rand(3, 4)[:2].shape)
[2, 4]
"""
if idx.step == 0:
raise ValueError("slice step cannot be zero")
# Slicing an empty shape returns the shape
if len(shape) == 0:
return shape
if idx.start is None:
start = 0
else:
start = idx.start if idx.start >= 0 else max(shape[0] + idx.start, 0)
if idx.stop is None:
stop = shape[0]
else:
stop = idx.stop if idx.stop >= 0 else max(shape[0] + idx.stop, 0)
step = 1 if idx.step is None else idx.step
if step > 0:
if start >= stop:
n_items = 0
else:
stop = min(stop, shape[0])
n_items = math.ceil((stop - start) / step)
else:
if start <= stop:
n_items = 0
else:
start = min(start, shape[0] - 1)
n_items = math.ceil((stop - start) / step)
return [n_items] + shape[1:]
def _shape_indexing(
shape: Union[list[int], torch.Size, Tuple[int]], idx: SHAPE_INDEX_TYPING
) -> List[int]:
"""Given an input shape and an index, returns the size of the resulting indexed spec.
This function includes indexing checks and may raise IndexErrors.
Args:
shape (list[int], torch.Size, Tuple[int): Input shape
idx (SHAPE_INDEX_TYPING): Index
Returns:
Shape of the resulting spec
Examples:
>>> idx = (2, ..., None)
>>> Categorical(2, shape=(3, 4))[idx].shape
torch.Size([4, 1])
>>> _shape_indexing([3, 4], idx)
torch.Size([4, 1])
"""
if not isinstance(shape, list):
shape = list(shape)
if idx is Ellipsis or (
isinstance(idx, slice) and (idx.step is idx.start is idx.stop is None)
):
return shape
if idx is None:
return [1] + shape
if len(shape) == 0 and (
isinstance(idx, int)
or isinstance(idx, range)
or isinstance(idx, list)
and len(idx) > 0
):
raise IndexError(
f"cannot use integer indices on 0-dimensional shape. `{idx}` received"
)
if isinstance(idx, int):
_validate_idx(shape, idx)
return shape[1:]
if isinstance(idx, range):
if len(idx) > 0 and (idx.start >= shape[0] or idx.stop > shape[0]):
raise IndexError(f"index out of bounds for axis 0 with size {shape[0]}")
return [len(idx)] + shape[1:]
if isinstance(idx, slice):
return _slice_indexing(shape, idx)
if isinstance(idx, tuple):
# Supports int, None, slice and ellipsis indices
# Index on the current shape dimension
shape_idx = 0
none_dims = 0
ellipsis = False
prev_is_list = False
shape_len = len(shape)
for item_idx, item in enumerate(idx):
if item is None:
shape = shape[:shape_idx] + [1] + shape[shape_idx:]
shape_idx += 1
none_dims += 1
elif isinstance(item, int):
_validate_idx(shape, item, shape_idx)
del shape[shape_idx]
elif isinstance(item, slice):
shape[shape_idx] = _slice_indexing([shape[shape_idx]], item)[0]
shape_idx += 1
elif item is Ellipsis:
if ellipsis:
raise IndexError("an index can only have a single ellipsis (`...`)")
# Move to the end of the shape, subtracted by the number of future indices impacting the dimensions (i.e. all except None and ...)
shape_idx = len(shape) - len(
[i for i in idx[item_idx + 1 :] if not (i is None or i is Ellipsis)]
)
ellipsis = True
elif any(
isinstance(item, _type)
for _type in [list, tuple, range, np.ndarray, torch.Tensor]
):
while isinstance(idx, tuple) and len(idx) == 1:
idx = idx[0]
# Nested tuples are handled as a list. Numpy behavior
if isinstance(item, tuple):
item = list(item)
if prev_is_list and isinstance(item, list):
del shape[shape_idx]
continue
if isinstance(item, list):
prev_is_list = True
if shape_idx >= len(shape):
raise IndexError("Raise IndexError: too many indices for array")
res = _shape_indexing([shape[shape_idx]], item)
shape = shape[:shape_idx] + res + shape[shape_idx + 1 :]
shape_idx += len(res)
else:
raise IndexError(
f"tuple indexing only supports integers, ranges, slices (`:`), ellipsis (`...`), new axis (`None`), tuples, list, tensor and ndarray indices. {str(type(idx))} received"
)
if len(idx) - none_dims - int(ellipsis) > shape_len:
raise IndexError(
f"shape is {shape_len}-dimensional, but {len(idx) - none_dims - int(ellipsis)} dimensions were indexed"
)
return shape
if isinstance(idx, list):
# int indexing only
_validate_iterable(idx, int, "list")
for item in np.array(idx).reshape(-1):
_validate_idx(shape, item, 0)
return list(np.array(idx).shape) + shape[1:]
if isinstance(idx, np.ndarray) or isinstance(idx, torch.Tensor):
# Out of bounds check
for item in idx.reshape(-1):
_validate_idx(shape, item)
return list(_getitem_batch_size(shape, idx))
class invertible_dict(dict):
"""An invertible dictionary.
Examples:
>>> my_dict = invertible_dict(a=3, b=2)
>>> inv_dict = my_dict.invert()
>>> assert {2, 3} == set(inv_dict.keys())
"""
def __init__(self, *args, inv_dict=None, **kwargs):
if inv_dict is None:
inv_dict = {}
super().__init__(*args, **kwargs)
self.inv_dict = inv_dict
def __setitem__(self, k, v):
if v in self.inv_dict or k in self:
raise Exception("overwriting in invertible_dict is not permitted")
self.inv_dict[v] = k
return super().__setitem__(k, v)
def update(self, d):
raise NotImplementedError
def invert(self):
d = invertible_dict()
for k, value in self.items():
d[value] = k
return d
def inverse(self):
return self.inv_dict
class Box:
"""A box of values."""
def __iter__(self):
raise NotImplementedError
def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> ContinuousBox:
raise NotImplementedError
def __repr__(self):
return f"{self.__class__.__name__}()"
def clone(self) -> CategoricalBox:
return deepcopy(self)
@dataclass(repr=False)
class ContinuousBox(Box):
"""A continuous box of values, in between a minimum (self.low) and a maximum (self.high)."""
_low: torch.Tensor
_high: torch.Tensor
device: torch.device | None = None
# We store the tensors on CPU to avoid overloading CUDA with tensors that are rarely used.
@property
def low(self):
return self._low.to(self.device)
@property
def high(self):
return self._high.to(self.device)
def unbind(self, dim: int = 0):
return tuple(
type(self)(low, high, self.device)
for (low, high) in zip(self.low.unbind(dim), self.high.unbind(dim))
)
@low.setter
def low(self, value):
self.device = value.device
self._low = value.cpu()
@high.setter
def high(self, value):
self.device = value.device
self._high = value.cpu()
def __post_init__(self):
self.low = self.low.clone()
self.high = self.high.clone()
def __iter__(self):
yield self.low
yield self.high
def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> ContinuousBox:
return self.__class__(self.low.to(dest), self.high.to(dest))
def clone(self) -> ContinuousBox:
return self.__class__(self.low.clone(), self.high.clone())
def __repr__(self):
min_str = indent(
f"\nlow=Tensor(shape={self.low.shape}, device={self.low.device}, dtype={self.low.dtype}, contiguous={self.high.is_contiguous()})",
" " * 4,
)
max_str = indent(
f"\nhigh=Tensor(shape={self.high.shape}, device={self.high.device}, dtype={self.high.dtype}, contiguous={self.high.is_contiguous()})",
" " * 4,
)
return f"{self.__class__.__name__}({min_str},{max_str})"
def __eq__(self, other):
if other is None:
minval, maxval = _minmax_dtype(self.low.dtype)
minval = torch.as_tensor(minval).to(self.low.device, self.low.dtype)
maxval = torch.as_tensor(maxval).to(self.low.device, self.low.dtype)
if (
torch.isclose(self.low, minval).all()
and torch.isclose(self.high, maxval).all()
):
return True
if (
not torch.isfinite(self.low).any()
and not torch.isfinite(self.high).any()
):
return True
return False
return (
type(self) == type(other)
and self.low.dtype == other.low.dtype
and self.high.dtype == other.high.dtype
and self.device == other.device
and torch.isclose(self.low, other.low).all()
and torch.isclose(self.high, other.high).all()
)
@dataclass(repr=False)
class CategoricalBox(Box):
"""A box of discrete, categorical values."""
n: int
register = invertible_dict()
def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> CategoricalBox:
return deepcopy(self)
def __repr__(self):
return f"{self.__class__.__name__}(n={self.n})"
class DiscreteBox(CategoricalBox):
"""Deprecated version of :class:`CategoricalBox`."""
...
@dataclass(repr=False)
class BoxList(Box):
"""A box of discrete values."""
boxes: List
def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> BoxList:
return BoxList([box.to(dest) for box in self.boxes])
def __iter__(self):
for elt in self.boxes:
yield elt
def __repr__(self):
return f"{self.__class__.__name__}(boxes={self.boxes})"
def __len__(self):
return len(self.boxes)
@staticmethod
def from_nvec(nvec: torch.Tensor):
if nvec.ndim == 0:
return CategoricalBox(nvec.item())
else:
return BoxList([BoxList.from_nvec(n) for n in nvec.unbind(-1)])
@dataclass(repr=False)
class BinaryBox(Box):
"""A box of n binary values."""
n: int
def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> ContinuousBox:
return deepcopy(self)
def __repr__(self):
return f"{self.__class__.__name__}(n={self.n})"
[docs]@dataclass(repr=False)
class TensorSpec:
"""Parent class of the tensor meta-data containers.
TorchRL's TensorSpec are used to present what input/output is to be expected for a specific class,
or sometimes to simulate simple behaviors by generating random data within a defined space.
TensorSpecs are primarily used in environments to specify their input/output structure without needing to
execute the environment (or starting it). They can also be used to instantiate shared buffers to pass
data from worker to worker.
TensorSpecs are dataclasses that always share the following fields: `shape`, `space, `dtype` and `device`.
As such, TensorSpecs possess some common behavior with :class:`~torch.Tensor` and :class:`~tensordict.TensorDict`:
they can be reshaped, indexed, squeezed, unsqueezed, moved to another device etc.
Args:
shape (torch.Size): size of the tensor. The shape includes the batch dimensions as well as the feature
dimension. A negative shape (``-1``) means that the dimension has a variable number of elements.
space (Box): Box instance describing what kind of values can be expected.
device (torch.device): device of the tensor.
dtype (torch.dtype): dtype of the tensor.
.. note:: A spec can be constructed from a :class:`~tensordict.TensorDict` using the :func:`~torchrl.envs.utils.make_composite_from_td`
function. This function makes a low-assumption educated guess on the specs that may correspond to the input
tensordict and can help to build specs automatically without an in-depth knowledge of the `TensorSpec` API.
"""
shape: torch.Size
space: Union[None, Box]
device: torch.device | None = None
dtype: torch.dtype = torch.float
domain: str = ""
SPEC_HANDLED_FUNCTIONS = {}
[docs] @classmethod
def implements_for_spec(cls, torch_function: Callable) -> Callable:
"""Register a torch function override for TensorSpec."""
@wraps(torch_function)
def decorator(func):
cls.SPEC_HANDLED_FUNCTIONS[torch_function] = func
return func
return decorator
@property
def device(self) -> torch.device:
"""The device of the spec.
Only :class:`Composite` specs can have a ``None`` device. All leaves must have a non-null device.
"""
return self._device
@device.setter
def device(self, device: torch.device | None) -> None:
self._device = _make_ordinal_device(device)
[docs] def clear_device_(self) -> T:
"""A no-op for all leaf specs (which must have a device).
For :class:`Composite` specs, this method will erase the device.
"""
return self
[docs] def encode(
self,
val: np.ndarray | torch.Tensor | TensorDictBase,
*,
ignore_device: bool = False,
) -> torch.Tensor | TensorDictBase:
"""Encodes a value given the specified spec, and return the corresponding tensor.
This method is to be used in environments that return a value (eg, a numpy array) that can be
easily mapped to the TorchRL required domain.
If the value is already a tensor, the spec will not change its value and return it as-is.
Args:
val (np.ndarray or torch.Tensor): value to be encoded as tensor.
Keyword Args:
ignore_device (bool, optional): if ``True``, the spec device will
be ignored. This is used to group tensor casting within a call
to ``TensorDict(..., device="cuda")`` which is faster.
Returns:
torch.Tensor matching the required tensor specs.
"""
if not isinstance(val, torch.Tensor):
if isinstance(val, list):
if len(val) == 1:
# gym used to return lists of images since 0.26.0
# We convert these lists in np.array or take the first element
# if there is just one.
# See https://github.com/pytorch/rl/pull/403/commits/73d77d033152c61d96126ccd10a2817fecd285a1
val = val[0]
else:
val = np.array(val)
if isinstance(val, np.ndarray) and not all(
stride > 0 for stride in val.strides
):
val = val.copy()
if not ignore_device:
val = torch.as_tensor(val, device=self.device, dtype=self.dtype)
else:
val = torch.as_tensor(val, dtype=self.dtype)
if val.shape != self.shape:
# if val.shape[-len(self.shape) :] != self.shape:
# option 1: add a singleton dim at the end
if val.shape == self.shape and self.shape[-1] == 1:
val = val.unsqueeze(-1)
else:
try:
val = val.reshape(self.shape)
except Exception as err:
raise RuntimeError(
f"Shape mismatch: the value has shape {val.shape} which "
f"is incompatible with the spec shape {self.shape}."
) from err
if _CHECK_SPEC_ENCODE:
self.assert_is_in(val)
return val
def __ne__(self, other):
return not (self == other)
def __setattr__(self, key, value):
if key == "shape":
value = _size(value)
super().__setattr__(key, value)
[docs] def to_numpy(
self, val: torch.Tensor | TensorDictBase, safe: bool = None
) -> np.ndarray | dict:
"""Returns the ``np.ndarray`` correspondent of an input tensor.
This is intended to be the inverse operation of :meth:`.encode`.
Args:
val (torch.Tensor): tensor to be transformed_in to numpy.
safe (bool): boolean value indicating whether a check should be
performed on the value against the domain of the spec.
Defaults to the value of the ``CHECK_SPEC_ENCODE`` environment variable.
Returns:
a np.ndarray.
"""
if safe is None:
safe = _CHECK_SPEC_ENCODE
if safe:
self.assert_is_in(val)
return val.detach().cpu().numpy()
@property
def ndim(self) -> int:
"""Number of dimensions of the spec shape.
Shortcut for ``len(spec.shape)``.
"""
return self.ndimension()
[docs] def ndimension(self) -> int:
"""Number of dimensions of the spec shape.
Shortcut for ``len(spec.shape)``.
"""
return len(self.shape)
@property
def _safe_shape(self) -> torch.Size:
"""Returns a shape where all heterogeneous values are replaced by one (to be expandable)."""
return _size([int(v) if v >= 0 else 1 for v in self.shape])
[docs] @abc.abstractmethod
def index(
self, index: INDEX_TYPING, tensor_to_index: torch.Tensor | TensorDictBase
) -> torch.Tensor | TensorDictBase:
"""Indexes the input tensor.
Args:
index (int, torch.Tensor, slice or list): index of the tensor
tensor_to_index: tensor to be indexed
Returns:
indexed tensor
"""
...
@overload
def expand(self, shape: torch.Size):
...
[docs] @abc.abstractmethod
def expand(self, *shape: int) -> T:
"""Returns a new Spec with the expanded shape.
Args:
*shape (tuple or iterable of int): the new shape of the Spec.
Must be broadcastable with the current shape:
its length must be at least as long as the current shape length,
and its last values must be compliant too; ie they can only differ
from it if the current dimension is a singleton.
"""
...
[docs] def squeeze(self, dim: int | None = None) -> T:
"""Returns a new Spec with all the dimensions of size ``1`` removed.
When ``dim`` is given, a squeeze operation is done only in that dimension.
Args:
dim (int or None): the dimension to apply the squeeze operation to
"""
shape = _squeezed_shape(self.shape, dim)
if shape is None:
return self
return self.__class__(shape=shape, device=self.device, dtype=self.dtype)
[docs] def unsqueeze(self, dim: int) -> T:
"""Returns a new Spec with one more singleton dimension (at the position indicated by ``dim``).
Args:
dim (int or None): the dimension to apply the unsqueeze operation to.
"""
shape = _unsqueezed_shape(self.shape, dim)
return self.__class__(shape=shape, device=self.device, dtype=self.dtype)
[docs] def make_neg_dim(self, dim: int) -> T:
"""Converts a specific dimension to ``-1``."""
if dim < 0:
dim = self.ndim + dim
if dim < 0 or dim > self.ndim - 1:
raise ValueError(f"dim={dim} is out of bound for ndim={self.ndim}")
self.shape = _size([s if i != dim else -1 for i, s in enumerate(self.shape)])
@overload
def reshape(self, shape) -> T:
...
[docs] def reshape(self, *shape) -> T:
"""Reshapes a ``TensorSpec``.
Check :func:`~torch.reshape` for more information on this method.
"""
if len(shape) == 1 and not isinstance(shape[0], int):
return self.reshape(*shape[0])
return self._reshape(shape)
view = reshape
@abc.abstractmethod
def _reshape(self, shape: torch.Size) -> T:
...
[docs] def unflatten(self, dim: int, sizes: Tuple[int]) -> T:
"""Unflattens a ``TensorSpec``.
Check :func:`~torch.unflatten` for more information on this method.
"""
return self._unflatten(dim, sizes)
def _unflatten(self, dim: int, sizes: Tuple[int]) -> T:
shape = torch.zeros(self.shape, device="meta").unflatten(dim, sizes).shape
return self._reshape(shape)
[docs] def flatten(self, start_dim: int, end_dim: int) -> T:
"""Flattens a ``TensorSpec``.
Check :func:`~torch.flatten` for more information on this method.
"""
return self._flatten(start_dim, end_dim)
def _flatten(self, start_dim, end_dim):
shape = torch.zeros(self.shape, device="meta").flatten(start_dim, end_dim).shape
return self._reshape(shape)
@abc.abstractmethod
def _project(
self, val: torch.Tensor | TensorDictBase
) -> torch.Tensor | TensorDictBase:
raise NotImplementedError(type(self))
[docs] @abc.abstractmethod
def is_in(self, val: torch.Tensor | TensorDictBase) -> bool:
"""If the value ``val`` could have been generated by the ``TensorSpec``, returns ``True``, otherwise ``False``.
More precisely, the ``is_in`` methods checks that the value ``val`` is within the limits defined by the ``space``
attribute (the box), and that the ``dtype``, ``device``, ``shape`` potentially other metadata match those
of the spec. If any of these checks fails, the ``is_in`` method will return ``False``.
Args:
val (torch.Tensor): value to be checked.
Returns:
boolean indicating if values belongs to the TensorSpec box.
"""
...
[docs] def contains(self, item: torch.Tensor | TensorDictBase) -> bool:
"""If the value ``val`` could have been generated by the ``TensorSpec``, returns ``True``, otherwise ``False``.
See :meth:`~.is_in` for more information.
"""
return self.is_in(item)
[docs] @abc.abstractmethod
def enumerate(self) -> Any:
"""Returns all the samples that can be obtained from the TensorSpec.
The samples will be stacked along the first dimension.
This method is only implemented for discrete specs.
"""
...
[docs] def project(
self, val: torch.Tensor | TensorDictBase
) -> torch.Tensor | TensorDictBase:
"""If the input tensor is not in the TensorSpec box, it maps it back to it given some defined heuristic.
Args:
val (torch.Tensor): tensor to be mapped to the box.
Returns:
a torch.Tensor belonging to the TensorSpec box.
"""
if not self.is_in(val):
return self._project(val)
return val
[docs] def assert_is_in(self, value: torch.Tensor) -> None:
"""Asserts whether a tensor belongs to the box, and raises an exception otherwise.
Args:
value (torch.Tensor): value to be checked.
"""
if not self.is_in(value):
raise AssertionError(
f"Encoding failed because value is not in space. "
f"Consider calling project(val) first. value was = {value} "
f"and spec was {self}."
)
[docs] def type_check(self, value: torch.Tensor, key: NestedKey = None) -> None:
"""Checks the input value ``dtype`` against the ``TensorSpec`` ``dtype`` and raises an exception if they don't match.
