readonlytensor

ReadOnlyTensor (Tensor)

A special type of tensor which is read-only.

This is a subclass of torch.Tensor which explicitly disallows operations that would cause in-place modifications.

Since ReadOnlyTensor if a subclass of torch.Tensor, most non-destructive PyTorch operations are on this tensor are supported.

Cloning a ReadOnlyTensor using the clone() method or Python's deepcopy(...) function results in a regular PyTorch tensor.

Reshaping or slicing operations might return a ReadOnlyTensor if the result ends up being a view of the original ReadOnlyTensor; otherwise, the returned tensor is a regular torch.Tensor.

Source code in evotorch/tools/readonlytensor.py

class ReadOnlyTensor(torch.Tensor):
    """
    A special type of tensor which is read-only.

    This is a subclass of `torch.Tensor` which explicitly disallows
    operations that would cause in-place modifications.

    Since ReadOnlyTensor if a subclass of `torch.Tensor`, most
    non-destructive PyTorch operations are on this tensor are supported.

    Cloning a ReadOnlyTensor using the `clone()` method or Python's
    `deepcopy(...)` function results in a regular PyTorch tensor.

    Reshaping or slicing operations might return a ReadOnlyTensor if the
    result ends up being a view of the original ReadOnlyTensor; otherwise,
    the returned tensor is a regular `torch.Tensor`.
    """

    def __getattribute__(self, attribute_name: str) -> Any:
        if (
            isinstance(attribute_name, str)
            and attribute_name.endswith("_")
            and (not ((attribute_name.startswith("__")) and (attribute_name.endswith("__"))))
        ):
            raise AttributeError(
                f"A ReadOnlyTensor explicitly disables all members whose names end with '_'."
                f" Cannot access member {repr(attribute_name)}."
            )
        else:
            return super().__getattribute__(attribute_name)

    def __cannot_modify(self, *ignore, **ignore_too):
        raise TypeError("The contents of a ReadOnlyTensor cannot be modified")

    __setitem__ = __cannot_modify
    __iadd__ = __cannot_modify
    __iand__ = __cannot_modify
    __idiv__ = __cannot_modify
    __ifloordiv__ = __cannot_modify
    __ilshift__ = __cannot_modify
    __imatmul__ = __cannot_modify
    __imod__ = __cannot_modify
    __imul__ = __cannot_modify
    __ior__ = __cannot_modify
    __ipow__ = __cannot_modify
    __irshift__ = __cannot_modify
    __isub__ = __cannot_modify
    __itruediv__ = __cannot_modify
    __ixor__ = __cannot_modify

    if _torch_older_than_1_12:
        # Define __str__ and __repr__ for when using PyTorch 1.11 or older.
        # With PyTorch 1.12, overriding __str__ and __repr__ are not necessary.
        def __to_string(self) -> str:
            s = super().__repr__()
            if "\n" not in s:
                return f"ReadOnlyTensor({super().__repr__()})"
            else:
                indenter = " " * 4
                s = (indenter + s.replace("\n", "\n" + indenter)).rstrip()
                return f"ReadOnlyTensor(\n{s}\n)"

        __str__ = __to_string
        __repr__ = __to_string

    def clone(self) -> torch.Tensor:
        return super().clone().as_subclass(torch.Tensor)

    def __mutable_if_independent(self, other: torch.Tensor) -> torch.Tensor:
        self_ptr = self.storage().data_ptr()
        other_ptr = other.storage().data_ptr()
        if self_ptr != other_ptr:
            other = other.as_subclass(torch.Tensor)
        return other

    def __getitem__(self, index_or_slice) -> torch.Tensor:
        result = super().__getitem__(index_or_slice)
        return self.__mutable_if_independent(result)

    def reshape(self, *args, **kwargs) -> torch.Tensor:
        result = super().reshape(*args, **kwargs)
        return self.__mutable_if_independent(result)

    def numpy(self) -> np.ndarray:
        arr: np.ndarray = torch.Tensor.numpy(self)
        arr.flags["WRITEABLE"] = False
        return arr

    def __array__(self, *args, **kwargs) -> np.ndarray:
        arr: np.ndarray = super().__array__(*args, **kwargs)
        arr.flags["WRITEABLE"] = False
        return arr

