parameter.py

from collections import OrderedDict

import torch
from torch._C import _disabled_torch_function_impl


# Metaclass to combine _TensorMeta and the instance check override for Parameter.
class _ParameterMeta(torch._C._TensorMeta):
    # Make `isinstance(t, Parameter)` return True for custom tensor instances that have the _is_param flag.
    def __instancecheck__(self, instance):
        if self is Parameter:
            if isinstance(instance, torch.Tensor) and getattr(
                instance, "_is_param", False
            ):
                return True
        return super().__instancecheck__(instance)


class Parameter(torch.Tensor, metaclass=_ParameterMeta):
    r"""A kind of Tensor that is to be considered a module parameter.

    Parameters are :class:`~torch.Tensor` subclasses, that have a
    very special property when used with :class:`Module` s - when they're
    assigned as Module attributes they are automatically added to the list of
    its parameters, and will appear e.g. in :meth:`~Module.parameters` iterator.
    Assigning a Tensor doesn't have such effect. This is because one might
    want to cache some temporary state, like last hidden state of the RNN, in
    the model. If there was no such class as :class:`Parameter`, these
    temporaries would get registered too.

    Args:
        data (Tensor): parameter tensor.
        requires_grad (bool, optional): if the parameter requires gradient. Note that
            the torch.no_grad() context does NOT affect the default behavior of
            Parameter creation--the Parameter will still have `requires_grad=True` in
            :class:`~no_grad` mode. See :ref:`locally-disable-grad-doc` for more
            details. Default: `True`
    """

    def __new__(cls, data=None, requires_grad=True):
        if data is None:
            data = torch.empty(0)
        if type(data) is torch.Tensor or type(data) is Parameter:
            # For ease of BC maintenance, keep this path for standard Tensor.
            # Eventually (tm), we should change the behavior for standard Tensor to match.
            return torch.Tensor._make_subclass(cls, data, requires_grad)

        # Path for custom tensors: set a flag on the instance to indicate parameter-ness.
        t = data.detach().requires_grad_(requires_grad)
        if type(t) is not type(data):
            raise RuntimeError(
                f"Creating a Parameter from an instance of type {type(data).__name__} "
                "requires that detach() returns an instance of the same type, but return "
                f"type {type(t).__name__} was found instead. To use the type as a "
                "Parameter, please correct the detach() semantics defined by "
                "its __torch_dispatch__() implementation."
            )
        t._is_param = True
        return t

    # Note: the 3 methods below only apply to standard Tensor. Parameters of custom tensor types
    # are still considered that custom tensor type and these methods will not be called for them.
    def __deepcopy__(self, memo):
        if id(self) in memo:
            return memo[id(self)]
        else:
            result = type(self)(
                self.data.clone(memory_format=torch.preserve_format), self.requires_grad
            )
            memo[id(self)] = result
            return result

    def __repr__(self):
        return "Parameter containing:\n" + super().__repr__()

    def __reduce_ex__(self, proto):
        state = torch._utils._get_obj_state(self)

        # See Note [Don't serialize hooks]
        hooks = OrderedDict()
        if not state:
            return (
                torch._utils._rebuild_parameter,
                (self.data, self.requires_grad, hooks),
            )

        return (
            torch._utils._rebuild_parameter_with_state,
            (self.data, self.requires_grad, hooks, state),
        )

    __torch_function__ = _disabled_torch_function_impl


class UninitializedTensorMixin:
    _allowed_methods = [
        torch.Tensor.__hash__,
        torch.Tensor.size,
        torch.Tensor.copy_,
        torch.Tensor.is_complex,
        torch.Tensor.is_floating_point,
        torch.Tensor.half,
        torch.Tensor.float,
        torch.Tensor.double,
        torch.Tensor.char,
        torch.Tensor.short,
        torch.Tensor.int,
        torch.Tensor.long,
        torch.Tensor.cuda,
        torch.Tensor.cpu,
        torch.Tensor.to,
        torch.Tensor.get_device,
        torch._has_compatible_shallow_copy_type,
    ]

    def materialize(self, shape, device=None, dtype=None):
        r"""Create a Parameter or Tensor with the same properties of the uninitialized one.

        Given a shape, it materializes a parameter in the same device
        and with the same `dtype` as the current one or the specified ones in the
        arguments.