Args:
value (torch.Tensor): tensor whose dtype has to be checked.
key (str, optional): if the TensorSpec has keys, the value
dtype will be checked against the spec pointed by the
indicated key.
"""
if value.dtype is not self.dtype:
raise TypeError(
f"value.dtype={value.dtype} but"
f" {self.__class__.__name__}.dtype={self.dtype}"
)
[docs] @abc.abstractmethod
def rand(self, shape: torch.Size = None) -> torch.Tensor | TensorDictBase:
"""Returns a random tensor in the space defined by the spec.
The sampling will be done uniformly over the space, unless the box is unbounded in which case normal values
will be drawn.
Args:
shape (torch.Size): shape of the random tensor
Returns:
a random tensor sampled in the TensorSpec box.
"""
...
[docs] def sample(self, shape: torch.Size = None) -> torch.Tensor | TensorDictBase:
"""Returns a random tensor in the space defined by the spec.
See :meth:`~.rand` for details.
"""
return self.rand(shape=shape)
[docs] def zero(self, shape: torch.Size = None) -> torch.Tensor | TensorDictBase:
"""Returns a zero-filled tensor in the box.
.. note:: Even though there is no guarantee that ``0`` belongs to the spec domain,
this method will not raise an exception when this condition is violated.
The primary use case of ``zero`` is to generate empty data buffers, not meaningful data.
Args:
shape (torch.Size): shape of the zero-tensor
Returns:
a zero-filled tensor sampled in the TensorSpec box.
"""
if shape is None:
shape = _size([])
return torch.zeros(
(*shape, *self._safe_shape), dtype=self.dtype, device=self.device
)
[docs] def zeros(self, shape: torch.Size = None) -> torch.Tensor | TensorDictBase:
"""Proxy to :meth:`~.zero`."""
return self.zero(shape=shape)
[docs] def one(self, shape: torch.Size = None) -> torch.Tensor | TensorDictBase:
"""Returns a one-filled tensor in the box.
.. note:: Even though there is no guarantee that ``1`` belongs to the spec domain,
this method will not raise an exception when this condition is violated.
The primary use case of ``one`` is to generate empty data buffers, not meaningful data.
Args:
shape (torch.Size): shape of the one-tensor
Returns:
a one-filled tensor sampled in the TensorSpec box.
"""
if self.dtype == torch.bool:
return ~self.zero(shape=shape)
return self.zero(shape) + 1
[docs] def ones(self, shape: torch.Size = None) -> torch.Tensor | TensorDictBase:
"""Proxy to :meth:`~.one`."""
return self.one(shape=shape)
[docs] @abc.abstractmethod
def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> "TensorSpec":
"""Casts a TensorSpec to a device or a dtype.
Returns the same spec if no change is made.
"""
...
[docs] def cuda(self, device=None):
"""Casts the TensorSpec to 'cuda' device."""
if device is None:
return self.to("cuda")
return self.to(f"cuda:{device}")
[docs] @abc.abstractmethod
def clone(self) -> "TensorSpec":
"""Creates a copy of the TensorSpec."""
...
def __repr__(self):
shape_str = indent("shape=" + str(self.shape), " " * 4)
space_str = indent("space=" + str(self.space), " " * 4)
device_str = indent("device=" + str(self.device), " " * 4)
dtype_str = indent("dtype=" + str(self.dtype), " " * 4)
domain_str = indent("domain=" + str(self.domain), " " * 4)
sub_string = ",\n".join(
[shape_str, space_str, device_str, dtype_str, domain_str]
)
string = f"{self.__class__.__name__}(\n{sub_string})"
return string
@classmethod
def __torch_function__(
cls,
func: Callable,
types,
args: Tuple = (),
kwargs: Optional[dict] = None,
) -> Callable:
if kwargs is None:
kwargs = {}
if func not in cls.SPEC_HANDLED_FUNCTIONS or not all(
issubclass(t, (TensorSpec,)) for t in types
):
return NotImplemented(
f"func {func} for spec {cls} with handles {cls.SPEC_HANDLED_FUNCTIONS}"
)
return cls.SPEC_HANDLED_FUNCTIONS[func](*args, **kwargs)
def unbind(self, dim: int = 0):
raise NotImplementedError
T = TypeVar("T")
class _LazyStackedMixin(Generic[T]):
def __init__(self, *specs: Tuple[T, ...], dim: int) -> None:
self._specs = list(specs)
self.dim = dim
if self.dim < 0:
self.dim = len(self.shape) + self.dim
def clear_device_(self):
"""Clears the device of the Composite."""
for spec in self._specs:
spec.clear_device_()
return self
def __getitem__(self, item):
is_key = isinstance(item, str) or (
isinstance(item, tuple) and all(isinstance(_item, str) for _item in item)
)
if is_key:
return torch.stack(
[composite_spec[item] for composite_spec in self._specs], dim=self.dim
)
elif isinstance(item, tuple):
# quick check that the index is along the stacked dim
# case 1: index is a tuple, and the first arg is an ellipsis. Then dim must be the last dim of all composite_specs
if item[0] is Ellipsis:
if len(item) == 1:
return self
elif self.dim == len(self.shape) - 1 and len(item) == 2:
# we can return
return self._specs[item[1]]
elif len(item) > 2:
# check that there is only one non-slice index
assigned = False
dim_idx = self.dim
for i, _item in enumerate(item[1:]):
if (
isinstance(_item, slice)
and not (
_item.start is None
and _item.stop is None
and _item.step is None
)
) or not isinstance(_item, slice):
if assigned:
raise RuntimeError(
"Found more than one meaningful index in a stacked composite spec."
)
item = _item
dim_idx = i + 1
assigned = True
if not assigned:
return self
if dim_idx != self.dim:
raise RuntimeError(
f"Indexing occured along dimension {dim_idx} but stacking was done along dim {self.dim}."
)
out = self._specs[item]
if isinstance(out, TensorSpec):
return out
return torch.stack(list(out), 0)
else:
raise IndexError(
f"Indexing a {self.__class__.__name__} with [..., idx] is only permitted if the stack dimension is the last dimension. "
f"Got self.dim={self.dim} and self.shape={self.shape}."
)
elif len(item) >= 2 and item[-1] is Ellipsis:
return self[item[:-1]]
elif any(_item is Ellipsis for _item in item):
raise IndexError("Cannot index along multiple dimensions.")
# Ellipsis is now ruled out
elif any(_item is None for _item in item):
raise IndexError(
f"Cannot index a {self.__class__.__name__} with None values"
)
# Must be an index with slices then
else:
for i, _item in enumerate(item):
if i == self.dim:
out = self._specs[_item]
if isinstance(out, TensorSpec):
return out
return torch.stack(list(out), 0)
elif isinstance(_item, slice):
# then the slice must be trivial
if not (_item.step is _item.start is _item.stop is None):
raise IndexError(
f"Got a non-trivial index at dim {i} when only the dim {self.dim} could be indexed."
)
else:
return self
else:
if not self.dim == 0:
raise IndexError(
f"Trying to index a {self.__class__.__name__} along dimension 0 when the stack dimension is {self.dim}."
)
out = self._specs[item]
if isinstance(out, TensorSpec):
return out
return torch.stack(list(out), 0)
def clone(self) -> T:
return torch.stack([spec.clone() for spec in self._specs], self.stack_dim)
@property
def stack_dim(self):
return self.dim
def zero(self, shape: torch.Size = None) -> TensorDictBase:
if shape is not None:
dim = self.dim + len(shape)
else:
dim = self.dim
if dim != 0:
raise RuntimeError(
f"Cannot create a nested tensor with a stack dimension other than 0. Got dim={0}"
)
return torch.nested.nested_tensor([spec.zero(shape) for spec in self._specs])
def one(self, shape: torch.Size = None) -> TensorDictBase:
if shape is not None:
dim = self.dim + len(shape)
else:
dim = self.dim
if dim != 0:
raise RuntimeError(
f"Cannot create a nested tensor with a stack dimension other than 0. Got dim={0}"
)
return torch.nested.nested_tensor([spec.one(shape) for spec in self._specs])
def rand(self, shape: torch.Size = None) -> TensorDictBase:
if shape is not None:
dim = self.dim + len(shape)
else:
dim = self.dim
samples = [spec.rand(shape) for spec in self._specs]
if dim != 0:
raise RuntimeError(
f"Cannot create a nested tensor with a stack dimension other than 0. Got self.dim={self.dim}."
)
return torch.nested.nested_tensor(samples)
def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> T:
if dest is None:
return self
return torch.stack([spec.to(dest) for spec in self._specs], self.dim)
def unbind(self, dim: int = 0):
if dim < 0:
dim = self.ndim + dim
shape = self.shape
if dim < 0 or dim > self.ndim - 1 or shape[dim] == -1:
raise ValueError(
f"Provided dim {dim} is not valid for unbinding shape {shape}"
)
if dim == self.stack_dim:
return self._specs
elif dim > self.dim:
dim = dim - 1
return type(self)(*[spec.unbind(dim) for spec in self._specs], dim=self.dim)
else:
return type(self)(
*[spec.unbind(dim) for spec in self._specs], dim=self.dim - 1
)
def unsqueeze(self, dim: int):
if dim < 0:
new_dim = dim + len(self.shape) + 1
else:
new_dim = dim
if new_dim > len(self.shape) or new_dim < 0:
raise ValueError(f"Cannot unsqueeze along dim {dim}.")
if new_dim > self.dim:
# unsqueeze 2, stack is on 1 => unsqueeze 1, stack along 1
new_stack_dim = self.dim
new_dim = new_dim - 1
else:
# unsqueeze 0, stack is on 1 => unsqueeze 0, stack on 1
new_stack_dim = self.dim + 1
return torch.stack(
[spec.unsqueeze(new_dim) for spec in self._specs], dim=new_stack_dim
)
def make_neg_dim(self, dim: int):
if dim < 0:
dim = self.ndim + dim
if dim < 0 or dim > self.ndim - 1:
raise ValueError(f"dim={dim} is out of bound for ndim={self.ndim}")
if dim == self.dim:
raise ValueError("Cannot make dim=self.dim negative")
if dim < self.dim:
for spec in self._specs:
spec.make_neg_dim(dim)
else:
for spec in self._specs:
spec.make_neg_dim(dim - 1)
def squeeze(self, dim: int = None):
if dim is None:
size = self.shape
if len(size) == 1 or size.count(1) == 0:
return self
first_singleton_dim = size.index(1)
squeezed_dict = self.squeeze(first_singleton_dim)
return squeezed_dict.squeeze(dim=None)
if dim < 0:
new_dim = self.ndim + dim
else:
new_dim = dim
if self.shape and (new_dim >= self.ndim or new_dim < 0):
raise RuntimeError(
f"squeezing is allowed for dims comprised between 0 and "
f"spec.ndim only. Got dim={dim} and shape"
f"={self.shape}."
)
if new_dim >= self.ndim or self.shape[new_dim] != 1:
return self
if new_dim == self.dim:
return self._specs[0]
if new_dim > self.dim:
# squeeze 2, stack is on 1 => squeeze 1, stack along 1
new_stack_dim = self.dim
new_dim = new_dim - 1
else:
# squeeze 0, stack is on 1 => squeeze 0, stack on 1
new_stack_dim = self.dim - 1
return torch.stack(
[spec.squeeze(new_dim) for spec in self._specs], dim=new_stack_dim
)
[docs]class Stacked(_LazyStackedMixin[TensorSpec], TensorSpec):
"""A lazy representation of a stack of tensor specs.
Stacks tensor-specs together along one dimension.
When random samples are drawn, a stack of samples is returned if possible.
If not, an error is thrown.
Indexing is allowed but only along the stack dimension.
This class aims at being used in multi-tasks and multi-agent settings, where
heterogeneous specs may occur (same semantic but different shape).
"""
def __eq__(self, other):
if not isinstance(other, Stacked):
return False
if self.device != other.device:
raise RuntimeError((self, other))
return False
if len(self._specs) != len(other._specs):
return False
for _spec1, _spec2 in zip(self._specs, other._specs):
if _spec1 != _spec2:
return False
return True
[docs] def enumerate(self) -> torch.Tensor | TensorDictBase:
return torch.stack(
[spec.enumerate() for spec in self._specs], dim=self.stack_dim + 1
)
def __len__(self):
return self.shape[0]
[docs] def to_numpy(self, val: torch.Tensor, safe: bool = None) -> dict:
if safe is None:
safe = _CHECK_SPEC_ENCODE
if safe:
if val.shape[self.dim] != len(self._specs):
raise ValueError(
"Size of Stacked and val differ along the stacking " "dimension"
)
for spec, v in zip(self._specs, torch.unbind(val, dim=self.dim)):
spec.assert_is_in(v)
return val.detach().cpu().numpy()
def __repr__(self):
shape_str = "shape=" + str(self.shape)
device_str = "device=" + str(self.device)
dtype_str = "dtype=" + str(self.dtype)
domain_str = "domain=" + str(self._specs[0].domain)
sub_string = ", ".join([shape_str, device_str, dtype_str, domain_str])
string = f"Stacked{self._specs[0].__class__.__name__}(\n {sub_string})"
return string
@property
def device(self) -> DEVICE_TYPING:
return self._specs[0].device
@property
def ndim(self):
return self.ndimension()
@property
def shape(self):
first_shape = self._specs[0].shape
shape = []
for i in range(len(first_shape)):
homo_dim = True
for spec in self._specs:
if spec.shape[i] != first_shape[i]:
homo_dim = False
break
shape.append(first_shape[i] if homo_dim else -1)
dim = self.dim
if dim < 0:
dim = len(shape) + dim + 1
shape.insert(dim, len(self._specs))
return _size(shape)
@shape.setter
def shape(self, shape):
if len(shape) != len(self.shape):
raise RuntimeError(
f"Cannot set shape of different length from self. shape={shape}, self.shape={self.shape}"
)
if shape[self.dim] != self.shape[self.dim]:
raise RuntimeError(
f"The shape attribute mismatches between the input {shape} and self.shape={self.shape}."
)
shape_strip = _size([s for i, s in enumerate(self.shape) if i != self.dim])
for spec in self._specs:
spec.shape = shape_strip
[docs] def expand(self, *shape):
if len(shape) == 1 and not isinstance(shape[0], (int,)):
return self.expand(*shape[0])
expand_shape = shape[: -len(self.shape)]
existing_shape = self.shape
shape_check = shape[-len(self.shape) :]
for _i, (size1, size2) in enumerate(zip(existing_shape, shape_check)):
if size1 != size2 and size1 != 1:
raise RuntimeError(
f"Expanding a non-singletom dimension: existing shape={size1} vs expand={size2}"
)
elif size1 != size2 and size1 == 1 and _i == self.dim:
# if we're expanding along the stack dim we just need to clone the existing spec
return torch.stack(
[self._specs[0].clone() for _ in range(size2)], self.dim
).expand(*shape)
if _i != len(self.shape) - 1:
raise RuntimeError(
f"Trying to expand non-congruent shapes: received {shape} when the shape is {self.shape}."
)
# remove the stack dim from the expanded shape, which we know to match
shape_check = [s for i, s in enumerate(shape_check) if i != self.dim]
specs = []
for spec in self._specs:
spec_shape = []
for dim_check, spec_dim in zip(shape_check, spec.shape):
spec_shape.append(dim_check if dim_check != -1 else spec_dim)
unstack_shape = list(expand_shape) + list(spec_shape)
specs.append(spec.expand(unstack_shape))
return torch.stack(
specs,
self.dim + len(expand_shape),
)
[docs] def type_check(self, value: torch.Tensor, key: NestedKey | None = None) -> None:
for (val, spec) in zip(value.unbind(self.dim), self._specs):
spec.type_check(val)
[docs] def is_in(self, value) -> bool:
if self.dim == 0 and not hasattr(value, "unbind"):
# We don't use unbind because value could be a tuple or a nested tensor
return all(
spec.contains(value) for (value, spec) in zip(value, self._specs)
)
return all(
spec.contains(value)
for (value, spec) in zip(value.unbind(self.dim), self._specs)
)
@property
def space(self):
raise NOT_IMPLEMENTED_ERROR
def _project(self, val: TensorDictBase) -> TensorDictBase:
raise NOT_IMPLEMENTED_ERROR
[docs] def encode(
self, val: Union[np.ndarray, torch.Tensor], *, ignore_device=False
) -> torch.Tensor:
if self.dim != 0 and not isinstance(val, tuple):
val = val.unbind(self.dim)
samples = [spec.encode(_val) for _val, spec in zip(val, self._specs)]
if is_tensor_collection(samples[0]):
return LazyStackedTensorDict.maybe_dense_stack(samples, dim=self.dim)
if isinstance(samples[0], torch.Tensor):
if any(t.is_nested for t in samples):
raise RuntimeError("Cannot stack nested tensors together.")
if len(samples) > 1 and not all(
t.shape == samples[0].shape for t in samples[1:]
):
return torch.nested.nested_tensor(samples)
return torch.stack(samples, dim=self.dim)
[docs]@dataclass(repr=False)
class OneHot(TensorSpec):
"""A unidimensional, one-hot discrete tensor spec.
By default, TorchRL assumes that categorical variables are encoded as
one-hot encodings of the variable. This allows for simple indexing of
tensors, e.g.
>>> batch, size = 3, 4
>>> action_value = torch.arange(batch*size)
>>> action_value = action_value.view(batch, size).to(torch.float)
>>> action = (action_value == action_value.max(-1,
... keepdim=True)[0]).to(torch.long)
>>> chosen_action_value = (action * action_value).sum(-1)
>>> print(chosen_action_value)
tensor([ 3., 7., 11.])
The last dimension of the shape (variable domain) cannot be indexed.
Args:
n (int): number of possible outcomes.
shape (torch.Size, optional): total shape of the sampled tensors.
If provided, the last dimension must match ``n``.
device (str, int or torch.device, optional): device of the tensors.
dtype (str or torch.dtype, optional): dtype of the tensors.
use_register (bool): experimental feature. If ``True``, every integer
will be mapped onto a binary vector in the order in which they
appear. This feature is designed for environment with no
a-priori definition of the number of possible outcomes (e.g.
discrete outcomes are sampled from an arbitrary set, whose
elements will be mapped in a register to a series of unique
one-hot binary vectors).
mask (torch.Tensor or None): mask some of the possible outcomes when a
sample is taken. See :meth:`~.update_mask` for more information.
Examples:
>>> from torchrl.data.tensor_specs import OneHot
>>> spec = OneHot(5, shape=(2, 5))
>>> spec.rand()
tensor([[False, True, False, False, False],
[False, True, False, False, False]])
>>> mask = torch.tensor([
... [False, False, False, False, True],
... [False, False, False, False, True]
... ])
>>> spec.update_mask(mask)
>>> spec.rand()
tensor([[False, False, False, False, True],
[False, False, False, False, True]])
"""
shape: torch.Size
space: CategoricalBox
device: torch.device | None = None
dtype: torch.dtype = torch.float
domain: str = ""
def __init__(
self,
n: int,
shape: Optional[torch.Size] = None,
device: Optional[DEVICE_TYPING] = None,
dtype: Optional[Union[str, torch.dtype]] = torch.bool,
use_register: bool = False,
mask: torch.Tensor | None = None,
):
dtype, device = _default_dtype_and_device(dtype, device)
self.use_register = use_register
space = CategoricalBox(n)
if shape is None:
shape = _size((space.n,))
else:
shape = _size(shape)
if not len(shape) or shape[-1] != space.n:
raise ValueError(
f"The last value of the shape must match n for transform of type {self.__class__}. "
f"Got n={space.n} and shape={shape}."
)
super().__init__(
shape=shape, space=space, device=device, dtype=dtype, domain="discrete"
)
self.update_mask(mask)
@property
def n(self):
return self.space.n
[docs] def update_mask(self, mask):
"""Sets a mask to prevent some of the possible outcomes when a sample is taken.
The mask can also be set during initialization of the spec.
Args:
mask (torch.Tensor or None): boolean mask. If None, the mask is
disabled. Otherwise, the shape of the mask must be expandable to
the shape of the spec. ``False`` masks an outcome and ``True``
leaves the outcome unmasked. If all the possible outcomes are
masked, then an error is raised when a sample is taken.