    # def __copy__(self):
    #    return ReadOnlyTensor(copy(self.as_subclass(torch.Tensor)))

    # def __deepcopy__(self, memo):
    #    return deepcopy(self.as_subclass(torch.Tensor), memo)

    @classmethod
    def __torch_function__(cls, func: Callable, types: Iterable, args: tuple = (), kwargs: Optional[Mapping] = None):
        if (kwargs is not None) and ("out" in kwargs):
            if isinstance(kwargs["out"], ReadOnlyTensor):
                raise TypeError(
                    f"The `out` keyword argument passed to {func} is a ReadOnlyTensor."
                    f" A ReadOnlyTensor explicitly fails when referenced via the `out` keyword argument of any torch"
                    f" function."
                    f" This restriction is for making sure that the torch operations which could normally do in-place"
                    f" modifications do not operate on ReadOnlyTensor instances."
                )
        return super().__torch_function__(func, types, args, kwargs)

__torch_function__(func, types, args=(), kwargs=None) classmethod special

This torch_function implementation wraps subclasses such that methods called on subclasses return a subclass instance instead of a torch.Tensor instance.

One corollary to this is that you need coverage for torch.Tensor methods if implementing torch_function for subclasses.

We recommend always calling super().__torch_function__ as the base case when doing the above.

While not mandatory, we recommend making __torch_function__ a classmethod.

Source code in evotorch/tools/readonlytensor.py

@classmethod
def __torch_function__(cls, func: Callable, types: Iterable, args: tuple = (), kwargs: Optional[Mapping] = None):
    if (kwargs is not None) and ("out" in kwargs):
        if isinstance(kwargs["out"], ReadOnlyTensor):
            raise TypeError(
                f"The `out` keyword argument passed to {func} is a ReadOnlyTensor."
                f" A ReadOnlyTensor explicitly fails when referenced via the `out` keyword argument of any torch"
                f" function."
                f" This restriction is for making sure that the torch operations which could normally do in-place"
                f" modifications do not operate on ReadOnlyTensor instances."
            )
    return super().__torch_function__(func, types, args, kwargs)

clone(self)

clone(*, memory_format=torch.preserve_format) -> Tensor

See :func:torch.clone

Source code in evotorch/tools/readonlytensor.py

def clone(self) -> torch.Tensor:
    return super().clone().as_subclass(torch.Tensor)

numpy(self)

numpy() -> numpy.ndarray

Returns :attr:self tensor as a NumPy :class:ndarray. This tensor and the returned :class:ndarray share the same underlying storage. Changes to :attr:self tensor will be reflected in the :class:ndarray and vice versa.

Source code in evotorch/tools/readonlytensor.py

def numpy(self) -> np.ndarray:
    arr: np.ndarray = torch.Tensor.numpy(self)
    arr.flags["WRITEABLE"] = False
    return arr

reshape(self, *args, **kwargs)

reshape(*shape) -> Tensor

Returns a tensor with the same data and number of elements as :attr:self but with the specified shape. This method returns a view if :attr:shape is compatible with the current shape. See :meth:torch.Tensor.view on when it is possible to return a view.

See :func:torch.reshape

Parameters:

Name	Type	Description	Default
shape	tuple of ints or int...	the desired shape	required

Source code in evotorch/tools/readonlytensor.py

def reshape(self, *args, **kwargs) -> torch.Tensor:
    result = super().reshape(*args, **kwargs)
    return self.__mutable_if_independent(result)

as_read_only_tensor(x, *, dtype=None, device=None)

Convert the given object to a ReadOnlyTensor.

The provided object can be a scalar, or an Iterable of numeric data, or an ObjectArray.

This function can be thought as the read-only counterpart of PyTorch's torch.as_tensor(...) function.