        Args:
            shape : (tuple): the shape for the materialized tensor.
            device (:class:`torch.device`): the desired device of the parameters
                and buffers in this module. Optional.
            dtype (:class:`torch.dtype`): the desired floating point type of
                the floating point parameters and buffers in this module. Optional.
        """
        if device is None:
            device = self.data.device
        if dtype is None:
            dtype = self.data.dtype
        self.data = torch.empty(shape, device=device, dtype=dtype)
        self.__class__ = self.cls_to_become

    @property
    def shape(self):
        raise RuntimeError(
            "Can't access the shape of an uninitialized parameter or buffer. "
            "This error usually happens in `load_state_dict` when trying to load "
            "an uninitialized parameter into an initialized one. "
            "Call `forward` to initialize the parameters before accessing their attributes."
        )

    def share_memory_(self):
        raise RuntimeError(
            "Can't share memory on an uninitialized parameter or buffer. "
            "Call `forward` to initialize the parameters before calling "
            "`module.share_memory()`."
        )

    def __repr__(self):
        return f"<{self.__class__.__name__}>"

    def __reduce_ex__(self, proto):
        # See Note [Don't serialize hooks]
        return (self.__class__, (self.requires_grad,))

    @classmethod
    def __torch_function__(cls, func, types, args=(), kwargs=None):
        # method-wrapper is to detect access to Tensor properties that are
        # wrapped in descriptors
        if func in cls._allowed_methods or func.__class__.__name__ == "method-wrapper":
            if kwargs is None:
                kwargs = {}
            return super().__torch_function__(func, types, args, kwargs)
        raise ValueError(
            f"Attempted to use an uninitialized parameter in {func}. "
            "This error happens when you are using a `LazyModule` or "
            f"explicitly manipulating `torch.nn.parameter.{cls.__name__}` "
            "objects. When using LazyModules Call `forward` with a dummy batch "
            "to initialize the parameters before calling torch functions"
        )


def is_lazy(param):
    return isinstance(param, UninitializedTensorMixin)


class UninitializedParameter(UninitializedTensorMixin, Parameter):
    r"""A parameter that is not initialized.

    Uninitialized Parameters are a special case of :class:`torch.nn.Parameter`
    where the shape of the data is still unknown.

    Unlike a :class:`torch.nn.Parameter`, uninitialized parameters
    hold no data and attempting to access some properties, like their shape,
    will throw a runtime error. The only operations that can be performed on a uninitialized
    parameter are changing its datatype, moving it to a different device and
    converting it to a regular :class:`torch.nn.Parameter`.

    The default device or dtype to use when the parameter is materialized can be set
    during construction using e.g. ``device='cuda'``.
    """

    cls_to_become = Parameter

    def __new__(cls, requires_grad=True, device=None, dtype=None) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        data = torch.empty(0, **factory_kwargs)
        return torch.Tensor._make_subclass(cls, data, requires_grad)

    def __deepcopy__(self, memo):
        if id(self) in memo:
            return memo[id(self)]
        else:
            result = type(self)(self.requires_grad, self.data.device, self.data.dtype)
            memo[id(self)] = result
            return result


# Metaclass to combine _TensorMeta and the instance check override for Buffer.
class _BufferMeta(torch._C._TensorMeta):
    # Make `isinstance(t, Buffer)` return True for custom tensor instances that have the _is_buffer flag.
    def __instancecheck__(self, instance):
        if self is Buffer:
            if isinstance(instance, torch.Tensor) and getattr(
                instance, "_is_buffer", False
            ):
                return True
        return super().__instancecheck__(instance)


class Buffer(torch.Tensor, metaclass=_BufferMeta):
    r"""A kind of Tensor that should not be considered a model
    parameter. For example, BatchNorm's ``running_mean`` is not a parameter, but is part of the module's state.

    Buffers are :class:`~torch.Tensor` subclasses, that have a
    very special property when used with :class:`Module` s -- when they're
    assigned as Module attributes they are automatically added to the list of
    its buffers, and will appear e.g. in :meth:`~torch.nn.Module.buffers` iterator.
    Assigning a Tensor doesn't have such effect. One can still assign a Tensor as explicitly by using
    the :meth:`~torch.nn.Module.register_buffer` function.

    Args:
        data (Tensor): buffer tensor.
        persistent (bool, optional): whether the buffer is part of the module's
            :attr:`state_dict`. Default: ``True``
    """

    def __new__(cls, data=None, *, persistent=True):
        if data is None:
            data = torch.empty(0)

        t = data.detach().requires_grad_(data.requires_grad)
        t.persistent = persistent
        t._is_buffer = True
        return t

    __torch_function__ = _disabled_torch_function_impl


class UninitializedBuffer(UninitializedTensorMixin, torch.Tensor):
    r"""A buffer that is not initialized.

    Uninitialized Buffer is a a special case of :class:`torch.Tensor`
    where the shape of the data is still unknown.

    Unlike a :class:`torch.Tensor`, uninitialized parameters
    hold no data and attempting to access some properties, like their shape,
    will throw a runtime error. The only operations that can be performed on a uninitialized
    parameter are changing its datatype, moving it to a different device and
    converting it to a regular :class:`torch.Tensor`.

    The default device or dtype to use when the buffer is materialized can be set
    during construction using e.g. ``device='cuda'``.
    """

    cls_to_become = torch.Tensor

    def __new__(
        cls, requires_grad=False, device=None, dtype=None, persistent=True
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        data = torch.empty(0, **factory_kwargs)
        ret = torch.Tensor._make_subclass(cls, data, requires_grad)
        ret.persistent = persistent
        ret._is_buffer = True
        return ret