Examples:
>>> mask = torch.tensor([True, False, False])
>>> ts = OneHot(3, (2, 3,), dtype=torch.int64, mask=mask)
>>> # All but one of the three possible outcomes are masked
>>> ts.rand()
tensor([[1, 0, 0],
[1, 0, 0]])
"""
if mask is not None:
try:
mask = mask.expand(self._safe_shape)
except RuntimeError as err:
raise RuntimeError("Cannot expand mask to the desired shape.") from err
if mask.dtype != torch.bool:
raise ValueError("Only boolean masks are accepted.")
self.mask = mask
[docs] def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> OneHot:
if dest is None:
return self
if isinstance(dest, torch.dtype):
dest_dtype = dest
dest_device = self.device
else:
dest_dtype = self.dtype
dest_device = torch.device(dest)
if dest_device == self.device and dest_dtype == self.dtype:
return self
return self.__class__(
n=self.space.n,
shape=self.shape,
device=dest_device,
dtype=dest_dtype,
use_register=self.use_register,
mask=self.mask.to(dest) if self.mask is not None else None,
)
[docs] def clone(self) -> OneHot:
return self.__class__(
n=self.space.n,
shape=self.shape,
device=self.device,
dtype=self.dtype,
use_register=self.use_register,
mask=self.mask.clone() if self.mask is not None else None,
)
[docs] def expand(self, *shape):
if len(shape) == 1 and isinstance(shape[0], (tuple, list, torch.Size)):
shape = shape[0]
if any(s1 != s2 and s2 != 1 for s1, s2 in zip(shape[-self.ndim :], self.shape)):
raise ValueError(
f"The last {self.ndim} of the expanded shape {shape} must match the"
f"shape of the {self.__class__.__name__} spec in expand()."
)
mask = self.mask
if mask is not None:
mask = mask.expand(_remove_neg_shapes(shape))
return self.__class__(
n=shape[-1],
shape=shape,
device=self.device,
dtype=self.dtype,
mask=mask,
)
def _reshape(self, shape):
mask = self.mask
if mask is not None:
mask = mask.reshape(shape)
return self.__class__(
n=shape[-1],
shape=shape,
device=self.device,
dtype=self.dtype,
mask=mask,
)
def _unflatten(self, dim, sizes):
mask = self.mask
if mask is not None:
mask = mask.unflatten(dim, sizes)
shape = torch.zeros(self.shape, device="meta").unflatten(dim, sizes).shape
return self.__class__(
n=shape[-1],
shape=shape,
device=self.device,
dtype=self.dtype,
mask=mask,
)
[docs] def squeeze(self, dim=None):
if self.shape[-1] == 1 and dim in (len(self.shape), -1, None):
raise ValueError(f"Final dimension of {type(self)} must remain unchanged")
shape = _squeezed_shape(self.shape, dim)
if shape is None:
return self
mask = self.mask
if mask is not None:
mask = mask.reshape(shape)
return self.__class__(
n=shape[-1],
shape=shape,
device=self.device,
dtype=self.dtype,
use_register=self.use_register,
mask=mask,
)
[docs] def unsqueeze(self, dim: int):
if dim in (len(self.shape), -1):
raise ValueError(f"Final dimension of {type(self)} must remain unchanged")
shape = _unsqueezed_shape(self.shape, dim)
mask = self.mask
if mask is not None:
mask = mask.reshape(shape)
return self.__class__(
n=shape[-1],
shape=shape,
device=self.device,
dtype=self.dtype,
use_register=self.use_register,
mask=mask,
)
def unbind(self, dim: int = 0):
if dim in (len(self.shape), -1):
raise ValueError(f"Final dimension of {type(self)} must remain unchanged")
orig_dim = dim
if dim < 0:
dim = len(self.shape) + dim
if dim < 0:
raise ValueError(
f"Cannot unbind along dim {orig_dim} with shape {self.shape}."
)
shape = tuple(s for i, s in enumerate(self.shape) if i != dim)
mask = self.mask
if mask is not None:
mask = mask.unbind(dim)
else:
mask = (None,) * self.shape[dim]
return tuple(
self.__class__(
n=shape[-1],
shape=shape,
device=self.device,
dtype=self.dtype,
use_register=self.use_register,
mask=mask[i],
)
for i in range(self.shape[dim])
)
@implement_for("torch", None, "2.1")
def rand(self, shape: torch.Size = None) -> torch.Tensor:
if shape is None:
shape = self.shape[:-1]
else:
shape = _size([*shape, *self.shape[:-1]])
mask = self.mask
n = int(self.space.n)
if mask is None:
m = torch.randint(n, shape, device=self.device)
else:
mask = mask.expand(_remove_neg_shapes(*shape, mask.shape[-1]))
if mask.ndim > 2:
mask_flat = torch.flatten(mask, 0, -2)
else:
mask_flat = mask
shape_out = mask.shape[:-1]
m = torch.multinomial(mask_flat.float(), 1).reshape(shape_out)
out = torch.nn.functional.one_hot(m, n).to(self.dtype)
# torch.zeros((*shape, self.space.n), device=self.device, dtype=self.dtype)
# out.scatter_(-1, m, 1)
return out
[docs] @implement_for("torch", "2.1")
def rand(self, shape: torch.Size = None) -> torch.Tensor: # noqa: F811
if shape is None:
shape = self.shape[:-1]
else:
shape = _size([*shape, *self.shape[:-1]])
mask = self.mask
if mask is None:
n = self.space.n
m = torch.randint(n, shape, device=self.device)
else:
mask = mask.expand(_remove_neg_shapes(*shape, mask.shape[-1]))
if mask.ndim > 2:
mask_flat = torch.flatten(mask, 0, -2)
else:
mask_flat = mask
shape_out = mask.shape[:-1]
m = torch.multinomial(mask_flat.float(), 1).reshape(shape_out)
out = torch.nn.functional.one_hot(m, self.space.n).to(self.dtype)
# torch.zeros((*shape, self.space.n), device=self.device, dtype=self.dtype)
# out.scatter_(-1, m, 1)
return out
[docs] def encode(
self,
val: Union[np.ndarray, torch.Tensor],
space: Optional[CategoricalBox] = None,
*,
ignore_device: bool = False,
) -> torch.Tensor:
if not isinstance(val, torch.Tensor):
if ignore_device:
val = torch.as_tensor(val)
else:
val = torch.as_tensor(val, device=self.device)
if space is None:
space = self.space
if self.use_register:
if val not in space.register:
space.register[val] = len(space.register)
val = space.register[val]
if (val >= space.n).any():
raise AssertionError("Value must be less than action space.")
val = torch.nn.functional.one_hot(val.long(), space.n).to(self.dtype)
return val
[docs] def to_numpy(self, val: torch.Tensor, safe: bool = None) -> np.ndarray:
if safe is None:
safe = _CHECK_SPEC_ENCODE
if safe:
if not isinstance(val, torch.Tensor):
raise NotImplementedError
self.assert_is_in(val)
val = val.long().argmax(-1).cpu().numpy()
if self.use_register:
inv_reg = self.space.register.inverse()
vals = []
for _v in val.view(-1):
vals.append(inv_reg[int(_v)])
return np.array(vals).reshape(tuple(val.shape))
return val
[docs] def enumerate(self) -> torch.Tensor:
return (
torch.eye(self.n, dtype=self.dtype, device=self.device)
.expand(*self.shape, self.n)
.permute(-2, *range(self.ndimension() - 1), -1)
)
[docs] def index(self, index: INDEX_TYPING, tensor_to_index: torch.Tensor) -> torch.Tensor:
if not isinstance(index, torch.Tensor):
raise ValueError(
f"Only tensors are allowed for indexing using "
f"{self.__class__.__name__}.index(...)"
)
index = index.nonzero().squeeze()
index = index.expand((*tensor_to_index.shape[:-1], index.shape[-1]))
return tensor_to_index.gather(-1, index)
def __getitem__(self, idx: SHAPE_INDEX_TYPING):
"""Indexes the current TensorSpec based on the provided index.
The last dimension of the spec corresponding to the variable domain cannot be indexed.
"""
indexed_shape = _shape_indexing(self.shape[:-1], idx)
return self.__class__(
n=self.space.n,
shape=_size(indexed_shape + [self.shape[-1]]),
device=self.device,
dtype=self.dtype,
use_register=self.use_register,
mask=self.mask[idx] if self.mask is not None else None,
)
def _project(self, val: torch.Tensor) -> torch.Tensor:
if self.mask is None:
out = torch.multinomial(val.to(torch.float), 1).squeeze(-1)
out = torch.nn.functional.one_hot(out, self.space.n).to(self.dtype)
return out
shape = self.mask.shape
shape = torch.broadcast_shapes(shape, val.shape)
mask_expand = self.mask.expand(shape)
gathered = mask_expand & val
oob = ~gathered.any(-1)
new_val = torch.multinomial(mask_expand[oob].float(), 1)
val = val.clone()
val[oob] = 0
val[oob] = torch.scatter(val[oob], -1, new_val, 1)
return val
[docs] def is_in(self, val: torch.Tensor) -> bool:
if self.mask is None:
shape = torch.broadcast_shapes(self._safe_shape, val.shape)
shape_match = val.shape == shape
if not shape_match:
return False
dtype_match = val.dtype == self.dtype
if not dtype_match:
return False
return (val.sum(-1) == 1).all()
shape = self.mask.shape
shape = torch.broadcast_shapes(shape, val.shape)
mask_expand = self.mask.expand(shape)
gathered = mask_expand & val
return gathered.any(-1).all()
def __eq__(self, other):
if not hasattr(other, "mask"):
return False
mask_equal = (self.mask is None and other.mask is None) or (
isinstance(self.mask, torch.Tensor)
and isinstance(other.mask, torch.Tensor)
and (self.mask.shape == other.mask.shape)
and (self.mask == other.mask).all()
)
return (
type(self) == type(other)
and self.shape == other.shape
and self.space == other.space
and self.device == other.device
and self.dtype == other.dtype
and self.domain == other.domain
and self.use_register == other.use_register
and mask_equal
)
[docs] def to_categorical(self, val: torch.Tensor, safe: bool = None) -> torch.Tensor:
"""Converts a given one-hot tensor in categorical format.
Args:
val (torch.Tensor, optional): One-hot tensor to convert in categorical format.
safe (bool): boolean value indicating whether a check should be
performed on the value against the domain of the spec.
Defaults to the value of the ``CHECK_SPEC_ENCODE`` environment variable.
Returns:
The categorical tensor.
Examples:
>>> one_hot = OneHot(3, shape=(2, 3))
>>> one_hot_sample = one_hot.rand()
>>> one_hot_sample
tensor([[False, True, False],
[False, True, False]])
>>> categ_sample = one_hot.to_categorical(one_hot_sample)
>>> categ_sample
tensor([1, 1])
"""
if safe is None:
safe = _CHECK_SPEC_ENCODE
if safe:
self.assert_is_in(val)
return val.long().argmax(-1)
[docs] def to_categorical_spec(self) -> Categorical:
"""Converts the spec to the equivalent categorical spec.
Examples:
>>> one_hot = OneHot(3, shape=(2, 3))
>>> one_hot.to_categorical_spec()
Categorical(
shape=torch.Size([2]),
space=CategoricalBox(n=3),
device=cpu,
dtype=torch.int64,
domain=discrete)
"""
return Categorical(
self.space.n,
device=self.device,
shape=self.shape[:-1],
mask=self.mask,
)
[docs] def to_one_hot(self, val: torch.Tensor, safe: bool = None) -> torch.Tensor:
"""No-op for OneHot."""
return val
class _BoundedMeta(abc.ABCMeta):
def __call__(cls, *args, **kwargs):
instance = super().__call__(*args, **kwargs)
if instance.domain == "continuous":
instance.__class__ = BoundedContinuous
else:
instance.__class__ = BoundedDiscrete
return instance
[docs]@dataclass(repr=False)
class Bounded(TensorSpec, metaclass=_BoundedMeta):
"""A bounded tensor spec.
``Bounded`` specs will never appear as such and always be subclassed as :class:`BoundedContinuous`
or :class:`BoundedDiscrete` depending on their dtype (floating points dtypes will result in
:class:`BoundedContinuous` instances, all others in :class:`BoundedDiscrete` instances).
Args:
low (np.ndarray, torch.Tensor or number): lower bound of the box.
high (np.ndarray, torch.Tensor or number): upper bound of the box.
shape (torch.Size): the shape of the ``Bounded`` spec. The shape must be specified.
Inputs ``low``, ``high`` and ``shape`` must be broadcastable.
device (str, int or torch.device, optional): device of the tensors.
dtype (str or torch.dtype, optional): dtype of the tensors.
domain (str): `"continuous"` or `"discrete"`. Can be used to override the automatic type assignment.
Examples:
>>> spec = Bounded(low=-1, high=1, shape=(), dtype=torch.float)
>>> spec
BoundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous)
>>> spec = Bounded(low=-1, high=1, shape=(), dtype=torch.int)
>>> spec
BoundedDiscrete(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int32, contiguous=True)),
device=cpu,
dtype=torch.int32,
domain=discrete)
>>> spec.to(torch.float)
BoundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous)
>>> spec = Bounded(low=-1, high=1, shape=(), dtype=torch.int, domain="continuous")
>>> spec
BoundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int32, contiguous=True)),
device=cpu,
dtype=torch.int32,
domain=continuous)
"""
# SPEC_HANDLED_FUNCTIONS = {}
CONFLICTING_KWARGS = (
"The keyword arguments {} and {} conflict. Only one of these can be passed."
)
def __init__(
self,
low: Union[float, torch.Tensor, np.ndarray] = None,
high: Union[float, torch.Tensor, np.ndarray] = None,
shape: Optional[Union[torch.Size, int]] = None,
device: Optional[DEVICE_TYPING] = None,
dtype: Optional[Union[torch.dtype, str]] = None,
**kwargs,
):
if "maximum" in kwargs:
if high is not None:
raise TypeError(self.CONFLICTING_KWARGS.format("high", "maximum"))
high = kwargs.pop("maximum")
warnings.warn(
"Maximum is deprecated since v0.4.0, using high instead.",
category=DeprecationWarning,
)
if "minimum" in kwargs:
if low is not None:
raise TypeError(self.CONFLICTING_KWARGS.format("low", "minimum"))
low = kwargs.pop("minimum")
warnings.warn(
"Minimum is deprecated since v0.4.0, using low instead.",
category=DeprecationWarning,
)
domain = kwargs.pop("domain", None)
if len(kwargs):
raise TypeError(f"Got unrecognised kwargs {tuple(kwargs.keys())}.")
dtype, device = _default_dtype_and_device(dtype, device)
if dtype is None:
dtype = torch.get_default_dtype()
if domain is None:
if dtype.is_floating_point:
domain = "continuous"
else:
domain = "discrete"
if not isinstance(low, torch.Tensor):
low = torch.tensor(low, dtype=dtype, device=device)
if not isinstance(high, torch.Tensor):
high = torch.tensor(high, dtype=dtype, device=device)
if high.device != device:
high = high.to(device)
if low.device != device:
low = low.to(device)
if dtype is not None and low.dtype is not dtype:
low = low.to(dtype)
if dtype is not None and high.dtype is not dtype:
high = high.to(dtype)
err_msg = (
"Bounded requires the shape to be explicitely (via "
"the shape argument) or implicitely defined (via either the "
"minimum or the maximum or both). If the maximum and/or the "
"minimum have a non-singleton shape, they must match the "
"provided shape if this one is set explicitely."
)
if shape is not None and not isinstance(shape, torch.Size):
if isinstance(shape, int):
shape = _size([shape])
else:
shape = _size(list(shape))
if shape is not None:
shape_corr = _remove_neg_shapes(shape)
else:
shape_corr = None
if high.ndimension():
if shape_corr is not None and shape_corr != high.shape:
raise RuntimeError(err_msg)
if shape is None:
shape = high.shape
if shape_corr is not None:
low = low.expand(shape_corr).clone()
elif low.ndimension():
if shape_corr is not None and shape_corr != low.shape:
raise RuntimeError(err_msg)
if shape is None:
shape = low.shape
if shape_corr is not None:
high = high.expand(shape_corr).clone()
elif shape_corr is None:
raise RuntimeError(err_msg)
else:
low = low.expand(shape_corr).clone()
high = high.expand(shape_corr).clone()
if low.numel() > high.numel():
high = high.expand_as(low).clone()
elif high.numel() > low.numel():
low = low.expand_as(high).clone()
if shape_corr is None:
shape = low.shape
else:
if isinstance(shape_corr, float):
shape_corr = _size([shape_corr])
elif not isinstance(shape_corr, torch.Size):
shape_corr = _size(shape_corr)
shape_corr_err_msg = (
f"low and shape_corr mismatch, got {low.shape} and {shape_corr}"
)
if len(low.shape) != len(shape_corr):
raise RuntimeError(shape_corr_err_msg)
if not all(_s == _sa for _s, _sa in zip(shape_corr, low.shape)):
raise RuntimeError(shape_corr_err_msg)
self.shape = shape
super().__init__(
shape=shape,
space=ContinuousBox(low, high, device=device),
device=device,
dtype=dtype,
domain=domain,
)
[docs] def enumerate(self) -> Any:
raise NotImplementedError(
f"enumerate is not implemented for spec of class {type(self).__name__}."
)
def __eq__(self, other):
return (
type(other) == type(self)
and self.device == other.device
and self.shape == other.shape
and self.space == other.space
and self.dtype == other.dtype
)
@property
def low(self):
return self.space.low
@property
def high(self):
return self.space.high
[docs] def expand(self, *shape):
if len(shape) == 1 and isinstance(shape[0], (tuple, list, torch.Size)):
shape = shape[0]
if any(
orig_val != val and val < 0
for val, orig_val in zip(shape[-len(self.shape) :], self.shape)
):
raise ValueError(
f"{self.__class__.__name__}.expand does not support negative shapes."
)
if any(s1 != s2 and s2 != 1 for s1, s2 in zip(shape[-self.ndim :], self.shape)):
raise ValueError(
f"The last {self.ndim} of the expanded shape {shape} must match the"
f"shape of the {self.__class__.__name__} spec in expand()."
)
return self.__class__(
low=self.space.low.expand(_remove_neg_shapes(shape)).clone(),
high=self.space.high.expand(_remove_neg_shapes(shape)).clone(),
shape=shape,
device=self.device,
dtype=self.dtype,
)
def _reshape(self, shape):
return self.__class__(
low=self.space.low.reshape(shape).clone(),
high=self.space.high.reshape(shape).clone(),
shape=shape,
device=self.device,
dtype=self.dtype,
)
def _unflatten(self, dim, sizes):
shape = torch.zeros(self.shape, device="meta").unflatten(dim, sizes).shape
return self.__class__(
low=self.space.low.unflatten(dim, sizes).clone(),
high=self.space.high.unflatten(dim, sizes).clone(),
shape=shape,
device=self.device,
dtype=self.dtype,
)
[docs] def squeeze(self, dim: int | None = None):
shape = _squeezed_shape(self.shape, dim)
if shape is None:
return self
if dim is None:
low = self.space.low.squeeze().clone()
high = self.space.high.squeeze().clone()
else:
low = self.space.low.squeeze(dim).clone()
high = self.space.high.squeeze(dim).clone()
return self.__class__(
low=low,
high=high,
shape=shape,
device=self.device,
dtype=self.dtype,
)
[docs] def unsqueeze(self, dim: int):
shape = _unsqueezed_shape(self.shape, dim)
return self.__class__(
low=self.space.low.unsqueeze(dim).clone(),
high=self.space.high.unsqueeze(dim).clone(),
shape=shape,
device=self.device,
dtype=self.dtype,
)
def unbind(self, dim: int = 0):
if dim in (len(self.shape), -1):
raise ValueError(f"Final dimension of {type(self)} must remain unchanged")
orig_dim = dim
if dim < 0:
dim = len(self.shape) + dim
if dim < 0:
raise ValueError(
f"Cannot unbind along dim {orig_dim} with shape {self.shape}."
