docs/source/notes/serialization.rst - platform/external/pytorch - Git at Google


 Serialization semantics
 =======================

 This note describes how you can save and load PyTorch tensors and module states
 in Python, and how to serialize Python modules so they can be loaded in C++.

 .. contents:: Table of Contents

 .. _saving-loading-tensors:

 Saving and loading tensors
 --------------------------

 :func:`torch.save` and :func:`torch.load` let you easily save and load tensors:

 ::

     >>> t = torch.tensor([1., 2.])
     >>> torch.save(t, 'tensor.pt')
     >>> torch.load('tensor.pt')
     tensor([1., 2.])

 By convention, PyTorch files are typically written with a ‘.pt’ or ‘.pth’ extension.

 :func:`torch.save` and :func:`torch.load` use Python’s pickle by default,
 so you can also save multiple tensors as part of Python objects like tuples,
 lists, and dicts:

 ::

     >>> d = {'a': torch.tensor([1., 2.]), 'b': torch.tensor([3., 4.])}
     >>> torch.save(d, 'tensor_dict.pt')
     >>> torch.load('tensor_dict.pt')
     {'a': tensor([1., 2.]), 'b': tensor([3., 4.])}

 Custom data structures that include PyTorch tensors can also be saved if the
 data structure is pickle-able.

 .. _preserve-storage-sharing:

 Saving and loading tensors preserves views
 ---------------------------------------------

 Saving tensors preserves their view relationships:

 ::

     >>> numbers = torch.arange(1, 10)
     >>> evens = numbers[1::2]
     >>> torch.save([numbers, evens], 'tensors.pt')
     >>> loaded_numbers, loaded_evens = torch.load('tensors.pt')
     >>> loaded_evens *= 2
     >>> loaded_numbers
     tensor([ 1,  4,  3,  8,  5, 12,  7, 16,  9])

 Behind the scenes, these tensors share the same "storage." See
 `Tensor Views <https://pytorch.org/docs/master/tensor_view.html>`_ for more
 on views and storage.

 When PyTorch saves tensors it saves their storage objects and tensor
 metadata separately. This is an implementation detail that may change in the
 future, but it typically saves space and lets PyTorch easily
 reconstruct the view relationships between the loaded tensors. In the above
 snippet, for example, only a single storage is written to 'tensors.pt'.

 In some cases, however, saving the current storage objects may be unnecessary
 and create prohibitively large files. In the following snippet a storage much
 larger than the saved tensor is written to a file:

 ::

     >>> large = torch.arange(1, 1000)
     >>> small = large[0:5]
     >>> torch.save(small, 'small.pt')
     >>> loaded_small = torch.load('small.pt')
     >>> loaded_small.storage().size()
     999

 Instead of saving only the five values in the `small` tensor to 'small.pt,'
 the 999 values in the storage it shares with `large` were saved and loaded.

 When saving tensors with fewer elements than their storage objects, the size of
 the saved file can be reduced by first cloning the tensors. Cloning a tensor
 produces a new tensor with a new storage object containing only the values
 in the tensor:

 ::

     >>> large = torch.arange(1, 1000)
     >>> small = large[0:5]
     >>> torch.save(small.clone(), 'small.pt')  # saves a clone of small
     >>> loaded_small = torch.load('small.pt')
     >>> loaded_small.storage().size()
     5

 Since the cloned tensors are independent of each other, however, they have
 none of the view relationships the original tensors did. If both file size and
 view relationships are important when saving tensors smaller than their
 storage objects, then care must be taken to construct new tensors that minimize
 the size of their storage objects but still have the desired view relationships
 before saving.

 .. _saving-loading-python-modules:

 Saving and loading torch.nn.Modules
 -----------------------------------

 See also: `Tutorial: Saving and loading modules <https://pytorch.org/tutorials/beginner/saving_loading_models.html>`_

 In PyTorch, a module’s state is frequently serialized using a ‘state dict.’
 A module’s state dict contains all of its parameters and persistent buffers:

 ::

     >>> bn = torch.nn.BatchNorm1d(3, track_running_stats=True)
     >>> list(bn.named_parameters())
     [('weight', Parameter containing: tensor([1., 1., 1.], requires_grad=True)),
      ('bias', Parameter containing: tensor([0., 0., 0.], requires_grad=True))]