Parameters:

Name	Type	Description	Default
x	Any	The object to be converted to a ReadOnlyTensor.	required
dtype	Optional[torch.dtype]	The dtype of the new ReadOnlyTensor (e.g. torch.float32). If this argument is not specified, dtype will be inferred from x. For example, if x is a PyTorch tensor or a numpy array, its existing dtype will be kept.	None
device	Union[str, torch.device]	The device in which the ReadOnlyTensor will be stored (e.g. "cpu"). If this argument is not specified, the device which is storing the original x will be re-used.	None

Returns:

Type	Description
Iterable	The read-only counterpart of the provided object.

Source code in evotorch/tools/readonlytensor.py

def as_read_only_tensor(
    x: Any, *, dtype: Optional[torch.dtype] = None, device: Optional[Union[str, torch.device]] = None
) -> Iterable:
    """
    Convert the given object to a ReadOnlyTensor.

    The provided object can be a scalar, or an Iterable of numeric data,
    or an ObjectArray.

    This function can be thought as the read-only counterpart of PyTorch's
    `torch.as_tensor(...)` function.

    Args:
        x: The object to be converted to a ReadOnlyTensor.
        dtype: The dtype of the new ReadOnlyTensor (e.g. torch.float32).
            If this argument is not specified, dtype will be inferred from `x`.
            For example, if `x` is a PyTorch tensor or a numpy array, its
            existing dtype will be kept.
        device: The device in which the ReadOnlyTensor will be stored
            (e.g. "cpu").
            If this argument is not specified, the device which is storing
            the original `x` will be re-used.
    Returns:
        The read-only counterpart of the provided object.
    """
    from .objectarray import ObjectArray

    kwargs = _device_and_dtype_kwargs(dtype=dtype, device=device)
    if isinstance(x, ObjectArray):
        if len(kwargs) != 0:
            raise ValueError(
                f"read_only_tensor(...): when making a read-only tensor from an ObjectArray,"
                f" the arguments `dtype` and `device` were not expected."
                f" However, the received keyword arguments are: {kwargs}."
            )
        return x.get_read_only_view()
    else:
        return torch.as_tensor(x, **kwargs).as_subclass(ReadOnlyTensor)

read_only_tensor(x, *, dtype=None, device=None)

Make a ReadOnlyTensor from the given object.

The provided object can be a scalar, or an Iterable of numeric data, or an ObjectArray.

This function can be thought as the read-only counterpart of PyTorch's torch.tensor(...) function.

Parameters:

Name	Type	Description	Default
x	Any	The object from which the new ReadOnlyTensor will be made.	required
dtype	Optional[torch.dtype]	The dtype of the new ReadOnlyTensor (e.g. torch.float32).	None
device	Union[str, torch.device]	The device in which the ReadOnlyTensor will be stored (e.g. "cpu").	None

Returns:

Type	Description
Iterable	The new read-only tensor.

Source code in evotorch/tools/readonlytensor.py

def read_only_tensor(
    x: Any, *, dtype: Optional[torch.dtype] = None, device: Optional[Union[str, torch.device]] = None
) -> Iterable:
    """
    Make a ReadOnlyTensor from the given object.

    The provided object can be a scalar, or an Iterable of numeric data,
    or an ObjectArray.

    This function can be thought as the read-only counterpart of PyTorch's
    `torch.tensor(...)` function.

    Args:
        x: The object from which the new ReadOnlyTensor will be made.
        dtype: The dtype of the new ReadOnlyTensor (e.g. torch.float32).
        device: The device in which the ReadOnlyTensor will be stored
            (e.g. "cpu").
    Returns:
        The new read-only tensor.
    """
    from .objectarray import ObjectArray

    kwargs = _device_and_dtype_kwargs(dtype=dtype, device=device)
    if isinstance(x, ObjectArray):
        if len(kwargs) != 0:
            raise ValueError(
                f"read_only_tensor(...): when making a read-only tensor from an ObjectArray,"
                f" the arguments `dtype` and `device` were not expected."
                f" However, the received keyword arguments are: {kwargs}."
            )
        return x.get_read_only_view()
    else:
        return torch.as_tensor(x, **kwargs).as_subclass(ReadOnlyTensor)