)
shape = tuple(s for i, s in enumerate(self.shape) if i != dim)
low = self.space.low.unbind(dim)
high = self.space.high.unbind(dim)
return tuple(
self.__class__(
low=low,
high=high,
shape=shape,
device=self.device,
dtype=self.dtype,
)
for low, high in zip(low, high)
)
[docs] def rand(self, shape: torch.Size = None) -> torch.Tensor:
if shape is None:
shape = _size([])
a, b = self.space
if self.dtype in (torch.float, torch.double, torch.half):
shape = [*shape, *self._safe_shape]
out = (
torch.zeros(shape, dtype=self.dtype, device=self.device).uniform_()
* (b - a)
+ a
)
if (out > b).any():
out[out > b] = b.expand_as(out)[out > b]
if (out < a).any():
out[out < a] = a.expand_as(out)[out < a]
return out
else:
if self.space.high.dtype == torch.bool:
maxi = self.space.high.int()
else:
maxi = self.space.high
if self.space.low.dtype == torch.bool:
mini = self.space.low.int()
else:
mini = self.space.low
interval = maxi - mini
r = torch.rand(_size([*shape, *self._safe_shape]), device=interval.device)
r = interval * r
r = self.space.low + r
r = r.to(self.dtype).to(self.device)
return r
def _project(self, val: torch.Tensor) -> torch.Tensor:
low = self.space.low.to(val.device)
high = self.space.high.to(val.device)
try:
val = val.clamp_(low.item(), high.item())
except ValueError:
low = low.expand_as(val)
high = high.expand_as(val)
val[val < low] = low[val < low]
val[val > high] = high[val > high]
except RuntimeError:
low = low.expand_as(val)
high = high.expand_as(val)
val[val < low] = low[val < low]
val[val > high] = high[val > high]
return val
[docs] def is_in(self, val: torch.Tensor) -> bool:
val_shape = _remove_neg_shapes(tensordict.utils._shape(val))
shape = torch.broadcast_shapes(self._safe_shape, val_shape)
shape = list(shape)
shape[-len(self.shape) :] = [
s_prev if s_prev >= 0 else s
for (s_prev, s) in zip(self.shape, shape[-len(self.shape) :])
]
shape_match = all(s1 == s2 or s1 == -1 for s1, s2 in zip(shape, val_shape))
if not shape_match:
return False
dtype_match = val.dtype == self.dtype
if not dtype_match:
return False
try:
within_bounds = (val >= self.space.low.to(val.device)).all() and (
val <= self.space.high.to(val.device)
).all()
return within_bounds
except NotImplementedError:
within_bounds = all(
(_val >= space.low.to(val.device)).all()
and (_val <= space.high.to(val.device)).all()
for (_val, space) in zip(val, self.space.unbind(0))
)
return within_bounds
except RuntimeError as err:
if "The size of tensor a" in str(err):
warnings.warn(f"Got a shape mismatch: {str(err)}")
return False
raise err
[docs] def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> Bounded:
if isinstance(dest, torch.dtype):
dest_dtype = dest
dest_device = self.device
elif dest is None:
return self
else:
dest_dtype = self.dtype
dest_device = torch.device(dest)
if dest_device == self.device and dest_dtype == self.dtype:
return self
space = self.space.to(dest_device)
return Bounded(
low=space.low,
high=space.high,
shape=self.shape,
device=dest_device,
dtype=dest_dtype,
)
[docs] def clone(self) -> Bounded:
return self.__class__(
low=self.space.low.clone(),
high=self.space.high.clone(),
shape=self.shape,
device=self.device,
dtype=self.dtype,
)
def __getitem__(self, idx: SHAPE_INDEX_TYPING):
"""Indexes the current TensorSpec based on the provided index."""
if _is_nested_list(idx):
raise NotImplementedError(
"Pending resolution of https://github.com/pytorch/pytorch/issues/100080."
)
indexed_shape = _size(_shape_indexing(self.shape, idx))
# Expand is required as pytorch.tensor indexing
return self.__class__(
low=self.space.low[idx].clone().expand(indexed_shape),
high=self.space.high[idx].clone().expand(indexed_shape),
shape=indexed_shape,
device=self.device,
dtype=self.dtype,
)
class BoundedContinuous(Bounded, metaclass=_BoundedMeta):
"""A specialized version of :class:`torchrl.data.Bounded` with continuous space."""
def __init__(
self,
low: Union[float, torch.Tensor, np.ndarray] = None,
high: Union[float, torch.Tensor, np.ndarray] = None,
shape: Optional[Union[torch.Size, int]] = None,
device: Optional[DEVICE_TYPING] = None,
dtype: Optional[Union[torch.dtype, str]] = None,
domain: str = "continuous",
):
super().__init__(
low=low, high=high, shape=shape, device=device, dtype=dtype, domain=domain
)
class BoundedDiscrete(Bounded, metaclass=_BoundedMeta):
"""A specialized version of :class:`torchrl.data.Bounded` with discrete space."""
def __init__(
self,
low: Union[float, torch.Tensor, np.ndarray] = None,
high: Union[float, torch.Tensor, np.ndarray] = None,
shape: Optional[Union[torch.Size, int]] = None,
device: Optional[DEVICE_TYPING] = None,
dtype: Optional[Union[torch.dtype, str]] = None,
domain: str = "discrete",
):
super().__init__(
low=low,
high=high,
shape=shape,
device=device,
dtype=dtype,
domain=domain,
)
def _is_nested_list(index, notuple=False):
if not notuple and isinstance(index, tuple):
for idx in index:
if _is_nested_list(idx, notuple=True):
return True
elif isinstance(index, list):
for idx in index:
if isinstance(idx, list):
return True
else:
return False
return False
[docs]class NonTensor(TensorSpec):
"""A spec for non-tensor data.
This spec has a shae, device and dtype like :class:`~tensordict.NonTensorData`.
:meth:`.rand` will return a :class:`~tensordict.NonTensorData` object with `None` data value.
(same will go for :meth:`.zero` and :meth:`.one`).
"""
def __init__(
self,
shape: Union[torch.Size, int] = _DEFAULT_SHAPE,
device: Optional[DEVICE_TYPING] = None,
dtype: torch.dtype | None = None,
**kwargs,
):
if isinstance(shape, int):
shape = _size([shape])
_, device = _default_dtype_and_device(None, device)
domain = None
super().__init__(
shape=shape, space=None, device=device, dtype=dtype, domain=domain, **kwargs
)
[docs] def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> NonTensor:
if isinstance(dest, torch.dtype):
dest_dtype = dest
dest_device = self.device
elif dest is None:
return self
else:
dest_dtype = self.dtype
dest_device = torch.device(dest)
if dest_device == self.device and dest_dtype == self.dtype:
return self
return self.__class__(shape=self.shape, device=dest_device, dtype=None)
[docs] def clone(self) -> NonTensor:
return self.__class__(shape=self.shape, device=self.device, dtype=self.dtype)
[docs] def rand(self, shape=None):
if shape is None:
shape = ()
return NonTensorData(
data=None, batch_size=(*shape, *self._safe_shape), device=self.device
)
[docs] def zero(self, shape=None):
if shape is None:
shape = ()
return NonTensorData(
data=None, batch_size=(*shape, *self._safe_shape), device=self.device
)
[docs] def one(self, shape=None):
if shape is None:
shape = ()
return NonTensorData(
data=None, batch_size=(*shape, *self._safe_shape), device=self.device
)
[docs] def is_in(self, val: torch.Tensor) -> bool:
shape = torch.broadcast_shapes(self._safe_shape, val.shape)
return (
isinstance(val, NonTensorData)
and val.shape == shape
# We relax constrains on device as they're hard to enforce for non-tensor
# tensordicts and pointless
# and val.device == self.device
and val.dtype == self.dtype
)
[docs] def expand(self, *shape):
if len(shape) == 1 and isinstance(shape[0], (tuple, list, torch.Size)):
shape = shape[0]
shape = _size(shape)
if not all(
(old == 1) or (old == new)
for old, new in zip(self.shape, shape[-len(self.shape) :])
):
raise ValueError(
f"The last elements of the expanded shape must match the current one. Got shape={shape} while self.shape={self.shape}."
)
return self.__class__(shape=shape, device=self.device, dtype=None)
def _reshape(self, shape):
return self.__class__(shape=shape, device=self.device, dtype=self.dtype)
def _unflatten(self, dim, sizes):
shape = torch.zeros(self.shape, device="meta").unflatten(dim, sizes).shape
return self.__class__(
shape=shape,
device=self.device,
dtype=self.dtype,
)
def __getitem__(self, idx: SHAPE_INDEX_TYPING):
"""Indexes the current TensorSpec based on the provided index."""
indexed_shape = _size(_shape_indexing(self.shape, idx))
return self.__class__(shape=indexed_shape, device=self.device, dtype=self.dtype)
def unbind(self, dim: int = 0):
orig_dim = dim
if dim < 0:
dim = len(self.shape) + dim
if dim < 0:
raise ValueError(
f"Cannot unbind along dim {orig_dim} with shape {self.shape}."
)
shape = tuple(s for i, s in enumerate(self.shape) if i != dim)
return tuple(
self.__class__(
shape=shape,
device=self.device,
dtype=self.dtype,
)
for i in range(self.shape[dim])
)
class _UnboundedMeta(abc.ABCMeta):
def __call__(cls, *args, **kwargs):
instance = super().__call__(*args, **kwargs)
if instance.domain == "continuous":
instance.__class__ = UnboundedContinuous
else:
instance.__class__ = UnboundedDiscrete
return instance
[docs]@dataclass(repr=False)
class Unbounded(TensorSpec, metaclass=_UnboundedMeta):
"""An unbounded tensor spec.
``Unbounded`` specs will never appear as such and always be subclassed as :class:`UnboundedContinuous`
or :class:`UnboundedDiscrete` depending on their dtype (floating points dtypes will result in
:class:`UnboundedContinuous` instances, all others in :class:`UnboundedDiscrete` instances).
Although it is not properly limited above and below, this class still has a :attr:`Box` space that encodes
the maximum and minimum value that the dtype accepts.
Args:
shape (torch.Size): the shape of the ``Bounded`` spec. The shape must be specified.
Inputs ``low``, ``high`` and ``shape`` must be broadcastable.
device (str, int or torch.device, optional): device of the tensors.
dtype (str or torch.dtype, optional): dtype of the tensors.
domain (str): `"continuous"` or `"discrete"`. Can be used to override the automatic type assignment.
Examples:
>>> spec = Unbounded(shape=(), dtype=torch.float)
>>> spec
UnboundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous)
>>> spec = Unbounded(shape=(), dtype=torch.int)
>>> spec
UnboundedDiscrete(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int32, contiguous=True)),
device=cpu,
dtype=torch.int32,
domain=discrete)
>>> spec.to(torch.float)
UnboundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous)
>>> spec = Unbounded(shape=(), dtype=torch.int, domain="continuous")
>>> spec
UnboundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int32, contiguous=True)),
device=cpu,
dtype=torch.int32,
domain=continuous)
"""
def __init__(
self,
shape: Union[torch.Size, int] = _DEFAULT_SHAPE,
device: Optional[DEVICE_TYPING] = None,
dtype: Optional[Union[str, torch.dtype]] = None,
**kwargs,
):
if isinstance(shape, int):
shape = _size([shape])
dtype, device = _default_dtype_and_device(dtype, device)
if dtype == torch.bool:
min_value = False
max_value = True
default_domain = "discrete"
else:
if dtype.is_floating_point:
min_value = torch.finfo(dtype).min
max_value = torch.finfo(dtype).max
default_domain = "continuous"
else:
min_value = torch.iinfo(dtype).min
max_value = torch.iinfo(dtype).max
default_domain = "discrete"
box = ContinuousBox(
torch.full(
_remove_neg_shapes(shape), min_value, device=device, dtype=dtype
),
torch.full(
_remove_neg_shapes(shape), max_value, device=device, dtype=dtype
),
)
domain = kwargs.pop("domain", default_domain)
super().__init__(
shape=shape, space=box, device=device, dtype=dtype, domain=domain, **kwargs
)
[docs] def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> Unbounded:
if isinstance(dest, torch.dtype):
dest_dtype = dest
dest_device = self.device
elif dest is None:
return self
else:
dest_dtype = self.dtype
dest_device = torch.device(dest)
if dest_device == self.device and dest_dtype == self.dtype:
return self
return Unbounded(shape=self.shape, device=dest_device, dtype=dest_dtype)
[docs] def clone(self) -> Unbounded:
return self.__class__(shape=self.shape, device=self.device, dtype=self.dtype)
[docs] def rand(self, shape: torch.Size = None) -> torch.Tensor:
if shape is None:
shape = _size([])
shape = [*shape, *self.shape]
if self.dtype.is_floating_point:
return torch.randn(shape, device=self.device, dtype=self.dtype)
return torch.empty(shape, device=self.device, dtype=self.dtype).random_()
[docs] def is_in(self, val: torch.Tensor) -> bool:
shape = torch.broadcast_shapes(self._safe_shape, val.shape)
return val.shape == shape and val.dtype == self.dtype
def _project(self, val: torch.Tensor) -> torch.Tensor:
return torch.as_tensor(val, dtype=self.dtype).reshape(self.shape)
[docs] def enumerate(self) -> Any:
raise NotImplementedError("enumerate cannot be called with continuous specs.")
[docs] def expand(self, *shape):
if len(shape) == 1 and isinstance(shape[0], (tuple, list, torch.Size)):
shape = shape[0]
# TODO: this blocks batched envs which expand shapes
# if any(val < 0 for val in shape):
# raise ValueError(
# f"{self.__class__.__name__}.expand does not support negative shapes."
# )
if any(s1 != s2 and s2 != 1 for s1, s2 in zip(shape[-self.ndim :], self.shape)):
raise ValueError(
f"The last {self.ndim} of the expanded shape {shape} must match the"
f"shape of the {self.__class__.__name__} spec in expand()."
)
return self.__class__(shape=shape, device=self.device, dtype=self.dtype)
def _reshape(self, shape):
return self.__class__(shape=shape, device=self.device, dtype=self.dtype)
def _unflatten(self, dim, sizes):
shape = torch.zeros(self.shape, device="meta").unflatten(dim, sizes).shape
return self.__class__(
shape=shape,
device=self.device,
dtype=self.dtype,
)
def __getitem__(self, idx: SHAPE_INDEX_TYPING):
"""Indexes the current TensorSpec based on the provided index."""
indexed_shape = _size(_shape_indexing(self.shape, idx))
return self.__class__(shape=indexed_shape, device=self.device, dtype=self.dtype)
def unbind(self, dim: int = 0):
orig_dim = dim
if dim < 0:
dim = len(self.shape) + dim
if dim < 0:
raise ValueError(
f"Cannot unbind along dim {orig_dim} with shape {self.shape}."
)
shape = tuple(s for i, s in enumerate(self.shape) if i != dim)
return tuple(
self.__class__(
shape=shape,
device=self.device,
dtype=self.dtype,
)
for i in range(self.shape[dim])
)
def __eq__(self, other):
# those specs are equivalent to a discrete spec
if isinstance(other, Bounded):
minval, maxval = _minmax_dtype(self.dtype)
minval = torch.as_tensor(minval).to(self.device, self.dtype)
maxval = torch.as_tensor(maxval).to(self.device, self.dtype)
return (
Bounded(
shape=self.shape,
high=maxval,
low=minval,
dtype=self.dtype,
device=self.device,
domain=self.domain,
)
== other
)
elif isinstance(other, Unbounded):
if self.dtype != other.dtype:
return False
if self.shape != other.shape:
return False
if self.device != other.device:
return False
return True
return super().__eq__(other)
[docs]class UnboundedContinuous(Unbounded):
"""A specialized version of :class:`torchrl.data.Unbounded` with continuous space."""
...
[docs]class UnboundedDiscrete(Unbounded):
"""A specialized version of :class:`torchrl.data.Unbounded` with discrete space."""
def __init__(
self,
shape: Union[torch.Size, int] = _DEFAULT_SHAPE,
device: Optional[DEVICE_TYPING] = None,
dtype: Optional[Union[str, torch.dtype]] = torch.int64,
**kwargs,
):
super().__init__(shape=shape, device=device, dtype=dtype, **kwargs)
[docs]@dataclass(repr=False)
class MultiOneHot(OneHot):
"""A concatenation of one-hot discrete tensor spec.
This class can be used when a single tensor must carry information about multiple one-hot encoded
values.
The last dimension of the shape (domain of the tensor elements) cannot be indexed.
Args:
nvec (iterable of integers): cardinality of each of the elements of
the tensor.
shape (torch.Size, optional): total shape of the sampled tensors.
If provided, the last dimension must match sum(nvec).
device (str, int or torch.device, optional): device of
the tensors.
dtype (str or torch.dtype, optional): dtype of the tensors.
mask (torch.Tensor or None): mask some of the possible outcomes when a
sample is taken. See :meth:`~.update_mask` for more information.
Examples:
>>> ts = MultiOneHot((3,2,3))
>>> ts.rand()
tensor([ True, False, False, True, False, False, False, True])
>>> ts.is_in(torch.tensor([
... 0, 0, 1,
... 0, 1,
... 1, 0, 0], dtype=torch.bool))
True
>>> ts.is_in(torch.tensor([
... 1, 0, 1,
... 0, 1,
... 1, 0, 0], dtype=torch.bool))
False
"""
def __init__(
self,
nvec: Sequence[int],
shape: Optional[torch.Size] = None,
device=None,
dtype=torch.bool,
use_register=False,
mask: torch.Tensor | None = None,
):
self.nvec = nvec
dtype, device = _default_dtype_and_device(dtype, device)
if shape is None:
shape = _size((sum(nvec),))
else:
shape = _size(shape)
if shape[-1] != sum(nvec):
raise ValueError(
f"The last value of the shape must match sum(nvec) for transform of type {self.__class__}. "
f"Got sum(nvec)={sum(nvec)} and shape={shape}."
)
space = BoxList([CategoricalBox(n) for n in nvec])
self.use_register = use_register
super(OneHot, self).__init__(
shape,
space,
device,
dtype,
domain="discrete",
)
self.update_mask(mask)
[docs] def enumerate(self) -> torch.Tensor:
nvec = self.nvec
enum_disc = self.to_categorical_spec().enumerate()
enums = torch.cat(
[
torch.nn.functional.one_hot(enum_unb, nv).to(self.dtype)
for nv, enum_unb in zip(nvec, enum_disc.unbind(-1))
],
-1,
)
return enums
[docs] def update_mask(self, mask):
"""Sets a mask to prevent some of the possible outcomes when a sample is taken.
The mask can also be set during initialization of the spec.
Args:
mask (torch.Tensor or None): boolean mask. If None, the mask is
disabled. Otherwise, the shape of the mask must be expandable to
the shape of the spec. ``False`` masks an outcome and ``True``
leaves the outcome unmasked. If all of the possible outcomes are
masked, then an error is raised when a sample is taken.
Examples:
>>> mask = torch.tensor([True, False, False,
... True, True])
>>> ts = MultiOneHot((3, 2), (2, 5), dtype=torch.int64, mask=mask)
>>> # All but one of the three possible outcomes for the first
>>> # one-hot group are masked, but neither of the two possible
>>> # outcomes for the second one-hot group are masked.