     >>> list(bn.named_buffers())
     [('running_mean', tensor([0., 0., 0.])),
      ('running_var', tensor([1., 1., 1.])),
      ('num_batches_tracked', tensor(0))]

     >>> bn.state_dict()
     OrderedDict([('weight', tensor([1., 1., 1.])),
                  ('bias', tensor([0., 0., 0.])),
                  ('running_mean', tensor([0., 0., 0.])),
                  ('running_var', tensor([1., 1., 1.])),
                  ('num_batches_tracked', tensor(0))])

 Instead of saving a module directly, for compatibility reasons it is recommended
 to instead save only its state dict. Python modules even have a function,
 :meth:`~torch.nn.Module.load_state_dict`, to restore their states from a state dict:

 ::

     >>> torch.save(bn.state_dict(), 'bn.pt')
     >>> bn_state_dict = torch.load('bn.pt')
     >>> new_bn = torch.nn.BatchNorm1d(3, track_running_stats=True)
     >>> new_bn.load_state_dict(bn_state_dict)
     <All keys matched successfully>

 Note that the state dict is first loaded from its file with :func:`torch.load`
 and the state then restored with :meth:`~torch.nn.Module.load_state_dict`.

 Even custom modules and modules containing other modules have state dicts and
 can use this pattern:

 ::

     # A module with two linear layers
     >>> class MyModule(torch.nn.Module):
           def __init__(self):
             super(MyModule, self).__init__()
             self.l0 = torch.nn.Linear(4, 2)
             self.l1 = torch.nn.Linear(2, 1)

           def forward(self, input):
             out0 = self.l0(input)
             out0_relu = torch.nn.functional.relu(out0)
             return self.l1(out0_relu)

     >>> m = MyModule()
     >>> m.state_dict()
     OrderedDict([('l0.weight', tensor([[ 0.1400, 0.4563, -0.0271, -0.4406],
                                        [-0.3289, 0.2827, 0.4588, 0.2031]])),
                  ('l0.bias', tensor([ 0.0300, -0.1316])),
                  ('l1.weight', tensor([[0.6533, 0.3413]])),
                  ('l1.bias', tensor([-0.1112]))])

     >>> torch.save(m.state_dict(), 'mymodule.pt')
     >>> m_state_dict = torch.load('mymodule.pt')
     >>> new_m = MyModule()
     >>> new_m.load_state_dict(m_state_dict)
     <All keys matched successfully>

 .. _serializing-python-modules:

 Serializing torch.nn.Modules and loading them in C++
 ----------------------------------------------------

 See also: `Tutorial: Loading a TorchScript Model in C++ <https://pytorch.org/tutorials/advanced/cpp_export.html>`_

 ScriptModules can be serialized as a TorchScript program and loaded
 using :func:`torch.jit.load`.
 This serialization encodes all the modules’ methods, submodules, parameters,
 and attributes, and it allows the serialized program to be loaded in C++
 (i.e. without Python).

 The distinction between :func:`torch.jit.save` and :func:`torch.save` may not
 be immediately clear. :func:`torch.save` saves Python objects with pickle.
 This is especially useful for prototyping, researching, and training.
 :func:`torch.jit.save`, on the other hand, serializes ScriptModules to a format
 that can be loaded in Python or C++. This is useful when saving and loading C++
 modules or for running modules trained in Python with C++, a common practice
 when deploying PyTorch models.

 To script, serialize and load a module in Python:

 ::

     >>> scripted_module = torch.jit.script(MyModule())
     >>> torch.jit.save(scripted_module, 'mymodule.pt')
     >>> torch.jit.load('mymodule.pt')
     RecursiveScriptModule( original_name=MyModule
                           (l0): RecursiveScriptModule(original_name=Linear)
                           (l1): RecursiveScriptModule(original_name=Linear) )


 Traced modules can also be saved with :func:`torch.jit.save`, with the caveat
 that only the traced code path is serialized. The following example demonstrates
 this:

 ::

     # A module with control flow
     >>> class ControlFlowModule(torch.nn.Module):
           def __init__(self):
             super(ControlFlowModule, self).__init__()
             self.l0 = torch.nn.Linear(4, 2)
             self.l1 = torch.nn.Linear(2, 1)

           def forward(self, input):
             if input.dim() > 1:
             return torch.tensor(0)

             out0 = self.l0(input)
             out0_relu = torch.nn.functional.relu(out0)
             return self.l1(out0_relu)

