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4926a51010
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/28690 Test Plan: Imported from OSS Differential Revision: D18333355 Pulled By: ifedan fbshipit-source-id: e02bd556e7b336bb02cd9ec89029a0e5f4f7cbe7
45 lines
1.7 KiB
Python
45 lines
1.7 KiB
Python
import torch
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from collections import OrderedDict
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class Parameter(torch.Tensor):
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r"""A kind of Tensor that is to be considered a module parameter.
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Parameters are :class:`~torch.Tensor` subclasses, that have a
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very special property when used with :class:`Module` s - when they're
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assigned as Module attributes they are automatically added to the list of
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its parameters, and will appear e.g. in :meth:`~Module.parameters` iterator.
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Assigning a Tensor doesn't have such effect. This is because one might
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want to cache some temporary state, like last hidden state of the RNN, in
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the model. If there was no such class as :class:`Parameter`, these
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temporaries would get registered too.
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Arguments:
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data (Tensor): parameter tensor.
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requires_grad (bool, optional): if the parameter requires gradient. See
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:ref:`excluding-subgraphs` for more details. Default: `True`
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"""
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def __new__(cls, data=None, requires_grad=True):
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if data is None:
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data = torch.Tensor()
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return torch.Tensor._make_subclass(cls, data, requires_grad)
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def __deepcopy__(self, memo):
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if id(self) in memo:
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return memo[id(self)]
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else:
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result = type(self)(self.data.clone(memory_format=torch.preserve_format), self.requires_grad)
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memo[id(self)] = result
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return result
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def __repr__(self):
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return 'Parameter containing:\n' + super(Parameter, self).__repr__()
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def __reduce_ex__(self, proto):
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# See Note [Don't serialize hooks]
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return (
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torch._utils._rebuild_parameter,
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(self.data, self.requires_grad, OrderedDict())
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)
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