>>> ts.rand()
tensor([[1, 0, 0, 0, 1],
[1, 0, 0, 1, 0]])
"""
if mask is not None:
try:
mask = mask.expand(*self._safe_shape)
except RuntimeError as err:
raise RuntimeError("Cannot expand mask to the desired shape.") from err
if mask.dtype != torch.bool:
raise ValueError("Only boolean masks are accepted.")
self.mask = mask
[docs] def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> MultiOneHot:
if isinstance(dest, torch.dtype):
dest_dtype = dest
dest_device = self.device
elif dest is None:
return self
else:
dest_dtype = self.dtype
dest_device = torch.device(dest)
if dest_device == self.device and dest_dtype == self.dtype:
return self
return MultiOneHot(
nvec=deepcopy(self.nvec),
shape=self.shape,
device=dest_device,
dtype=dest_dtype,
mask=self.mask.to(dest) if self.mask is not None else None,
)
[docs] def clone(self) -> MultiOneHot:
return self.__class__(
nvec=deepcopy(self.nvec),
shape=self.shape,
device=self.device,
dtype=self.dtype,
mask=self.mask.clone() if self.mask is not None else None,
)
def __eq__(self, other):
if not hasattr(other, "mask"):
return False
mask_equal = (self.mask is None and other.mask is None) or (
isinstance(self.mask, torch.Tensor)
and isinstance(other.mask, torch.Tensor)
and (self.mask.shape == other.mask.shape)
and (self.mask == other.mask).all()
)
return (
type(self) == type(other)
and self.shape == other.shape
and self.space == other.space
and self.device == other.device
and self.dtype == other.dtype
and self.domain == other.domain
and self.use_register == other.use_register
and mask_equal
)
[docs] def rand(self, shape: Optional[torch.Size] = None) -> torch.Tensor:
if shape is None:
shape = self.shape[:-1]
else:
shape = _size([*shape, *self.shape[:-1]])
mask = self.mask
if mask is None:
x = torch.cat(
[
torch.nn.functional.one_hot(
torch.randint(
space.n,
(
*shape,
1,
),
device=self.device,
),
space.n,
).to(self.dtype)
for space in self.space
],
-1,
).squeeze(-2)
return x
mask = mask.expand(_remove_neg_shapes(*shape, mask.shape[-1]))
mask_splits = torch.split(mask, [space.n for space in self.space], -1)
out = []
for _mask in mask_splits:
if mask.ndim > 2:
mask_flat = torch.flatten(_mask, 0, -2)
else:
mask_flat = _mask
shape_out = _mask.shape[:-1]
m = torch.multinomial(mask_flat.float(), 1).reshape(shape_out)
m = torch.nn.functional.one_hot(m, _mask.shape[-1]).to(self.dtype)
out.append(m)
return torch.cat(out, -1)
[docs] def encode(
self, val: Union[np.ndarray, torch.Tensor], *, ignore_device: bool = False
) -> torch.Tensor:
if not isinstance(val, torch.Tensor):
if not ignore_device:
val = torch.tensor(val, device=self.device)
else:
val = torch.as_tensor(val)
x = []
for v, space in zip(val.unbind(-1), self.space):
if not (v < space.n).all():
raise RuntimeError(
f"value {v} is greater than the allowed max {space.n}"
)
x.append(
super(MultiOneHot, self).encode(v, space, ignore_device=ignore_device)
)
return torch.cat(x, -1).reshape(self.shape)
def _split(self, val: torch.Tensor) -> Optional[torch.Tensor]:
split_sizes = [space.n for space in self.space]
if val.ndim < 1 or val.shape[-1] != sum(split_sizes):
return None
return val.split(split_sizes, dim=-1)
[docs] def index(self, index: INDEX_TYPING, tensor_to_index: torch.Tensor) -> torch.Tensor:
if not isinstance(index, torch.Tensor):
raise ValueError(
f"Only tensors are allowed for indexing using"
f" {self.__class__.__name__}.index(...)"
)
indices = self._split(index)
tensor_to_index = self._split(tensor_to_index)
out = []
for _index, _tensor_to_index in zip(indices, tensor_to_index):
_index = _index.nonzero().squeeze()
_index = _index.expand((*_tensor_to_index.shape[:-1], _index.shape[-1]))
out.append(_tensor_to_index.gather(-1, _index))
return torch.cat(out, -1)
[docs] def is_in(self, val: torch.Tensor) -> bool:
vals = self._split(val)
if vals is None:
return False
return all(spec.is_in(val) for val, spec in zip(vals, self._split_self()))
def _project(self, val: torch.Tensor) -> torch.Tensor:
vals = self._split(val)
return torch.cat(
[spec._project(val) for val, spec in zip(vals, self._split_self())], -1
)
def _split_self(self):
result = []
device = self.device
dtype = self.dtype
use_register = self.use_register
mask = (
self.mask.split([space.n for space in self.space], -1)
if self.mask is not None
else [None] * len(self.space)
)
for _mask, space in zip(mask, self.space):
n = space.n
shape = self.shape[:-1] + (n,)
result.append(
OneHot(
n=n,
shape=shape,
device=device,
dtype=dtype,
use_register=use_register,
mask=_mask,
)
)
return result
[docs] def to_categorical(self, val: torch.Tensor, safe: bool = None) -> torch.Tensor:
"""Converts a given one-hot tensor in categorical format.
Args:
val (torch.Tensor, optional): One-hot tensor to convert in categorical format.
safe (bool): boolean value indicating whether a check should be
performed on the value against the domain of the spec.
Defaults to the value of the ``CHECK_SPEC_ENCODE`` environment variable.
Returns:
The categorical tensor.
Examples:
>>> mone_hot = MultiOneHot((2, 3, 4))
>>> onehot_sample = mone_hot.rand()
>>> onehot_sample
tensor([False, True, False, False, True, False, True, False, False])
>>> categ_sample = mone_hot.to_categorical(onehot_sample)
>>> categ_sample
tensor([1, 2, 1])
"""
if safe is None:
safe = _CHECK_SPEC_ENCODE
if safe:
self.assert_is_in(val)
vals = self._split(val)
return torch.stack([val.long().argmax(-1) for val in vals], -1)
[docs] def to_categorical_spec(self) -> MultiCategorical:
"""Converts the spec to the equivalent categorical spec.
Examples:
>>> mone_hot = MultiOneHot((2, 3, 4))
>>> categ = mone_hot.to_categorical_spec()
>>> categ
MultiCategorical(
shape=torch.Size([3]),
space=BoxList(boxes=[CategoricalBox(n=2), CategoricalBox(n=3), CategoricalBox(n=4)]),
device=cpu,
dtype=torch.int64,
domain=discrete)
"""
return MultiCategorical(
[_space.n for _space in self.space],
device=self.device,
shape=[*self.shape[:-1], len(self.space)],
mask=self.mask,
)
[docs] def to_one_hot(self, val: torch.Tensor, safe: bool = None) -> torch.Tensor:
"""No-op for MultiOneHot."""
return val
[docs] def expand(self, *shape):
nvecs = [space.n for space in self.space]
if len(shape) == 1 and isinstance(shape[0], (tuple, list, torch.Size)):
shape = shape[0]
if any(s1 != s2 and s2 != 1 for s1, s2 in zip(shape[-self.ndim :], self.shape)):
raise ValueError(
f"The last {self.ndim} of the expanded shape {shape} must match the"
f"shape of the {self.__class__.__name__} spec in expand()."
)
mask = self.mask.expand(shape) if self.mask is not None else None
return self.__class__(
nvec=nvecs,
shape=shape,
device=self.device,
dtype=self.dtype,
mask=mask,
)
def _reshape(self, shape):
nvecs = [space.n for space in self.space]
mask = self.mask.reshape(shape) if self.mask is not None else None
return self.__class__(
nvec=nvecs,
shape=shape,
device=self.device,
dtype=self.dtype,
mask=mask,
)
def _unflatten(self, dim, sizes):
nvecs = [space.n for space in self.space]
shape = torch.zeros(self.shape, device="meta").unflatten(dim, sizes).shape
mask = self.mask.reshape(shape) if self.mask is not None else None
return self.__class__(
nvec=nvecs,
shape=shape,
device=self.device,
dtype=self.dtype,
mask=mask,
)
[docs] def squeeze(self, dim=None):
if self.shape[-1] == 1 and dim in (len(self.shape), -1, None):
raise ValueError(f"Final dimension of {type(self)} must remain unchanged")
shape = _squeezed_shape(self.shape, dim)
if shape is None:
return self
mask = self.mask.reshape(shape) if self.mask is not None else None
return self.__class__(
nvec=self.nvec,
shape=shape,
device=self.device,
dtype=self.dtype,
mask=mask,
)
[docs] def unsqueeze(self, dim: int):
if dim in (len(self.shape), -1):
raise ValueError(f"Final dimension of {type(self)} must remain unchanged")
shape = _unsqueezed_shape(self.shape, dim)
mask = self.mask.reshape(shape) if self.mask is not None else None
return self.__class__(
nvec=self.nvec, shape=shape, device=self.device, dtype=self.dtype, mask=mask
)
def unbind(self, dim: int = 0):
if dim in (len(self.shape), -1):
raise ValueError(f"Final dimension of {type(self)} must remain unchanged")
orig_dim = dim
if dim < 0:
dim = len(self.shape) + dim
if dim < 0:
raise ValueError(
f"Cannot unbind along dim {orig_dim} with shape {self.shape}."
)
shape = tuple(s for i, s in enumerate(self.shape) if i != dim)
mask = self.mask
if mask is None:
mask = (None,) * self.shape[dim]
else:
mask = mask.unbind(dim)
return tuple(
self.__class__(
nvec=self.nvec,
shape=shape,
device=self.device,
dtype=self.dtype,
mask=mask[i],
)
for i in range(self.shape[dim])
)
def __getitem__(self, idx: SHAPE_INDEX_TYPING):
"""Indexes the current TensorSpec based on the provided index.
The last dimension of the spec corresponding to the domain of the tensor elements is non-indexable.
"""
indexed_shape = _shape_indexing(self.shape[:-1], idx)
return self.__class__(
nvec=self.nvec,
shape=_size(indexed_shape + [self.shape[-1]]),
device=self.device,
dtype=self.dtype,
)
[docs]class Categorical(TensorSpec):
"""A discrete tensor spec.
An alternative to :class:`OneHot` for categorical variables in TorchRL.
Categorical variables perform indexing insted of masking, which can speed-up
computation and reduce memory cost for large categorical variables.
The spec will have the shape defined by the ``shape`` argument: if a singleton dimension is
desired for the training dimension, one should specify it explicitly.
Args:
n (int): number of possible outcomes.
shape: (torch.Size, optional): shape of the variable, default is "torch.Size([])".
device (str, int or torch.device, optional): device of the tensors.
dtype (str or torch.dtype, optional): dtype of the tensors.
mask (torch.Tensor or None): mask some of the possible outcomes when a
sample is taken. See :meth:`~.update_mask` for more information.
Examples:
>>> categ = Categorical(3)
>>> categ
Categorical(
shape=torch.Size([]),
space=CategoricalBox(n=3),
device=cpu,
dtype=torch.int64,
domain=discrete)
>>> categ.rand()
tensor(2)
>>> categ = Categorical(3, shape=(1,))
>>> categ
Categorical(
shape=torch.Size([1]),
space=CategoricalBox(n=3),
device=cpu,
dtype=torch.int64,
domain=discrete)
>>> categ.rand()
tensor([1])
"""
shape: torch.Size
space: CategoricalBox
device: torch.device | None = None
dtype: torch.dtype = torch.float
domain: str = ""
# SPEC_HANDLED_FUNCTIONS = {}
def __init__(
self,
n: int,
shape: torch.Size | None = None,
device: DEVICE_TYPING | None = None,
dtype: str | torch.dtype = torch.long,
mask: torch.Tensor | None = None,
):
if shape is None:
shape = _size([])
dtype, device = _default_dtype_and_device(dtype, device)
space = CategoricalBox(n)
super().__init__(
shape=shape, space=space, device=device, dtype=dtype, domain="discrete"
)
self.update_mask(mask)
[docs] def enumerate(self) -> torch.Tensor:
arange = torch.arange(self.n, dtype=self.dtype, device=self.device)
if self.ndim:
arange = arange.view(-1, *(1,) * self.ndim)
return arange.expand(self.n, *self.shape)
@property
def n(self):
return self.space.n
[docs] def update_mask(self, mask):
"""Sets a mask to prevent some of the possible outcomes when a sample is taken.
The mask can also be set during initialization of the spec.
Args:
mask (torch.Tensor or None): boolean mask. If None, the mask is
disabled. Otherwise, the shape of the mask must be expandable to
the shape of the equivalent one-hot spec. ``False`` masks an
outcome and ``True`` leaves the outcome unmasked. If all of the
possible outcomes are masked, then an error is raised when a
sample is taken.
Examples:
>>> mask = torch.tensor([True, False, True])
>>> ts = Categorical(3, (10,), dtype=torch.int64, mask=mask)
>>> # One of the three possible outcomes is masked
>>> ts.rand()
tensor([0, 2, 2, 0, 2, 0, 2, 2, 0, 2])
"""
if mask is not None:
try:
mask = mask.expand(_remove_neg_shapes(*self.shape, self.space.n))
except RuntimeError as err:
raise RuntimeError("Cannot expand mask to the desired shape.") from err
if mask.dtype != torch.bool:
raise ValueError("Only boolean masks are accepted.")
self.mask = mask
[docs] def rand(self, shape: torch.Size = None) -> torch.Tensor:
if shape is None:
shape = _size([])
if self.mask is None:
return torch.randint(
0,
self.space.n,
_size([*shape, *self.shape]),
device=self.device,
dtype=self.dtype,
)
mask = self.mask
mask = mask.expand(_remove_neg_shapes(*shape, *mask.shape))
if mask.ndim > 2:
mask_flat = torch.flatten(mask, 0, -2)
else:
mask_flat = mask
shape_out = mask.shape[:-1]
out = torch.multinomial(mask_flat.float(), 1).reshape(shape_out)
return out
def _project(self, val: torch.Tensor) -> torch.Tensor:
if val.dtype not in (torch.int, torch.long):
val = torch.round(val)
if self.mask is None:
return val.clamp_(min=0, max=self.space.n - 1)
shape = self.mask.shape
shape = _size([*torch.broadcast_shapes(shape[:-1], val.shape), shape[-1]])
mask_expand = self.mask.expand(shape)
gathered = mask_expand.gather(-1, val.unsqueeze(-1))
oob = ~gathered.all(-1)
new_val = torch.multinomial(mask_expand[oob].float(), 1).squeeze(-1)
val = torch.masked_scatter(val, oob, new_val)
return val
[docs] def is_in(self, val: torch.Tensor) -> bool:
if self.mask is None:
shape = torch.broadcast_shapes(self._safe_shape, val.shape)
shape_match = val.shape == shape
if not shape_match:
return False
dtype_match = val.dtype == self.dtype
if not dtype_match:
return False
return (0 <= val).all() and (val < self.space.n).all()
shape = self.mask.shape
shape = _size([*torch.broadcast_shapes(shape[:-1], val.shape), shape[-1]])
mask_expand = self.mask.expand(shape)
gathered = mask_expand.gather(-1, val.unsqueeze(-1))
return gathered.all()
def __getitem__(self, idx: SHAPE_INDEX_TYPING):
"""Indexes the current TensorSpec based on the provided index."""
indexed_shape = _size(_shape_indexing(self.shape, idx))
return self.__class__(
n=self.space.n,
shape=indexed_shape,
device=self.device,
dtype=self.dtype,
)
def __eq__(self, other):
if not hasattr(other, "mask"):
return False
mask_equal = (self.mask is None and other.mask is None) or (
isinstance(self.mask, torch.Tensor)
and isinstance(other.mask, torch.Tensor)
and (self.mask.shape == other.mask.shape)
and (self.mask == other.mask).all()
)
return (
type(self) == type(other)
and self.shape == other.shape
and self.space == other.space
and self.device == other.device
and self.dtype == other.dtype
and self.domain == other.domain
and mask_equal
)
[docs] def to_numpy(self, val: torch.Tensor, safe: bool = None) -> dict:
if safe is None:
safe = _CHECK_SPEC_ENCODE
# if not val.shape and not safe:
# return val.item()
return super().to_numpy(val, safe)
[docs] def to_one_hot(self, val: torch.Tensor, safe: bool = None) -> torch.Tensor:
"""Encodes a discrete tensor from the spec domain into its one-hot correspondent.
Args:
val (torch.Tensor, optional): Tensor to one-hot encode.
safe (bool): boolean value indicating whether a check should be
performed on the value against the domain of the spec.
Defaults to the value of the ``CHECK_SPEC_ENCODE`` environment variable.
Returns:
The one-hot encoded tensor.
Examples:
>>> categ = Categorical(3)
>>> categ_sample = categ.zero()
>>> categ_sample
tensor(0)
>>> onehot_sample = categ.to_one_hot(categ_sample)
>>> onehot_sample
tensor([ True, False, False])
"""
if safe is None:
safe = _CHECK_SPEC_ENCODE
if safe:
self.assert_is_in(val)
return torch.nn.functional.one_hot(val, self.space.n).bool()
[docs] def to_categorical(self, val: torch.Tensor, safe: bool = None) -> torch.Tensor:
"""No-op for categorical."""
return val
[docs] def to_one_hot_spec(self) -> OneHot:
"""Converts the spec to the equivalent one-hot spec.
Examples:
>>> categ = Categorical(3)
>>> categ.to_one_hot_spec()
OneHot(
shape=torch.Size([3]),
space=CategoricalBox(n=3),
device=cpu,
dtype=torch.bool,
domain=discrete)
"""
shape = [*self.shape, self.space.n]
return OneHot(
n=self.space.n,
shape=shape,
device=self.device,
)
[docs] def expand(self, *shape):
if len(shape) == 1 and isinstance(shape[0], (tuple, list, torch.Size)):
shape = shape[0]
if any(s1 != s2 and s2 != 1 for s1, s2 in zip(shape[-self.ndim :], self.shape)):
raise ValueError(
f"The last {self.ndim} of the expanded shape {shape} must match the"
f"shape of the {self.__class__.__name__} spec in expand()."
)
return self.__class__(
n=self.space.n, shape=shape, device=self.device, dtype=self.dtype
)
def _reshape(self, shape):
return self.__class__(
n=self.space.n, shape=shape, device=self.device, dtype=self.dtype
)
def _unflatten(self, dim, sizes):
shape = torch.zeros(self.shape, device="meta").unflatten(dim, sizes).shape
return self.__class__(
n=self.space.n, shape=shape, device=self.device, dtype=self.dtype
)
[docs] def squeeze(self, dim=None):
shape = _squeezed_shape(self.shape, dim)
mask = self.mask
if mask is not None:
mask = mask.view(*shape, mask.shape[-1])
if shape is None:
return self
return self.__class__(
n=self.space.n,
shape=shape,
device=self.device,
dtype=self.dtype,
mask=mask,
)
[docs] def unsqueeze(self, dim: int):
shape = _unsqueezed_shape(self.shape, dim)
mask = self.mask
if mask is not None:
mask = mask.view(*shape, mask.shape[-1])
return self.__class__(
n=self.space.n,
shape=shape,
device=self.device,
dtype=self.dtype,
mask=mask,
)
def unbind(self, dim: int = 0):
orig_dim = dim
if dim < 0:
dim = len(self.shape) + dim
if dim < 0:
raise ValueError(
f"Cannot unbind along dim {orig_dim} with shape {self.shape}."
)
shape = tuple(s for i, s in enumerate(self.shape) if i != dim)
mask = self.mask
if mask is None:
mask = (None,) * self.shape[dim]
else:
mask = mask.unbind(dim)
return tuple(
self.__class__(
n=self.space.n,
shape=shape,
device=self.device,
dtype=self.dtype,
mask=mask[i],
)
for i in range(self.shape[dim])
)
[docs] def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> Categorical:
if isinstance(dest, torch.dtype):
dest_dtype = dest
dest_device = self.device
elif dest is None:
return self
else:
dest_dtype = self.dtype
dest_device = torch.device(dest)
if dest_device == self.device and dest_dtype == self.dtype:
return self
return self.__class__(
n=self.space.n, shape=self.shape, device=dest_device, dtype=dest_dtype
)
[docs] def clone(self) -> Categorical:
return self.__class__(
n=self.space.n,
shape=self.shape,
device=self.device,
dtype=self.dtype,
mask=self.mask.clone() if self.mask is not None else None,
)
[docs]@dataclass(repr=False)
class Binary(Categorical):
"""A binary discrete tensor spec.
A binary tensor spec encodes tensors of arbitrary size where the values are either 0 or 1 (or ``True`` or ``False``
if the dtype it ``torch.bool``).
Unlike :class:`OneHot`, `Binary` can have more than one non-null element along the last dimension.
Args:
n (int): length of the binary vector. If provided along with ``shape``, ``shape[-1]`` must match ``n``.
If not provided, ``shape`` must be passed.
.. warning:: the ``n`` argument from ``Binary`` must not be confused with the ``n`` argument from :class:`Categorical`
or :class:`OneHot` which denotes the maximum nmber of elements that can be sampled.
For clarity, use ``shape`` instead.
shape (torch.Size, optional): total shape of the sampled tensors.
If provided, the last dimension must match ``n``.
device (str, int or torch.device, optional): device of the tensors.
dtype (str or torch.dtype, optional): dtype of the tensors.
Defaults to ``torch.int8``.
Examples:
>>> torch.manual_seed(0)
>>> spec = Binary(n=4, shape=(2, 4))
>>> print(spec.rand())
tensor([[0, 1, 1, 0],
[1, 1, 1, 1]], dtype=torch.int8)
>>> spec = Binary(shape=(2, 4))
>>> print(spec.rand())
tensor([[1, 1, 1, 0],
[0, 1, 0, 0]], dtype=torch.int8)
>>> spec = Binary(n=4)
>>> print(spec.rand())
tensor([0, 0, 0, 1], dtype=torch.int8)
"""
def __init__(
self,
n: int | None = None,
shape: Optional[torch.Size] = None,
device: Optional[DEVICE_TYPING] = None,
dtype: Union[str, torch.dtype] = torch.int8,
):
if n is None and not shape:
raise TypeError("Must provide either n or shape.")
if n is None:
n = shape[-1]
if shape is None or not len(shape):
shape = _size((n,))
else:
shape = _size(shape)
if shape[-1] != n:
raise ValueError(
f"The last value of the shape must match n for spec {self.__class__}. "
f"Got n={n} and shape={shape}."