     >>> traced_module = torch.jit.trace(ControlFlowModule(), torch.randn(4))
     >>> torch.jit.save(traced_module, 'controlflowmodule_traced.pt')
     >>> loaded = torch.jit.load('controlflowmodule_traced.pt')
     >>> loaded(torch.randn(2, 4)))
     tensor([[-0.1571], [-0.3793]], grad_fn=<AddBackward0>)

     >>> scripted_module = torch.jit.script(ControlFlowModule(), torch.randn(4))
     >>> torch.jit.save(scripted_module, 'controlflowmodule_scripted.pt')
     >>> loaded = torch.jit.load('controlflowmodule_scripted.pt')
     >> loaded(torch.randn(2, 4))
     tensor(0)

 The above module has an if statement that is not triggered by the traced inputs,
 and so is not part of the traced module and not serialized with it.
 The scripted module, however, contains the if statement and is serialized with it.
 See the `TorchScript documentation <https://pytorch.org/docs/stable/jit.html>`_
 for more on scripting and tracing.

 Finally, to load the module in C++:

 ::

     >>> torch::jit::script::Module module;
     >>> module = torch::jit::load('controlflowmodule_scripted.pt');

 See the `PyTorch C++ API documentation <https://pytorch.org/cppdocs/>`_
 for details about how to use PyTorch modules in C++.

 .. _saving-loading-across-versions:

 Saving and loading ScriptModules across PyTorch versions
 -----------------------------------------------------------

 The PyTorch Team recommends saving and loading modules with the same version of
 PyTorch. Older versions of PyTorch may not support newer modules, and newer
 versions may have removed or modified older behavior. These changes are
 explicitly described in
 PyTorch’s `release notes <https://github.com/pytorch/pytorch/releases>`_,
 and modules relying on functionality that has changed may need to be updated
 to continue working properly. In limited cases, detailed below, PyTorch will
 preserve the historic behavior of serialized ScriptModules so they do not require
 an update.

 torch.div performing integer division
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

 In PyTorch 1.5 and earlier :func:`torch.div` would perform floor division when
 given two integer inputs:

 ::

     # PyTorch 1.5 (and earlier)
     >>> a = torch.tensor(5)
     >>> b = torch.tensor(3)
     >>> a / b
     tensor(1)

 In PyTorch 1.7, however, :func:`torch.div` will always perform a true division
 of its inputs, just like division in Python 3:

 ::

     # PyTorch 1.7
     >>> a = torch.tensor(5)
     >>> b = torch.tensor(3)
     >>> a / b
     tensor(1.6667)

 The behavior of :func:`torch.div` is preserved in serialized ScriptModules.
 That is, ScriptModules serialized with versions of PyTorch before 1.6 will continue
 to see :func:`torch.div` perform floor division when given two integer inputs
 even when loaded with newer versions of PyTorch. ScriptModules using :func:`torch.div`
 and serialized on PyTorch 1.6 and later cannot be loaded in earlier versions of
 PyTorch, however, since those earlier versions do not understand the new behavior.

 torch.full always inferring a float dtype
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

 In PyTorch 1.5 and earlier :func:`torch.full` always returned a float tensor,
 regardless of the fill value it’s given:

 ::

     # PyTorch 1.5 and earlier
     >>> torch.full((3,), 1)  # Note the integer fill value...
     tensor([1., 1., 1.])     # ...but float tensor!

 In PyTorch 1.7, however, :func:`torch.full` will infer the returned tensor’s
 dtype from the fill value:

 ::

     # PyTorch 1.7
     >>> torch.full((3,), 1)
     tensor([1, 1, 1])

     >>> torch.full((3,), True)
     tensor([True, True, True])

     >>> torch.full((3,), 1.)
     tensor([1., 1., 1.])

     >>> torch.full((3,), 1 + 1j)
     tensor([1.+1.j, 1.+1.j, 1.+1.j])

 The behavior of :func:`torch.full` is preserved in serialized ScriptModules. That is,
 ScriptModules serialized with versions of PyTorch before 1.6 will continue to see
 torch.full return float tensors by default, even when given bool or
 integer fill values. ScriptModules using :func:`torch.full` and serialized on PyTorch 1.6
 and later cannot be loaded in earlier versions of PyTorch, however, since those
 earlier versions do not understand the new behavior.

