>>> 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.
torch.save() and 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.
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 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()
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()
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.
See also: Tutorial: Saving and loading modules
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,
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 torch.load()
and the state then restored with 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().__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>
See also: Tutorial: Loading a TorchScript Model in C++
ScriptModules can be serialized as a TorchScript program and loaded
using 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 torch.jit.save() and torch.save() may not
be immediately clear. torch.save() saves Python objects with pickle.
This is especially useful for prototyping, researching, and training.
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 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().__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
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
for details about how to use PyTorch modules in C++.
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,
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.
In PyTorch 1.5 and earlier 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, 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 torch.div() is preserved in serialized ScriptModules.
That is, ScriptModules serialized with versions of PyTorch before 1.6 will continue
to see torch.div() perform floor division when given two integer inputs
even when loaded with newer versions of PyTorch. ScriptModules using 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.
In PyTorch 1.5 and earlier 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, 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 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 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