)
super().__init__(n=2, shape=shape, device=device, dtype=dtype)
[docs] def expand(self, *shape):
if len(shape) == 1 and isinstance(shape[0], (tuple, list, torch.Size)):
shape = shape[0]
if any(val < 0 for val in shape):
raise ValueError(
f"{self.__class__.__name__}.expand does not support negative shapes."
)
if any(s1 != s2 and s2 != 1 for s1, s2 in zip(shape[-self.ndim :], self.shape)):
raise ValueError(
f"The last {self.ndim} of the expanded shape {shape} must match the"
f"shape of the {self.__class__.__name__} spec in expand()."
)
return self.__class__(
n=self.shape[-1], shape=shape, device=self.device, dtype=self.dtype
)
def _reshape(self, shape):
return self.__class__(
n=self.shape[-1], shape=shape, device=self.device, dtype=self.dtype
)
def _unflatten(self, dim, sizes):
shape = torch.zeros(self.shape, device="meta").unflatten(dim, sizes).shape
return self.__class__(
n=self.shape[-1], shape=shape, device=self.device, dtype=self.dtype
)
[docs] def squeeze(self, dim=None):
shape = _squeezed_shape(self.shape, dim)
if shape is None:
return self
return self.__class__(
n=self.shape[-1], shape=shape, device=self.device, dtype=self.dtype
)
[docs] def unsqueeze(self, dim: int):
shape = _unsqueezed_shape(self.shape, dim)
return self.__class__(
n=self.shape[-1], shape=shape, device=self.device, dtype=self.dtype
)
def unbind(self, dim: int = 0):
if dim in (len(self.shape) - 1, -1):
raise ValueError(f"Final dimension of {type(self)} must remain unchanged")
orig_dim = dim
if dim < 0:
dim = len(self.shape) + dim
if dim < 0:
raise ValueError(
f"Cannot unbind along dim {orig_dim} with shape {self.shape}."
)
shape = tuple(s for i, s in enumerate(self.shape) if i != dim)
return tuple(
self.__class__(
n=self.shape[-1], shape=shape, device=self.device, dtype=self.dtype
)
for i in range(self.shape[dim])
)
[docs] def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> Binary:
if isinstance(dest, torch.dtype):
dest_dtype = dest
dest_device = self.device
elif dest is None:
return self
else:
dest_dtype = self.dtype
dest_device = torch.device(dest)
if dest_device == self.device and dest_dtype == self.dtype:
return self
return self.__class__(
n=self.shape[-1], shape=self.shape, device=dest_device, dtype=dest_dtype
)
[docs] def clone(self) -> Binary:
return self.__class__(
n=self.shape[-1],
shape=self.shape,
device=self.device,
dtype=self.dtype,
)
def __getitem__(self, idx: SHAPE_INDEX_TYPING):
"""Indexes the current TensorSpec based on the provided index.
The last dimension of the spec (length n of the binary vector) cannot be indexed.
"""
indexed_shape = _shape_indexing(self.shape[:-1], idx)
return self.__class__(
n=self.shape[-1],
shape=_size(indexed_shape + [self.shape[-1]]),
device=self.device,
dtype=self.dtype,
)
def __eq__(self, other):
if not isinstance(other, Binary):
if isinstance(other, Categorical):
return (
other.n == 2
and other.device == self.device
and other.shape == self.shape
and other.dtype == self.dtype
)
return False
return super().__eq__(other)
[docs]@dataclass(repr=False)
class MultiCategorical(Categorical):
"""A concatenation of discrete tensor spec.
Args:
nvec (iterable of integers or torch.Tensor): cardinality of each of the elements of
the tensor. Can have several axes.
shape (torch.Size, optional): total shape of the sampled tensors.
If provided, the last m dimensions must match nvec.shape.
device (str, int or torch.device, optional): device of
the tensors.
dtype (str or torch.dtype, optional): dtype of the tensors.
remove_singleton (bool, optional): if ``True``, singleton samples (of size [1])
will be squeezed. Defaults to ``True``.
mask (torch.Tensor or None): mask some of the possible outcomes when a
sample is taken. See :meth:`~.update_mask` for more information.
Examples:
>>> ts = MultiCategorical((3, 2, 3))
>>> ts.is_in(torch.tensor([2, 0, 1]))
True
>>> ts.is_in(torch.tensor([2, 10, 1]))
False
"""
def __init__(
self,
nvec: Union[Sequence[int], torch.Tensor, int],
shape: Optional[torch.Size] = None,
device: Optional[DEVICE_TYPING] = None,
dtype: Optional[Union[str, torch.dtype]] = torch.int64,
mask: torch.Tensor | None = None,
remove_singleton: bool = True,
):
if not isinstance(nvec, torch.Tensor):
nvec = torch.tensor(nvec)
if nvec.ndim < 1:
nvec = nvec.unsqueeze(0)
self.nvec = nvec
dtype, device = _default_dtype_and_device(dtype, device)
if shape is None:
shape = nvec.shape
else:
shape = _size(shape)
if shape[-1] != nvec.shape[-1]:
raise ValueError(
f"The last value of the shape must match nvec.shape[-1] for transform of type {self.__class__}. "
f"Got nvec.shape[-1]={sum(nvec)} and shape={shape}."
)
self.nvec = self.nvec.expand(_remove_neg_shapes(shape))
space = BoxList.from_nvec(self.nvec)
super(Categorical, self).__init__(
shape, space, device, dtype, domain="discrete"
)
self.update_mask(mask)
self.remove_singleton = remove_singleton
[docs] def enumerate(self) -> torch.Tensor:
if self.mask is not None:
raise RuntimeError(
"Cannot enumerate a masked TensorSpec. Submit an issue on github if this feature is requested."
)
if self.nvec._base.ndim == 1:
nvec = self.nvec._base
else:
# we have to use unique() to isolate the nvec
nvec = self.nvec.view(-1, self.nvec.shape[-1]).unique(dim=0).squeeze(0)
if nvec.ndim > 1:
raise ValueError(
f"Cannot call enumerate on heterogeneous nvecs: unique nvecs={nvec}."
)
arange = torch.meshgrid(
*[torch.arange(n, device=self.device, dtype=self.dtype) for n in nvec],
indexing="ij",
)
arange = torch.stack([arange_.reshape(-1) for arange_ in arange], dim=-1)
arange = arange.view(arange.shape[0], *(1,) * (self.ndim - 1), self.shape[-1])
arange = arange.expand(arange.shape[0], *self.shape)
return arange
[docs] def update_mask(self, mask):
"""Sets a mask to prevent some of the possible outcomes when a sample is taken.
The mask can also be set during initialization of the spec.
Args:
mask (torch.Tensor or None): boolean mask. If None, the mask is
disabled. Otherwise, the shape of the mask must be expandable to
the shape of the equivalent one-hot spec. ``False`` masks an
outcome and ``True`` leaves the outcome unmasked. If all of the
possible outcomes are masked, then an error is raised when a
sample is taken.
Examples:
>>> torch.manual_seed(0)
>>> mask = torch.tensor([False, False, True,
... True, True])
>>> ts = MultiCategorical((3, 2), (5, 2,), dtype=torch.int64, mask=mask)
>>> # All but one of the three possible outcomes for the first
>>> # group are masked, but neither of the two possible
>>> # outcomes for the second group are masked.
>>> ts.rand()
tensor([[2, 1],
[2, 0],
[2, 1],
[2, 1],
[2, 1]])
"""
if mask is not None:
try:
mask = mask.expand(_remove_neg_shapes(*self.shape[:-1], mask.shape[-1]))
except RuntimeError as err:
raise RuntimeError("Cannot expand mask to the desired shape.") from err
if mask.dtype != torch.bool:
raise ValueError("Only boolean masks are accepted.")
self.mask = mask
[docs] def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> MultiCategorical:
if isinstance(dest, torch.dtype):
dest_dtype = dest
dest_device = self.device
elif dest is None:
return self
else:
dest_dtype = self.dtype
dest_device = torch.device(dest)
if dest_device == self.device and dest_dtype == self.dtype:
return self
mask = self.mask.to(dest) if self.mask is not None else None
return self.__class__(
nvec=self.nvec.to(dest),
shape=None,
device=dest_device,
dtype=dest_dtype,
mask=mask,
)
def __eq__(self, other):
if not hasattr(other, "mask"):
return False
mask_equal = (self.mask is None and other.mask is None) or (
isinstance(self.mask, torch.Tensor)
and isinstance(other.mask, torch.Tensor)
and (self.mask.shape == other.mask.shape)
and (self.mask == other.mask).all()
)
return (
type(self) == type(other)
and self.shape == other.shape
and self.space == other.space
and self.device == other.device
and self.dtype == other.dtype
and self.domain == other.domain
and mask_equal
)
[docs] def clone(self) -> MultiCategorical:
return self.__class__(
nvec=self.nvec.clone(),
shape=None,
device=self.device,
dtype=self.dtype,
mask=self.mask.clone() if self.mask is not None else None,
)
def _rand(self, space: Box, shape: torch.Size, i: int):
x = []
for _s in space:
if isinstance(_s, BoxList):
x.append(self._rand(_s, shape[:-1], i - 1))
else:
x.append(
torch.randint(
0,
_s.n,
shape,
device=self.device,
dtype=self.dtype,
)
)
return torch.stack(x, -1)
[docs] def rand(self, shape: Optional[torch.Size] = None) -> torch.Tensor:
if self.mask is not None:
splits = self._split_self()
return torch.stack([split.rand(shape) for split in splits], -1)
if shape is None:
shape = self.shape[:-1]
else:
shape = (
*shape,
*self.shape[:-1],
)
x = self._rand(space=self.space, shape=shape, i=self.nvec.ndim)
if self.remove_singleton and self.shape == _size([1]):
x = x.squeeze(-1)
return x
def _split_self(self):
result = []
device = self.device
dtype = self.dtype
nvec = self.nvec
if nvec.ndim > 1:
nvec = torch.flatten(nvec, 0, -2)[0]
if (self.nvec != nvec).any():
raise ValueError(
f"Only homogeneous MultiDiscrete specs can be masked, got nvec={self.nvec}."
)
nvec = nvec.tolist()
mask = (
self.mask.split(nvec, -1)
if self.mask is not None
else [None] * len(self.space)
)
for n, _mask in zip(nvec, mask):
shape = self.shape[:-1]
result.append(
Categorical(n=n, shape=shape, device=device, dtype=dtype, mask=_mask)
)
return result
def _project(self, val: torch.Tensor) -> torch.Tensor:
if self.mask is not None:
return torch.stack(
[
spec._project(_val)
for (_val, spec) in zip(val.unbind(-1), self._split_self())
],
-1,
)
val_is_scalar = val.ndim < 1
if val_is_scalar:
val = val.unsqueeze(0)
if not self.dtype.is_floating_point:
val = torch.round(val)
val = val.type(self.dtype)
val[val >= self.nvec] = (self.nvec.expand_as(val)[val >= self.nvec] - 1).type(
self.dtype
)
return val.squeeze(0) if val_is_scalar else val
[docs] def is_in(self, val: torch.Tensor) -> bool:
if self.mask is not None:
vals = val.unbind(-1)
splits = self._split_self()
if not len(vals) == len(splits):
return False
return all(spec.is_in(val) for (val, spec) in zip(vals, splits))
if val.ndim < 1:
val = val.unsqueeze(0)
shape = _remove_neg_shapes(self.shape)
shape = torch.broadcast_shapes(shape, val.shape)
if shape != val.shape:
return False
if self.dtype != val.dtype:
return False
val_device = val.device
return (
(
(val >= torch.zeros(self.nvec.size(), device=val_device))
& (val < self.nvec.to(val_device))
)
.all()
.item()
)
[docs] def to_one_hot(
self, val: torch.Tensor, safe: bool = None
) -> Union[MultiOneHot, torch.Tensor]:
"""Encodes a discrete tensor from the spec domain into its one-hot correspondent.
Args:
val (torch.Tensor, optional): Tensor to one-hot encode.
safe (bool): boolean value indicating whether a check should be
performed on the value against the domain of the spec.
Defaults to the value of the ``CHECK_SPEC_ENCODE`` environment variable.
Returns:
The one-hot encoded tensor.
"""
if safe is None:
safe = _CHECK_SPEC_ENCODE
if safe:
self.assert_is_in(val)
return torch.cat(
[
torch.nn.functional.one_hot(val[..., i], n).bool()
for i, n in enumerate(self.nvec)
],
-1,
).to(self.device)
[docs] def to_one_hot_spec(self) -> MultiOneHot:
"""Converts the spec to the equivalent one-hot spec."""
if self.ndim > 1:
return torch.stack([spec.to_one_hot_spec() for spec in self.unbind(0)])
nvec = [_space.n for _space in self.space]
return MultiOneHot(
nvec,
device=self.device,
shape=[*self.shape[:-1], sum(nvec)],
mask=self.mask,
)
[docs] def to_categorical(self, val: torch.Tensor, safe: bool = None) -> MultiCategorical:
"""Not op for MultiCategorical."""
return val
[docs] def to_categorical_spec(self) -> MultiCategorical:
"""Not op for MultiCategorical."""
return self
[docs] def expand(self, *shape):
if len(shape) == 1 and isinstance(shape[0], (tuple, list, torch.Size)):
shape = shape[0]
if any(s1 != s2 and s2 != 1 for s1, s2 in zip(shape[-self.ndim :], self.shape)):
raise ValueError(
f"The last {self.ndim} of the expanded shape {shape} must match the"
f"shape of the {self.__class__.__name__} spec in expand()."
)
mask = (
self.mask.expand(*shape, self.mask.shape[-1])
if self.mask is not None
else None
)
return self.__class__(
nvec=self.nvec,
shape=shape,
device=self.device,
dtype=self.dtype,
mask=mask,
)
def _reshape(self, shape):
mask = (
self.mask.reshape(*shape, self.mask.shape[-1])
if self.mask is not None
else None
)
return self.__class__(
nvec=self.nvec,
shape=shape,
device=self.device,
dtype=self.dtype,
mask=mask,
)
def _unflatten(self, dim, sizes):
shape = torch.zeros(self.shape, device="meta").unflatten(dim, sizes).shape
return self._reshape(shape)
[docs] def squeeze(self, dim: int | None = None):
if self.shape[-1] == 1 and dim in (len(self.shape), -1, None):
raise ValueError(f"Final dimension of {type(self)} must remain unchanged")
shape = _squeezed_shape(self.shape, dim)
if shape is None:
return self
if dim is None:
nvec = self.nvec.squeeze()
else:
nvec = self.nvec.squeeze(dim)
mask = self.mask
if mask is not None:
mask = mask.view(*shape[:-1], mask.shape[-1])
return self.__class__(
nvec=nvec, shape=shape, device=self.device, dtype=self.dtype, mask=mask
)
[docs] def unsqueeze(self, dim: int):
if dim in (len(self.shape), -1):
raise ValueError(f"Final dimension of {type(self)} must remain unchanged")
shape = _unsqueezed_shape(self.shape, dim)
nvec = self.nvec.unsqueeze(dim)
mask = self.mask
if mask is not None:
mask = mask.view(*shape[:-1], mask.shape[-1])
return self.__class__(
nvec=nvec,
shape=shape,
device=self.device,
dtype=self.dtype,
mask=mask,
)
def unbind(self, dim: int = 0):
if dim in (len(self.shape), -1):
raise ValueError(f"Final dimension of {type(self)} must remain unchanged")
orig_dim = dim
if dim < 0:
dim = len(self.shape) + dim
if dim < 0:
raise ValueError(
f"Cannot unbind along dim {orig_dim} with shape {self.shape}."
)
shape = tuple(s for i, s in enumerate(self.shape) if i != dim)
mask = self.mask
nvec = self.nvec.unbind(dim)
if mask is not None:
mask = mask.unbind(dim)
else:
mask = (None,) * self.shape[dim]
return tuple(
self.__class__(
nvec=nvec[i],
shape=shape,
device=self.device,
dtype=self.dtype,
mask=mask[i],
)
for i in range(self.shape[dim])
)
def __getitem__(self, idx: SHAPE_INDEX_TYPING):
"""Indexes the current TensorSpec based on the provided index."""
if _is_nested_list(idx):
raise NotImplementedError(
"Pending resolution of https://github.com/pytorch/pytorch/issues/100080."
)
return self.__class__(
nvec=self.nvec[idx].clone(),
shape=None,
device=self.device,
dtype=self.dtype,
)
[docs]class Composite(TensorSpec):
"""A composition of TensorSpecs.
If a ``TensorSpec`` is the set-description of Tensor category, the ``Composite`` class is akin to
the :class:`~tensordict.TensorDict` class. Like :class:`~tensordict.TensorDict`, it has a ``shape`` (akin to the
``TensorDict``'s ``batch_size``) and an optional ``device``.
Args:
*args: if an unnamed argument is passed, it must be a dictionary with keys
matching the expected keys to be found in the :obj:`Composite` object.
This is useful to build nested CompositeSpecs with tuple indices.
**kwargs (key (str): value (TensorSpec)): dictionary of tensorspecs
to be stored. Values can be None, in which case is_in will be assumed
to be ``True`` for the corresponding tensors, and :obj:`project()` will have no
effect. `spec.encode` cannot be used with missing values.
Attributes:
device (torch.device or None): if not specified, the device of the composite
spec is ``None`` (as it is the case for TensorDicts). A non-none device
constraints all leaves to be of the same device. On the other hand,
a ``None`` device allows leaves to have different devices. Defaults
to ``None``.
shape (torch.Size): the leading shape of all the leaves. Equivalent
to the batch-size of the corresponding tensordicts.
Examples:
>>> pixels_spec = Bounded(
... low=torch.zeros(4, 3, 32, 32),
... high=torch.ones(4, 3, 32, 32),
... dtype=torch.uint8
... )
>>> observation_vector_spec = Bounded(
... low=torch.zeros(4, 33),
... high=torch.ones(4, 33),
... dtype=torch.float)
>>> composite_spec = Composite(
... pixels=pixels_spec,
... observation_vector=observation_vector_spec,
... shape=(4,)
... )
>>> composite_spec
Composite(
pixels: BoundedDiscrete(
shape=torch.Size([4, 3, 32, 32]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([4, 3, 32, 32]), device=cpu, dtype=torch.uint8, contiguous=True),
high=Tensor(shape=torch.Size([4, 3, 32, 32]), device=cpu, dtype=torch.uint8, contiguous=True)),
device=cpu,
dtype=torch.uint8,
domain=discrete),
observation_vector: BoundedContinuous(
shape=torch.Size([4, 33]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([4, 33]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([4, 33]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous),
device=None,
shape=torch.Size([4]))
>>> td = composite_spec.rand()
>>> td
TensorDict(
fields={
observation_vector: Tensor(shape=torch.Size([4, 33]), device=cpu, dtype=torch.float32, is_shared=False),
pixels: Tensor(shape=torch.Size([4, 3, 32, 32]), device=cpu, dtype=torch.uint8, is_shared=False)},
batch_size=torch.Size([4]),
device=None,
is_shared=False)
>>> # we can build a nested composite spec using unnamed arguments
>>> print(Composite({("a", "b"): None, ("a", "c"): None}))
Composite(
a: Composite(
b: None,
c: None,
device=None,
shape=torch.Size([])),
device=None,
shape=torch.Size([]))
"""
shape: torch.Size
domain: str = "composite"
SPEC_HANDLED_FUNCTIONS = {}
@classmethod
def __new__(cls, *args, **kwargs):
cls._device = None
cls._locked = False
return super().__new__(cls)
@property
def shape(self):
return self._shape
@shape.setter
def shape(self, value: torch.Size):
if self.locked:
raise RuntimeError("Cannot modify shape of locked composite spec.")
for key, spec in self.items():
if isinstance(spec, Composite):
if spec.shape[: len(value)] != value:
spec.shape = value
elif spec is not None:
if spec.shape[: len(value)] != value:
raise ValueError(
f"The shape of the spec and the Composite mismatch during shape resetting: the "
f"{self.ndim} first dimensions should match but got self['{key}'].shape={spec.shape} and "
f"Composite.shape={self.shape}."