	Serialization semantics
	=======================

	This note describes how you can save and load PyTorch tensors and module states
	in Python, and how to serialize Python modules so they can be loaded in C++.

	.. contents:: Table of Contents

	.. _saving-loading-tensors:

	Saving and loading tensors
	--------------------------

	:func:`torch.save` and :func:`torch.load` let you easily save and load tensors:

	::

	>>> t = torch.tensor([1., 2.])
	>>> torch.save(t, 'tensor.pt')
	>>> torch.load('tensor.pt')
	tensor([1., 2.])

	By convention, PyTorch files are typically written with a ‘.pt’ or ‘.pth’ extension.

	:func:`torch.save` and :func:`torch.load` use Python’s pickle by default,
	so you can also save multiple tensors as part of Python objects like tuples,
	lists, and dicts:

	::

	>>> d = {'a': torch.tensor([1., 2.]), 'b': torch.tensor([3., 4.])}
	>>> torch.save(d, 'tensor_dict.pt')
	>>> torch.load('tensor_dict.pt')
	{'a': tensor([1., 2.]), 'b': tensor([3., 4.])}

	Custom data structures that include PyTorch tensors can also be saved if the
	data structure is pickle-able.

	.. _preserve-storage-sharing:

	Saving and loading tensors preserves views
	---------------------------------------------

	Saving tensors preserves their view relationships:

	::

	>>> numbers = torch.arange(1, 10)
	>>> evens = numbers[1::2]
	>>> torch.save([numbers, evens], 'tensors.pt')
	>>> loaded_numbers, loaded_evens = torch.load('tensors.pt')
	>>> loaded_evens *= 2
	>>> loaded_numbers
	tensor([ 1, 4, 3, 8, 5, 12, 7, 16, 9])

	Behind the scenes, these tensors share the same "storage." See
	`Tensor Views <https://pytorch.org/docs/master/tensor_view.html>`_ for more
	on views and storage.

	When PyTorch saves tensors it saves their storage objects and tensor
	metadata separately. This is an implementation detail that may change in the
	future, but it typically saves space and lets PyTorch easily
	reconstruct the view relationships between the loaded tensors. In the above
	snippet, for example, only a single storage is written to 'tensors.pt'.

	In some cases, however, saving the current storage objects may be unnecessary
	and create prohibitively large files. In the following snippet a storage much
	larger than the saved tensor is written to a file:

	::

	>>> large = torch.arange(1, 1000)
	>>> small = large[0:5]
	>>> torch.save(small, 'small.pt')
	>>> loaded_small = torch.load('small.pt')
	>>> loaded_small.storage().size()
	999

	Instead of saving only the five values in the `small` tensor to 'small.pt,'
	the 999 values in the storage it shares with `large` were saved and loaded.

	When saving tensors with fewer elements than their storage objects, the size of
	the saved file can be reduced by first cloning the tensors. Cloning a tensor
	produces a new tensor with a new storage object containing only the values
	in the tensor:

	::

	>>> large = torch.arange(1, 1000)
	>>> small = large[0:5]
	>>> torch.save(small.clone(), 'small.pt') # saves a clone of small
	>>> loaded_small = torch.load('small.pt')
	>>> loaded_small.storage().size()
	5

	Since the cloned tensors are independent of each other, however, they have
	none of the view relationships the original tensors did. If both file size and
	view relationships are important when saving tensors smaller than their
	storage objects, then care must be taken to construct new tensors that minimize
	the size of their storage objects but still have the desired view relationships
	before saving.

	.. _saving-loading-python-modules:

	Saving and loading torch.nn.Modules
	-----------------------------------

	See also: `Tutorial: Saving and loading modules <https://pytorch.org/tutorials/beginner/saving_loading_models.html>`_

	In PyTorch, a module’s state is frequently serialized using a ‘state dict.’
	A module’s state dict contains all of its parameters and persistent buffers:

	::

	>>> bn = torch.nn.BatchNorm1d(3, track_running_stats=True)
	>>> list(bn.named_parameters())
	[('weight', Parameter containing: tensor([1., 1., 1.], requires_grad=True)),
	('bias', Parameter containing: tensor([0., 0., 0.], requires_grad=True))]