)
self._shape = _size(value)
[docs] def is_empty(self):
"""Whether the composite spec contains specs or not."""
return len(self._specs) == 0
@property
def ndim(self):
return self.ndimension()
def set(self, name, spec):
if self.locked:
raise RuntimeError("Cannot modify a locked Composite.")
if spec is not None:
shape = spec.shape
if shape[: self.ndim] != self.shape:
raise ValueError(
"The shape of the spec and the Composite mismatch: the first "
f"{self.ndim} dimensions should match but got spec.shape={spec.shape} and "
f"Composite.shape={self.shape}."
)
self._specs[name] = spec
def __init__(
self, *args, shape: torch.Size = None, device: torch.device = None, **kwargs
):
# For compatibility with TensorDict
batch_size = kwargs.pop("batch_size", None)
if batch_size is not None:
if shape is not None:
raise TypeError("Cannot specify both batch_size and shape.")
shape = batch_size
if shape is None:
shape = _size(())
self._shape = _size(shape)
self._specs = {}
for key, value in kwargs.items():
self.set(key, value)
_device = (
_make_ordinal_device(torch.device(device)) if device is not None else device
)
if len(kwargs):
for key, item in self.items():
if item is None:
continue
if (
isinstance(item, Composite)
and item.device is None
and _device is not None
):
item = item.clone().to(_device)
elif (_device is not None) and (item.device != _device):
raise RuntimeError(
f"Setting a new attribute ({key}) on another device "
f"({item.device} against {_device}). All devices of "
"Composite must match."
)
self._device = _device
if len(args):
if len(args) > 1:
raise RuntimeError(
"Got multiple arguments, when at most one is expected for Composite."
)
argdict = args[0]
if not isinstance(argdict, (dict, Composite)):
raise RuntimeError(
f"Expected a dictionary of specs, but got an argument of type {type(argdict)}."
)
for k, item in argdict.items():
if isinstance(item, dict):
item = Composite(item, shape=shape, device=_device)
self[k] = item
@property
def device(self) -> DEVICE_TYPING:
return self._device
@device.setter
def device(self, device: DEVICE_TYPING):
if device is None and self._device is not None:
raise RuntimeError(
"To erase the device of a composite spec, call " "spec.clear_device_()."
)
device = _make_ordinal_device(torch.device(device))
self.to(device)
[docs] def clear_device_(self):
"""Clears the device of the Composite."""
self._device = None
for spec in self._specs.values():
spec.clear_device_()
return self
def __getitem__(self, idx):
"""Indexes the current Composite based on the provided index."""
if isinstance(idx, (str, tuple)):
idx_unravel = unravel_key(idx)
else:
idx_unravel = ()
if idx_unravel:
if isinstance(idx_unravel, tuple):
return self[idx[0]][idx[1:]]
if idx_unravel in {"shape", "device", "dtype", "space"}:
raise AttributeError(f"Composite has no key {idx_unravel}")
return self._specs[idx_unravel]
indexed_shape = _shape_indexing(self.shape, idx)
indexed_specs = {}
for k, v in self._specs.items():
_idx = idx
if isinstance(idx, tuple):
protected_dims = 0
if any(
isinstance(v, spec_class)
for spec_class in [
Binary,
MultiCategorical,
OneHot,
]
):
protected_dims = 1
# TensorSpecs dims which are not part of the composite shape cannot be indexed
_idx = idx + (slice(None),) * (
len(v.shape) - len(self.shape) - protected_dims
)
indexed_specs[k] = v[_idx] if v is not None else None
try:
device = self.device
except RuntimeError:
device = self._device
return self.__class__(
indexed_specs,
shape=indexed_shape,
device=device,
)
[docs] def get(self, item, default=NO_DEFAULT):
"""Gets an item from the Composite.
If the item is absent, a default value can be passed.
"""
try:
return self[item]
except KeyError:
if item is not NO_DEFAULT:
return default
raise
def __setitem__(self, key, value):
if isinstance(key, tuple) and len(key) > 1:
if key[0] not in self.keys(True):
self[key[0]] = Composite(shape=self.shape, device=self.device)
self[key[0]][key[1:]] = value
return
elif isinstance(key, tuple):
self[key[0]] = value
return
elif not isinstance(key, str):
raise TypeError(f"Got key of type {type(key)} when a string was expected.")
if key in {"shape", "device", "dtype", "space"}:
raise AttributeError(f"Composite[{key}] cannot be set")
if isinstance(value, dict):
value = Composite(value, device=self._device, shape=self.shape)
if (
value is not None
and self.device is not None
and value.device != self.device
):
if isinstance(value, Composite) and value.device is None:
value = value.clone().to(self.device)
else:
raise RuntimeError(
f"Setting a new attribute ({key}) on another device ({value.device} against {self.device}). "
f"All devices of Composite must match."
)
self.set(key, value)
def __iter__(self):
yield from self._specs
def __delitem__(self, key: NestedKey) -> None:
key = unravel_key(key)
if isinstance(key, tuple):
spec = self[key[:-1]]
del spec[key[-1]]
return
elif not isinstance(key, str):
raise TypeError(
f"Got key of type {type(key)} when a string or a tuple of strings was expected."
)
if key in {"shape", "device", "dtype", "space"}:
raise ValueError(f"Key name {key} is prohibited.")
del self._specs[key]
[docs] def encode(
self, vals: Dict[str, Any], *, ignore_device: bool = False
) -> Dict[str, torch.Tensor]:
if isinstance(vals, TensorDict):
out = vals.empty() # create and empty tensordict similar to vals
else:
out = TensorDict._new_unsafe({}, _size([]))
for key, item in vals.items():
if item is None:
raise RuntimeError(
"Composite.encode cannot be used with missing values."
)
try:
out[key] = self[key].encode(item, ignore_device=ignore_device)
except KeyError:
raise KeyError(
f"The Composite instance with keys {self.keys()} does not have a '{key}' key."
)
except RuntimeError as err:
raise RuntimeError(
f"Encoding key {key} raised a RuntimeError. Scroll up to know more."
) from err
return out
def __repr__(self) -> str:
sub_str = [
indent(f"{k}: {str(item)}", 4 * " ") for k, item in self._specs.items()
]
sub_str = ",\n".join(sub_str)
return f"Composite(\n{sub_str},\n device={self._device},\n shape={self.shape})"
[docs] def type_check(
self,
value: Union[torch.Tensor, TensorDictBase],
selected_keys: Union[str, Optional[Sequence[str]]] = None,
):
if isinstance(value, torch.Tensor) and isinstance(selected_keys, str):
value = {selected_keys: value}
selected_keys = [selected_keys]
for _key in self.keys():
if self[_key] is not None and (
selected_keys is None or _key in selected_keys
):
self._specs[_key].type_check(value[_key], _key)
[docs] def is_in(self, val: Union[dict, TensorDictBase]) -> bool:
for key, item in self._specs.items():
if item is None or (isinstance(item, Composite) and item.is_empty()):
continue
val_item = val.get(key, NO_DEFAULT)
if not item.is_in(val_item):
return False
return True
[docs] def project(self, val: TensorDictBase) -> TensorDictBase:
for key, item in self.items():
if item is None:
continue
_val = val.get(key)
if not self._specs[key].is_in(_val):
val.set(key, self._specs[key].project(_val))
return val
[docs] def rand(self, shape: torch.Size = None) -> TensorDictBase:
if shape is None:
shape = _size([])
_dict = {}
for key, item in self.items():
if item is not None:
_dict[key] = item.rand(shape)
# No need to run checks since we know Composite is compliant with
# TensorDict requirements
return TensorDict._new_unsafe(
_dict,
batch_size=_size([*shape, *self.shape]),
device=self._device,
)
[docs] def keys(
self,
include_nested: bool = False,
leaves_only: bool = False,
*,
is_leaf: Callable[[type], bool] | None = None,
) -> _CompositeSpecKeysView: # noqa: D417
"""Keys of the Composite.
The keys argument reflect those of :class:`tensordict.TensorDict`.
Args:
include_nested (bool, optional): if ``False``, the returned keys will not be nested. They will
represent only the immediate children of the root, and not the whole nested sequence, i.e. a
:obj:`Composite(next=Composite(obs=None))` will lead to the keys
:obj:`["next"]. Default is ``False``, i.e. nested keys will not
be returned.
leaves_only (bool, optional): if ``False``, the values returned
will contain every level of nesting, i.e. a :obj:`Composite(next=Composite(obs=None))`
will lead to the keys :obj:`["next", ("next", "obs")]`.
Default is ``False``.
Keyword Args:
is_leaf (callable, optional): reads a type and returns a boolean indicating if that type
should be seen as a leaf. By default, all non-Composite nodes are considered as
leaves.
"""
return _CompositeSpecItemsView(
self,
include_nested=include_nested,
leaves_only=leaves_only,
is_leaf=is_leaf,
)._keys()
[docs] def items(
self,
include_nested: bool = False,
leaves_only: bool = False,
*,
is_leaf: Callable[[type], bool] | None = None,
) -> _CompositeSpecItemsView: # noqa: D417
"""Items of the Composite.
Args:
include_nested (bool, optional): if ``False``, the returned keys will not be nested. They will
represent only the immediate children of the root, and not the whole nested sequence, i.e. a
:obj:`Composite(next=Composite(obs=None))` will lead to the keys
:obj:`["next"]. Default is ``False``, i.e. nested keys will not
be returned.
leaves_only (bool, optional): if ``False``, the values returned
will contain every level of nesting, i.e. a :obj:`Composite(next=Composite(obs=None))`
will lead to the keys :obj:`["next", ("next", "obs")]`.
Default is ``False``.
Keyword Args:
is_leaf (callable, optional): reads a type and returns a boolean indicating if that type
should be seen as a leaf. By default, all non-Composite nodes are considered as
leaves.
"""
return _CompositeSpecItemsView(
self,
include_nested=include_nested,
leaves_only=leaves_only,
is_leaf=is_leaf,
)
[docs] def values(
self,
include_nested: bool = False,
leaves_only: bool = False,
*,
is_leaf: Callable[[type], bool] | None = None,
) -> _CompositeSpecValuesView: # noqa: D417
"""Values of the Composite.
Args:
include_nested (bool, optional): if ``False``, the returned keys will not be nested. They will
represent only the immediate children of the root, and not the whole nested sequence, i.e. a
:obj:`Composite(next=Composite(obs=None))` will lead to the keys
:obj:`["next"]. Default is ``False``, i.e. nested keys will not
be returned.
leaves_only (bool, optional): if ``False``, the values returned
will contain every level of nesting, i.e. a :obj:`Composite(next=Composite(obs=None))`
will lead to the keys :obj:`["next", ("next", "obs")]`.
Default is ``False``.
Keyword Args:
is_leaf (callable, optional): reads a type and returns a boolean indicating if that type
should be seen as a leaf. By default, all non-Composite nodes are considered as
leaves.
"""
return _CompositeSpecItemsView(
self,
include_nested=include_nested,
leaves_only=leaves_only,
is_leaf=is_leaf,
)._values()
def _reshape(self, shape):
_specs = {
key: val.reshape((*shape, *val.shape[self.ndimension() :]))
for key, val in self._specs.items()
}
return Composite(_specs, shape=shape)
def _unflatten(self, dim, sizes):
shape = torch.zeros(self.shape, device="meta").unflatten(dim, sizes).shape
return self._reshape(shape)
def __len__(self):
return len(self.keys())
[docs] def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> Composite:
if dest is None:
return self
if not isinstance(dest, (str, int, torch.device)):
raise ValueError(
"Only device casting is allowed with specs of type Composite."
)
if self._device and self._device == torch.device(dest):
return self
_device = torch.device(dest)
items = list(self.items())
kwargs = {}
for key, value in items:
if value is None:
kwargs[key] = value
continue
kwargs[key] = value.to(dest)
return self.__class__(**kwargs, device=_device, shape=self.shape)
[docs] def clone(self) -> Composite:
try:
device = self.device
except RuntimeError:
device = self._device
return self.__class__(
{
key: item.clone() if item is not None else None
for key, item in self.items()
},
device=device,
shape=self.shape,
)
[docs] def enumerate(self) -> TensorDictBase:
# We are going to use meshgrid to create samples of all the subspecs in here
# but first let's get rid of the batch size, we'll put it back later
self_without_batch = self
while self_without_batch.ndim:
self_without_batch = self_without_batch[0]
samples = {key: spec.enumerate() for key, spec in self_without_batch.items()}
if samples:
idx_rep = torch.meshgrid(
*(torch.arange(s.shape[0]) for s in samples.values()), indexing="ij"
)
idx_rep = tuple(idx.reshape(-1) for idx in idx_rep)
samples = {
key: sample[idx]
for ((key, sample), idx) in zip(samples.items(), idx_rep)
}
samples = TensorDict(
samples, batch_size=idx_rep[0].shape[:1], device=self.device
)
# Expand
if self.ndim:
samples = samples.reshape(-1, *(1,) * self.ndim)
samples = samples.expand(samples.shape[0], *self.shape)
else:
samples = TensorDict(batch_size=self.shape, device=self.device)
return samples
[docs] def empty(self):
"""Create a spec like self, but with no entries."""
try:
device = self.device
except RuntimeError:
device = self._device
return self.__class__(
{},
device=device,
shape=self.shape,
)
[docs] def to_numpy(self, val: TensorDict, safe: bool = None) -> dict:
return {key: self[key].to_numpy(val) for key, val in val.items()}
[docs] def zero(self, shape: torch.Size = None) -> TensorDictBase:
if shape is None:
shape = _size([])
try:
device = self.device
except RuntimeError:
device = self._device
return TensorDict(
{
key: self[key].zero(shape)
for key in self.keys(True)
if isinstance(key, str) and self[key] is not None
},
_size([*shape, *self._safe_shape]),
device=device,
)
def __eq__(self, other):
return (
type(self) is type(other)
and self.shape == other.shape
and self._device == other._device
and set(self._specs.keys()) == set(other._specs.keys())
and all((self._specs[key] == spec) for (key, spec) in other._specs.items())
)
def update(self, dict_or_spec: Union[Composite, Dict[str, TensorSpec]]) -> None:
for key, item in dict_or_spec.items():
if key in self.keys(True) and isinstance(self[key], Composite):
self[key].update(item)
continue
try:
if isinstance(item, TensorSpec) and item.device != self.device:
item = deepcopy(item)
if self.device is not None:
item = item.to(self.device)
except RuntimeError as err:
if DEVICE_ERR_MSG in str(err):
try:
item_device = item.device
self.device = item_device
except RuntimeError as suberr:
if DEVICE_ERR_MSG in str(suberr):
pass
else:
raise suberr
else:
raise err
self[key] = item
return self
[docs] def expand(self, *shape):
if len(shape) == 1 and isinstance(shape[0], (tuple, list, torch.Size)):
shape = shape[0]
if any(s1 != s2 and s2 != 1 for s1, s2 in zip(shape[-self.ndim :], self.shape)):
raise ValueError(
f"The last {self.ndim} of the expanded shape {shape} must match the "
f"shape of the {self.__class__.__name__} spec in expand()."
)
try:
device = self.device
except RuntimeError:
device = self._device
specs = {
key: value.expand((*shape, *value.shape[self.ndim :]))
if value is not None
else None
for key, value in tuple(self.items())
}
out = Composite(
specs,
shape=shape,
device=device,
)
return out
[docs] def squeeze(self, dim: int | None = None):
if dim is not None:
if dim < 0:
dim += len(self.shape)
shape = _squeezed_shape(self.shape, dim)
if shape is None:
return self
try:
device = self.device
except RuntimeError:
device = self._device
return Composite(
{key: value.squeeze(dim) for key, value in self.items()},
shape=shape,
device=device,
)
if self.shape.count(1) == 0:
return self
# we can't just recursively apply squeeze with dim=None because we don't want
# to squeeze non-batch dims of the values. Instead we find the first dim in
# the batch dims with size 1, squeeze that, then recurse on the root spec
out = self.squeeze(self.shape.index(1))
return out.squeeze()
[docs] def unsqueeze(self, dim: int):
if dim < 0:
dim += len(self.shape) + 1
shape = _unsqueezed_shape(self.shape, dim)
try:
device = self.device
except RuntimeError:
device = self._device
return Composite(
{
key: value.unsqueeze(dim) if value is not None else None
for key, value in self.items()
},
shape=shape,
device=device,
)
def unbind(self, dim: int = 0):
orig_dim = dim
if dim < 0:
dim = len(self.shape) + dim
if dim < 0:
raise ValueError(
f"Cannot unbind along dim {orig_dim} with shape {self.shape}."
)
shape = (s for i, s in enumerate(self.shape) if i != dim)
unbound_vals = {key: val.unbind(dim) for key, val in self.items()}
return tuple(
self.__class__(
{key: val[i] for key, val in unbound_vals.items()},
shape=shape,
device=self.device,
)
for i in range(self.shape[dim])
)
[docs] def lock_(self, recurse=False):
"""Locks the Composite and prevents modification of its content.
This is only a first-level lock, unless specified otherwise through the
``recurse`` arg.
Leaf specs can always be modified in place, but they cannot be replaced
in their Composite parent.
Examples:
>>> shape = [3, 4, 5]
>>> spec = Composite(
... a=Composite(
... b=Composite(shape=shape[:3], device="cpu"), shape=shape[:2]
... ),
... shape=shape[:1],
... )
>>> spec["a"] = spec["a"].clone()
>>> recurse = False
>>> spec.lock_(recurse=recurse)
>>> try:
... spec["a"] = spec["a"].clone()
... except RuntimeError:
... print("failed!")
failed!
>>> try:
... spec["a", "b"] = spec["a", "b"].clone()
... print("succeeded!")
... except RuntimeError:
... print("failed!")
succeeded!
>>> recurse = True
>>> spec.lock_(recurse=recurse)
>>> try:
... spec["a", "b"] = spec["a", "b"].clone()
... print("succeeded!")
... except RuntimeError:
... print("failed!")
failed!
"""
self._locked = True
if recurse:
for value in self.values():
if isinstance(value, Composite):
value.lock_(recurse)
return self
[docs] def unlock_(self, recurse=False):
"""Unlocks the Composite and allows modification of its content.
This is only a first-level lock modification, unless specified
otherwise through the ``recurse`` arg.
"""
self._locked = False
if recurse:
for value in self.values():
if isinstance(value, Composite):
value.unlock_(recurse)
return self
@property
def locked(self):
return self._locked
[docs]class StackedComposite(_LazyStackedMixin[Composite], Composite):
"""A lazy representation of a stack of composite specs.
Stacks composite specs together along one dimension.
When random samples are drawn, a LazyStackedTensorDict is returned.
Indexing is allowed but only along the stack dimension.
This class is aimed to be used in multi-task and multi-agent settings, where
heterogeneous specs may occur (same semantic but different shape).