	>>> list(bn.named_buffers())
	[('running_mean', tensor([0., 0., 0.])),
	('running_var', tensor([1., 1., 1.])),
	('num_batches_tracked', tensor(0))]

	>>> bn.state_dict()
	OrderedDict([('weight', tensor([1., 1., 1.])),
	('bias', tensor([0., 0., 0.])),
	('running_mean', tensor([0., 0., 0.])),
	('running_var', tensor([1., 1., 1.])),
	('num_batches_tracked', tensor(0))])

	Instead of saving a module directly, for compatibility reasons it is recommended
	to instead save only its state dict. Python modules even have a function,
	:meth:`~torch.nn.Module.load_state_dict`, to restore their states from a state dict:

	::

	>>> torch.save(bn.state_dict(), 'bn.pt')
	>>> bn_state_dict = torch.load('bn.pt')
	>>> new_bn = torch.nn.BatchNorm1d(3, track_running_stats=True)
	>>> new_bn.load_state_dict(bn_state_dict)
	<All keys matched successfully>

	Note that the state dict is first loaded from its file with :func:`torch.load`
	and the state then restored with :meth:`~torch.nn.Module.load_state_dict`.

	Even custom modules and modules containing other modules have state dicts and
	can use this pattern:

	::

	# A module with two linear layers
	>>> class MyModule(torch.nn.Module):
	def __init__(self):
	super(MyModule, self).__init__()
	self.l0 = torch.nn.Linear(4, 2)
	self.l1 = torch.nn.Linear(2, 1)

	def forward(self, input):
	out0 = self.l0(input)
	out0_relu = torch.nn.functional.relu(out0)
	return self.l1(out0_relu)

	>>> m = MyModule()
	>>> m.state_dict()
	OrderedDict([('l0.weight', tensor([[ 0.1400, 0.4563, -0.0271, -0.4406],
	[-0.3289, 0.2827, 0.4588, 0.2031]])),
	('l0.bias', tensor([ 0.0300, -0.1316])),
	('l1.weight', tensor([[0.6533, 0.3413]])),
	('l1.bias', tensor([-0.1112]))])

	>>> torch.save(m.state_dict(), 'mymodule.pt')
	>>> m_state_dict = torch.load('mymodule.pt')
	>>> new_m = MyModule()
	>>> new_m.load_state_dict(m_state_dict)
	<All keys matched successfully>

	.. _serializing-python-modules:

	Serializing torch.nn.Modules and loading them in C++
	----------------------------------------------------

	See also: `Tutorial: Loading a TorchScript Model in C++ <https://pytorch.org/tutorials/advanced/cpp_export.html>`_

	ScriptModules can be serialized as a TorchScript program and loaded
	using :func:`torch.jit.load`.
	This serialization encodes all the modules’ methods, submodules, parameters,
	and attributes, and it allows the serialized program to be loaded in C++
	(i.e. without Python).

	The distinction between :func:`torch.jit.save` and :func:`torch.save` may not
	be immediately clear. :func:`torch.save` saves Python objects with pickle.
	This is especially useful for prototyping, researching, and training.
	:func:`torch.jit.save`, on the other hand, serializes ScriptModules to a format
	that can be loaded in Python or C++. This is useful when saving and loading C++
	modules or for running modules trained in Python with C++, a common practice
	when deploying PyTorch models.

	To script, serialize and load a module in Python:

	::

	>>> scripted_module = torch.jit.script(MyModule())
	>>> torch.jit.save(scripted_module, 'mymodule.pt')
	>>> torch.jit.load('mymodule.pt')
	RecursiveScriptModule( original_name=MyModule
	(l0): RecursiveScriptModule(original_name=Linear)
	(l1): RecursiveScriptModule(original_name=Linear) )


	Traced modules can also be saved with :func:`torch.jit.save`, with the caveat
	that only the traced code path is serialized. The following example demonstrates
	this:

	::

	# A module with control flow
	>>> class ControlFlowModule(torch.nn.Module):
	def __init__(self):
	super(ControlFlowModule, self).__init__()
	self.l0 = torch.nn.Linear(4, 2)
	self.l1 = torch.nn.Linear(2, 1)

	def forward(self, input):
	if input.dim() > 1:
	return torch.tensor(0)

	out0 = self.l0(input)
	out0_relu = torch.nn.functional.relu(out0)
	return self.l1(out0_relu)

	>>> traced_module = torch.jit.trace(ControlFlowModule(), torch.randn(4))
	>>> torch.jit.save(traced_module, 'controlflowmodule_traced.pt')
	>>> loaded = torch.jit.load('controlflowmodule_traced.pt')
	>>> loaded(torch.randn(2, 4)))
	tensor([[-0.1571], [-0.3793]], grad_fn=<AddBackward0>)

	>>> scripted_module = torch.jit.script(ControlFlowModule(), torch.randn(4))
	>>> torch.jit.save(scripted_module, 'controlflowmodule_scripted.pt')
	>>> loaded = torch.jit.load('controlflowmodule_scripted.pt')
	>> loaded(torch.randn(2, 4))
	tensor(0)

	The above module has an if statement that is not triggered by the traced inputs,
	and so is not part of the traced module and not serialized with it.
	The scripted module, however, contains the if statement and is serialized with it.
	See the `TorchScript documentation <https://pytorch.org/docs/stable/jit.html>`_
	for more on scripting and tracing.

	Finally, to load the module in C++:

	::

	>>> torch::jit::script::Module module;
	>>> module = torch::jit::load('controlflowmodule_scripted.pt');

	See the `PyTorch C++ API documentation <https://pytorch.org/cppdocs/>`_
	for details about how to use PyTorch modules in C++.

	.. _saving-loading-across-versions:

	Saving and loading ScriptModules across PyTorch versions
	-----------------------------------------------------------

	The PyTorch Team recommends saving and loading modules with the same version of
	PyTorch. Older versions of PyTorch may not support newer modules, and newer
	versions may have removed or modified older behavior. These changes are
	explicitly described in
	PyTorch’s `release notes <https://github.com/pytorch/pytorch/releases>`_,
	and modules relying on functionality that has changed may need to be updated
	to continue working properly. In limited cases, detailed below, PyTorch will
	preserve the historic behavior of serialized ScriptModules so they do not require
	an update.

	torch.div performing integer division
	^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

	In PyTorch 1.5 and earlier :func:`torch.div` would perform floor division when
	given two integer inputs:

	::

	# PyTorch 1.5 (and earlier)
	>>> a = torch.tensor(5)
	>>> b = torch.tensor(3)
	>>> a / b
	tensor(1)

	In PyTorch 1.7, however, :func:`torch.div` will always perform a true division
	of its inputs, just like division in Python 3:

	::

	# PyTorch 1.7
	>>> a = torch.tensor(5)
	>>> b = torch.tensor(3)
	>>> a / b
	tensor(1.6667)

	The behavior of :func:`torch.div` is preserved in serialized ScriptModules.
	That is, ScriptModules serialized with versions of PyTorch before 1.6 will continue
	to see :func:`torch.div` perform floor division when given two integer inputs
	even when loaded with newer versions of PyTorch. ScriptModules using :func:`torch.div`
	and serialized on PyTorch 1.6 and later cannot be loaded in earlier versions of
	PyTorch, however, since those earlier versions do not understand the new behavior.

	torch.full always inferring a float dtype
	^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

	In PyTorch 1.5 and earlier :func:`torch.full` always returned a float tensor,
	regardless of the fill value it’s given:

	::

	# PyTorch 1.5 and earlier
	>>> torch.full((3,), 1) # Note the integer fill value...
	tensor([1., 1., 1.]) # ...but float tensor!

	In PyTorch 1.7, however, :func:`torch.full` will infer the returned tensor’s
	dtype from the fill value:

	::

	# PyTorch 1.7
	>>> torch.full((3,), 1)
	tensor([1, 1, 1])

	>>> torch.full((3,), True)
	tensor([True, True, True])

	>>> torch.full((3,), 1.)
	tensor([1., 1., 1.])

	>>> torch.full((3,), 1 + 1j)
	tensor([1.+1.j, 1.+1.j, 1.+1.j])

	The behavior of :func:`torch.full` is preserved in serialized ScriptModules. That is,
	ScriptModules serialized with versions of PyTorch before 1.6 will continue to see
	torch.full return float tensors by default, even when given bool or
	integer fill values. ScriptModules using :func:`torch.full` and serialized on PyTorch 1.6
	and later cannot be loaded in earlier versions of PyTorch, however, since those
	earlier versions do not understand the new behavior.