"""
def update(self, dict) -> None:
for key, item in dict.items():
if key in self.keys() and isinstance(
item, (Dict, Composite, StackedComposite)
):
for spec, sub_item in zip(self._specs, item.unbind(self.dim)):
spec[key].update(sub_item)
continue
self[key] = item
return self
[docs] def enumerate(self) -> TensorDictBase:
dim = self.stack_dim
return LazyStackedTensorDict.maybe_dense_stack(
[spec.enumerate() for spec in self._specs], dim + 1
)
def __eq__(self, other):
if not isinstance(other, StackedComposite):
return False
if len(self._specs) != len(other._specs):
return False
if self.stack_dim != other.stack_dim:
return False
if self.device != other.device:
return False
for _spec1, _spec2 in zip(self._specs, other._specs):
if _spec1 != _spec2:
return False
return True
[docs] def to_numpy(self, val: TensorDict, safe: bool = None) -> dict:
if safe is None:
safe = _CHECK_SPEC_ENCODE
if safe:
if val.shape[self.dim] != len(self._specs):
raise ValueError(
"Size of StackedComposite and val differ along the "
"stacking dimension"
)
for spec, v in zip(self._specs, torch.unbind(val, dim=self.dim)):
spec.assert_is_in(v)
return {key: self[key].to_numpy(val) for key, val in val.items()}
def __len__(self):
return self.shape[0]
[docs] def keys(
self,
include_nested: bool = False,
leaves_only: bool = False,
*,
is_leaf: Callable[[type], bool] | None = None,
) -> _CompositeSpecKeysView:
return _CompositeSpecItemsView(
self,
include_nested=include_nested,
leaves_only=leaves_only,
is_leaf=is_leaf,
)._keys()
[docs] def items(
self,
include_nested: bool = False,
leaves_only: bool = False,
*,
is_leaf: Callable[[type], bool] | None = None,
) -> _CompositeSpecItemsView:
return list(
_CompositeSpecItemsView(
self,
include_nested=include_nested,
leaves_only=leaves_only,
is_leaf=is_leaf,
)
)
[docs] def values(
self,
include_nested: bool = False,
leaves_only: bool = False,
*,
is_leaf: Callable[[type], bool] | None = None,
) -> _CompositeSpecValuesView:
return _CompositeSpecItemsView(
self,
include_nested=include_nested,
leaves_only=leaves_only,
is_leaf=is_leaf,
)._values()
[docs] def project(self, val: TensorDictBase) -> TensorDictBase:
vals = []
for spec, subval in zip(self._specs, val.unbind(self.dim)):
if not spec.is_in(subval):
vals.append(spec.project(subval))
else:
vals.append(subval)
res = LazyStackedTensorDict.maybe_dense_stack(vals, dim=self.dim)
if not isinstance(val, LazyStackedTensorDict):
res = res.to_tensordict()
return res
[docs] def type_check(
self,
value: Union[torch.Tensor, TensorDictBase],
selected_keys: Union[NestedKey, Optional[Sequence[NestedKey]]] = None,
):
if selected_keys is None:
if isinstance(value, torch.Tensor):
raise ValueError(
"value must be of type TensorDictBase when key is None"
)
for spec, subvalue in zip(self._specs, value.unbind(self.dim)):
spec.type_check(subvalue)
else:
if isinstance(value, torch.Tensor) and isinstance(selected_keys, str):
value = {selected_keys: value}
selected_keys = [selected_keys]
for _key in self.keys():
if self[_key] is not None and _key in selected_keys:
self[_key].type_check(value[_key], _key)
def __repr__(self) -> str:
sub_str = ",\n".join(
[indent(f"{k}: {repr(item)}", 4 * " ") for k, item in self.items()]
)
sub_str = indent(f"fields={{\n{', '.join([sub_str])}}}", 4 * " ")
exclusive_key_str = self.repr_exclusive_keys()
device_str = indent(f"device={self._specs[0].device}", 4 * " ")
shape_str = indent(f"shape={self.shape}", 4 * " ")
stack_dim = indent(f"stack_dim={self.dim}", 4 * " ")
string = ",\n".join(
[sub_str, exclusive_key_str, device_str, shape_str, stack_dim]
)
return f"StackedComposite(\n{string})"
def repr_exclusive_keys(self):
keys = set(self.keys())
exclusive_keys = [
",\n".join(
[
indent(f"{k}: {repr(spec[k])}", 4 * " ")
for k in spec.keys()
if k not in keys
]
)
for spec in self._specs
]
exclusive_key_str = ",\n".join(
[
indent(f"{i} ->\n{line}", 4 * " ")
for i, line in enumerate(exclusive_keys)
if line != ""
]
)
return indent(f"exclusive_fields={{\n{exclusive_key_str}}}", 4 * " ")
[docs] def is_in(self, value) -> bool:
for spec, subval in zip(self._specs, value.unbind(self.dim)):
if not spec.contains(subval):
return False
return True
def __delitem__(self, key: NestedKey):
"""Deletes a key from the stacked composite spec.
This method will be executed if the key is present in at least one of the stacked specs,
otherwise it will raise an error.
Args:
key (NestedKey): the key to delete.
"""
at_least_one_deletion = False
for spec in self._specs:
try:
del spec[key]
at_least_one_deletion = True
except KeyError:
continue
if not at_least_one_deletion:
raise KeyError(
f"Key {key} must be present in at least one of the stacked specs."
)
return self
def __iter__(self):
for k in self.keys():
yield self[k]
def __setitem__(self, key: NestedKey, value):
key = unravel_key(key)
is_key = isinstance(key, str) or (
isinstance(key, tuple) and all(isinstance(_item, str) for _item in key)
)
if is_key:
self.set(key, value)
else:
raise ValueError(
f"{self.__class__} expects str or tuple of str as key to set values "
)
@property
def device(self) -> DEVICE_TYPING:
device = self.__dict__.get("_device", NO_DEFAULT)
if device is NO_DEFAULT:
devices = {spec.device for spec in self._specs}
if len(devices) == 1:
device = list(devices)[0]
elif len(devices) == 2:
device0, device1 = devices
if device0 is None:
device = device1
elif device1 is None:
device = device0
else:
device = None
else:
device = None
self.__dict__["_device"] = device
return device
@property
def ndim(self):
return self.ndimension()
def set(self, name, spec):
for sub_spec, sub_item in zip(self._specs, spec.unbind(self.dim)):
sub_spec[name] = sub_item
@property
def shape(self):
shape = list(self._specs[0].shape)
dim = self.dim
if dim < 0:
dim = len(shape) + dim + 1
shape.insert(dim, len(self._specs))
return _size(shape)
[docs] def expand(self, *shape):
if len(shape) == 1 and not isinstance(shape[0], (int,)):
return self.expand(*shape[0])
expand_shape = shape[: -len(self.shape)]
existing_shape = self.shape
shape_check = shape[-len(self.shape) :]
for _i, (size1, size2) in enumerate(zip(existing_shape, shape_check)):
if size1 != size2 and size1 != 1:
raise RuntimeError(
f"Expanding a non-singletom dimension: existing shape={size1} vs expand={size2}"
)
elif size1 != size2 and size1 == 1 and _i == self.dim:
# if we're expanding along the stack dim we just need to clone the existing spec
return torch.stack(
[self._specs[0].clone() for _ in range(size2)], self.dim
).expand(*shape)
if _i != len(self.shape) - 1:
raise RuntimeError(
f"Trying to expand non-congruent shapes: received {shape} when the shape is {self.shape}."
)
# remove the stack dim from the expanded shape, which we know to match
unstack_shape = list(expand_shape) + [
s for i, s in enumerate(shape_check) if i != self.dim
]
return torch.stack(
[spec.expand(unstack_shape) for spec in self._specs],
self.dim + len(expand_shape),
)
[docs] def empty(self):
return StackedComposite.maybe_dense_stack(
[spec.empty() for spec in self._specs], dim=self.stack_dim
)
[docs] def encode(
self, vals: Dict[str, Any], ignore_device: bool = False
) -> Dict[str, torch.Tensor]:
raise NOT_IMPLEMENTED_ERROR
[docs] def zero(self, shape: torch.Size = None) -> TensorDictBase:
if shape is not None:
dim = self.dim + len(shape)
else:
dim = self.dim
return LazyStackedTensorDict.maybe_dense_stack(
[spec.zero(shape) for spec in self._specs], dim
)
[docs] def one(self, shape: torch.Size = None) -> TensorDictBase:
if shape is not None:
dim = self.dim + len(shape)
else:
dim = self.dim
return LazyStackedTensorDict.maybe_dense_stack(
[spec.one(shape) for spec in self._specs], dim
)
[docs] def rand(self, shape: torch.Size = None) -> TensorDictBase:
if shape is not None:
dim = self.dim + len(shape)
else:
dim = self.dim
return LazyStackedTensorDict.maybe_dense_stack(
[spec.rand(shape) for spec in self._specs], dim
)
@TensorSpec.implements_for_spec(torch.stack)
def _stack_specs(list_of_spec, dim=0, out=None):
if out is not None:
raise NotImplementedError(
"In-place spec modification is not a feature of torchrl, hence "
"torch.stack(list_of_specs, dim, out=spec) is not implemented."
)
if not len(list_of_spec):
raise ValueError("Cannot stack an empty list of specs.")
spec0 = list_of_spec[0]
if isinstance(spec0, TensorSpec):
device = spec0.device
all_equal = True
for spec in list_of_spec[1:]:
if not isinstance(spec, spec0.__class__):
raise RuntimeError(
"Stacking specs cannot occur: Found more than one type of specs in the list."
)
if device != spec.device:
raise RuntimeError(f"Devices differ, got {device} and {spec.device}")
if spec.dtype != spec0.dtype:
raise RuntimeError(f"Dtypes differ, got {spec0.dtype} and {spec.dtype}")
if spec.ndim != spec0.ndim:
raise RuntimeError(f"Ndims differ, got {spec0.ndim} and {spec.ndim}")
all_equal = all_equal and spec == spec0
if all_equal:
shape = list(spec0.shape)
if dim < 0:
dim += len(shape) + 1
shape.insert(dim, len(list_of_spec))
return spec0.clone().unsqueeze(dim).expand(shape)
return Stacked(*list_of_spec, dim=dim)
else:
raise NotImplementedError
@Composite.implements_for_spec(torch.stack)
def _stack_composite_specs(list_of_spec, dim=0, out=None):
if out is not None:
raise NotImplementedError(
"In-place spec modification is not a feature of torchrl, hence "
"torch.stack(list_of_specs, dim, out=spec) is not implemented."
)
if not len(list_of_spec):
raise ValueError("Cannot stack an empty list of specs.")
spec0 = list_of_spec[0]
if isinstance(spec0, Composite):
devices = {spec.device for spec in list_of_spec}
if len(devices) == 1:
device = list(devices)[0]
elif len(devices) == 2:
device0, device1 = devices
if device0 is None:
device = device1
elif device1 is None:
device = device0
else:
device = None
all_equal = True
for spec in list_of_spec[1:]:
if not isinstance(spec, Composite):
raise RuntimeError(
"Stacking specs cannot occur: Found more than one type of spec in "
"the list."
)
if device != spec.device and device is not None:
# spec.device must be None
spec = spec.to(device)
if spec.shape != spec0.shape:
raise RuntimeError(f"Shapes differ, got {spec.shape} and {spec0.shape}")
all_equal = all_equal and spec == spec0
if all_equal:
shape = list(spec0.shape)
if dim < 0:
dim += len(shape) + 1
shape.insert(dim, len(list_of_spec))
return spec0.clone().unsqueeze(dim).expand(shape)
return StackedComposite(*list_of_spec, dim=dim)
else:
raise NotImplementedError
@TensorSpec.implements_for_spec(torch.squeeze)
def _squeeze_spec(spec: TensorSpec, *args, **kwargs) -> TensorSpec:
return spec.squeeze(*args, **kwargs)
@Composite.implements_for_spec(torch.squeeze)
def _squeeze_composite_spec(spec: Composite, *args, **kwargs) -> Composite:
return spec.squeeze(*args, **kwargs)
@TensorSpec.implements_for_spec(torch.unsqueeze)
def _unsqueeze_spec(spec: TensorSpec, *args, **kwargs) -> TensorSpec:
return spec.unsqueeze(*args, **kwargs)
@Composite.implements_for_spec(torch.unsqueeze)
def _unsqueeze_composite_spec(spec: Composite, *args, **kwargs) -> Composite:
return spec.unsqueeze(*args, **kwargs)
def _keys_to_empty_composite_spec(keys):
"""Given a list of keys, creates a Composite tree where each leaf is assigned a None value."""
if not len(keys):
return
c = Composite()
for key in keys:
if isinstance(key, str):
c[key] = None
elif key[0] in c.keys():
if c[key[0]] is None:
# if the value is None we just replace it
c[key[0]] = _keys_to_empty_composite_spec([key[1:]])
elif isinstance(c[key[0]], Composite):
# if the value is Composite, we update it
out = _keys_to_empty_composite_spec([key[1:]])
if out is not None:
c[key[0]].update(out)
else:
raise RuntimeError("Conflicting keys")
else:
c[key[0]] = _keys_to_empty_composite_spec(key[1:])
return c
def _squeezed_shape(shape: torch.Size, dim: int | None) -> torch.Size | None:
if dim is None:
if len(shape) == 1 or shape.count(1) == 0:
return None
new_shape = _size([s for s in shape if s != 1])
else:
if dim < 0:
dim += len(shape)
if shape[dim] != 1:
return None
new_shape = _size([s for i, s in enumerate(shape) if i != dim])
return new_shape
def _unsqueezed_shape(shape: torch.Size, dim: int) -> torch.Size:
n = len(shape)
if dim < -(n + 1) or dim > n:
raise ValueError(
f"Dimension out of range, expected value in the range [{-(n+1)}, {n}], but "
f"got {dim}"
)
if dim < 0:
dim += n + 1
new_shape = list(shape)
new_shape.insert(dim, 1)
return _size(new_shape)
class _CompositeSpecItemsView:
"""Wrapper class that enables richer behavior of `items` for Composite."""
def __init__(
self,
composite: Composite,
include_nested,
leaves_only,
*,
is_leaf,
):
self.composite = composite
self.leaves_only = leaves_only
self.include_nested = include_nested
self.is_leaf = is_leaf
def __iter__(self):
from tensordict.base import _NESTED_TENSORS_AS_LISTS
is_leaf = self.is_leaf
if is_leaf in (None, _NESTED_TENSORS_AS_LISTS):
def _is_leaf(cls):
return not issubclass(cls, Composite)
else:
_is_leaf = is_leaf
def _iter_from_item(key, item):
if self.include_nested and isinstance(item, Composite):
for subkey, subitem in item.items(
include_nested=True,
leaves_only=self.leaves_only,
is_leaf=is_leaf,
):
if not isinstance(subkey, tuple):
subkey = (subkey,)
yield (key, *subkey), subitem
if not self.leaves_only and not _is_leaf(type(item)):
yield (key, item)
elif not self.leaves_only or _is_leaf(type(item)):
yield key, item
for key, item in self._get_composite_items(is_leaf):
if is_leaf is _NESTED_TENSORS_AS_LISTS and isinstance(
item, _LazyStackedMixin
):
for (i, spec) in enumerate(item._specs):
yield from _iter_from_item(unravel_key((key, str(i))), spec)
else:
yield from _iter_from_item(key, item)
def _get_composite_items(self, is_leaf):
if isinstance(self.composite, StackedComposite):
from tensordict.base import _NESTED_TENSORS_AS_LISTS
if is_leaf is _NESTED_TENSORS_AS_LISTS:
for i, spec in enumerate(self.composite._specs):
for key, item in spec.items():
yield ((str(i), key), item)
else:
keys = self.composite._specs[0].keys()
keys = set(keys)
for spec in self.composite._specs[1:]:
keys = keys.intersection(spec.keys())
yield from ((key, self.composite[key]) for key in sorted(keys, key=str))
else:
yield from self.composite._specs.items()
def __len__(self):
i = 0
for _ in self:
i += 1
return i
def __repr__(self):
return f"{type(self).__name__}(keys={list(self)})"
def __contains__(self, item):
item = unravel_key(item)
if len(item) == 1:
item = item[0]
for key in self.__iter__():
if key == item:
return True
else:
return False
def _keys(self):
return _CompositeSpecKeysView(self)
def _values(self):
return _CompositeSpecValuesView(self)
class _CompositeSpecKeysView:
def __init__(self, items: _CompositeSpecItemsView):
self.items = items
def __iter__(self):
yield from (key for (key, _) in self.items)
def __contains__(self, item):
item = unravel_key(item)
return any(key == item for key in self)
def __len__(self):
return len(self.items)
def __repr__(self):
return f"{type(self).__name__}(keys={list(self)})"
class _CompositeSpecValuesView:
def __init__(self, items: _CompositeSpecItemsView):
self.items = items
def __iter__(self):
yield from (val for (_, val) in self.items)
def __len__(self):
return len(self.items)
def __repr__(self):
return f"{type(self).__name__}(values={list(self)})"
def _minmax_dtype(dtype):
if dtype is torch.bool:
return False, True
if dtype.is_floating_point:
info = torch.finfo(dtype)
else:
info = torch.iinfo(dtype)
return info.min, info.max
def _remove_neg_shapes(*shape):
if len(shape) == 1 and not isinstance(shape[0], int):
shape = shape[0]
if isinstance(shape, np.integer):
shape = (int(shape),)
return _remove_neg_shapes(*shape)
return _size([int(d) if d >= 0 else 1 for d in shape])
##############
# Legacy
#
class _LegacySpecMeta(abc.ABCMeta):
def __call__(cls, *args, **kwargs):
warnings.warn(
f"The {cls.__name__} has been deprecated and will be removed in v0.7. Please use "
f"{cls.__bases__[-1].__name__} instead.",
category=DeprecationWarning,
)
instance = super().__call__(*args, **kwargs)
if (
type(instance) in (UnboundedDiscreteTensorSpec, UnboundedDiscrete)
and instance.domain == "continuous"
):
instance.__class__ = UnboundedContinuous
elif (
type(instance) in (UnboundedContinuousTensorSpec, UnboundedContinuous)
and instance.domain == "discrete"
):
instance.__class__ = UnboundedDiscrete
return instance
def __instancecheck__(cls, instance):
check0 = super().__instancecheck__(instance)
if check0:
return True
parent_cls = cls.__bases__[-1]
return isinstance(instance, parent_cls)
[docs]class CompositeSpec(Composite, metaclass=_LegacySpecMeta):
"""Deprecated version of :class:`torchrl.data.Composite`."""
...
[docs]class OneHotDiscreteTensorSpec(OneHot, metaclass=_LegacySpecMeta):
"""Deprecated version of :class:`torchrl.data.OneHot`."""
...
[docs]class MultiOneHotDiscreteTensorSpec(MultiOneHot, metaclass=_LegacySpecMeta):
"""Deprecated version of :class:`torchrl.data.MultiOneHot`."""
...
[docs]class NonTensorSpec(NonTensor, metaclass=_LegacySpecMeta):
"""Deprecated version of :class:`torchrl.data.NonTensor`."""
...
[docs]class MultiDiscreteTensorSpec(MultiCategorical, metaclass=_LegacySpecMeta):
"""Deprecated version of :class:`torchrl.data.MultiCategorical`."""
...
[docs]class LazyStackedTensorSpec(Stacked, metaclass=_LegacySpecMeta):
"""Deprecated version of :class:`torchrl.data.Stacked`."""
...
[docs]class LazyStackedCompositeSpec(StackedComposite, metaclass=_LegacySpecMeta):
"""Deprecated version of :class:`torchrl.data.StackedComposite`."""
...
[docs]class DiscreteTensorSpec(Categorical, metaclass=_LegacySpecMeta):
"""Deprecated version of :class:`torchrl.data.Categorical`."""
...
[docs]class BinaryDiscreteTensorSpec(Binary, metaclass=_LegacySpecMeta):
"""Deprecated version of :class:`torchrl.data.Binary`."""
...
_BoundedLegacyMeta = type("_BoundedLegacyMeta", (_LegacySpecMeta, _BoundedMeta), {})
[docs]class BoundedTensorSpec(Bounded, metaclass=_BoundedLegacyMeta):
"""Deprecated version of :class:`torchrl.data.Bounded`."""
...
class _UnboundedContinuousMetaclass(_UnboundedMeta):
def __instancecheck__(cls, instance):
return isinstance(instance, Unbounded) and instance.domain == "continuous"
_LegacyUnboundedContinuousMetaclass = type(
"_LegacyUnboundedDiscreteMetaclass",
(_UnboundedContinuousMetaclass, _LegacySpecMeta),
{},
)
[docs]class UnboundedContinuousTensorSpec(
Unbounded, metaclass=_LegacyUnboundedContinuousMetaclass
):
"""Deprecated version of :class:`torchrl.data.Unbounded` with continuous space."""
...
class _UnboundedDiscreteMetaclass(_UnboundedMeta):
def __instancecheck__(cls, instance):
return isinstance(instance, Unbounded) and instance.domain == "discrete"
_LegacyUnboundedDiscreteMetaclass = type(
"_LegacyUnboundedDiscreteMetaclass",
(_UnboundedDiscreteMetaclass, _LegacySpecMeta),
{},
)
[docs]class UnboundedDiscreteTensorSpec(
Unbounded, metaclass=_LegacyUnboundedDiscreteMetaclass
):
"""Deprecated version of :class:`torchrl.data.Unbounded` with discrete space."""
def __init__(
self,
shape: Union[torch.Size, int] = _DEFAULT_SHAPE,
device: Optional[DEVICE_TYPING] = None,
dtype: Optional[Union[str, torch.dtype]] = torch.int64,
**kwargs,
):
super().__init__(shape=shape, device=device, dtype=dtype, **kwargs)
