
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/main/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().__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>


.. _serialized-file-format:

Serialized file format for ``torch.save``
-----------------------------------------

Since PyTorch 1.6.0, ``torch.save`` defaults to returning an uncompressed ZIP64
archive unless the user sets ``_use_new_zipfile_serialization=False``.

In this archive, the files are ordered as such

.. code-block:: text

    checkpoint.pth
    ├── data.pkl
    ├── byteorder  # added in PyTorch 2.1.0
    ├── data/
    │   ├── 0
    │   ├── 1
    │   ├── 2
    │   └── …
    └── version

The entries are as follows:
  * ``data.pkl`` is the result of pickling the object passed to ``torch.save``
    excluding ``torch.Storage`` objects that it contains
  * ``byteorder`` contains a string with the ``sys.byteorder`` when saving (“little” or “big”)
  * ``data/`` contains all the storages in the object, where each storage is a separate file
  * ``version`` contains a version number at save time that can be used at load time

When saving, PyTorch will ensure that the local file header of each file is padded
to an offset that is a multiple of 64 bytes, ensuring that the offset of each file
is 64-byte aligned.

.. note::
    Tensors on certain devices such as XLA are serialized as pickled numpy arrays. As
    such, their storages are not serialized. In these cases ``data/`` might not exist
    in the checkpoint.

.. _weights-only:

``torch.load`` with ``weights_only=True``
-----------------------------------------

Starting in version 2.6, ``torch.load`` will use ``weights_only=True`` if the ``pickle_module``
argument is not passed.

As discussed in the documentation for :func:`torch.load`, ``weights_only=True`` restricts
the unpickler used in ``torch.load`` to only executing functions/building classes required for
``state_dicts`` of plain ``torch.Tensors`` as well as some other primitive types. Further,
unlike the default ``Unpickler`` provided by the ``pickle`` module, the ``weights_only`` Unpickler
is not allowed to dynamically import anything during unpickling.

As mentioned above, saving a module's ``state_dict`` is a best practice when using ``torch.save``. If loading an old
checkpoint that contains an ``nn.Module``, we recommend ``weights_only=False``. When loading a checkpoint that contains
tensor subclasses, there will likely be functions/classes that need to be allowlisted, see below for further details.

If the ``weights_only`` Unpickler encounters a function or class that is not allowlisted
by default within the pickle file, you should see an actionable error like such

.. code::

    _pickle.UnpicklingError: Weights only load failed. This file can still be loaded,
    to do so you have two options, do those steps only if you trust the source of the checkpoint.
        1. Re-running `torch.load` with `weights_only` set to `False` will likely succeed,
            but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.
        2. Alternatively, to load with `weights_only=True` please check the recommended
           steps in the following error message.
           WeightsUnpickler error: Unsupported global: GLOBAL {__module__}.{__name__} was not an allowed global by
           default. Please use `torch.serialization.add_safe_globals([{__name__}])` or the
           `torch.serialization.safe_globals([{__name__}])` context manager to allowlist this global
           if you trust this class/function.

Please follow the steps in the error message and allowlist the functions or classes only if you trust them.

To get all GLOBALs (functions/classes) in the checkpoint that are not yet allowlisted you can use
:func:`torch.serialization.get_unsafe_globals_in_checkpoint` which will return a list of strings of the form
``{__module__}.{__name__}``. If you trust these functions/classes, you can import them and allowlist them per
the error message either via :func:`torch.serialization.add_safe_globals` or the context manager
:class:`torch.serialization.safe_globals`.

To access the list of user-allowlisted functions/classes you can use :func:`torch.serialization.get_safe_globals` and
to clear the current list see :func:`torch.serialization.clear_safe_globals`.

Troubleshooting ``weights_only``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Getting unsafe globals
""""""""""""""""""""""

A caveat is that :func:`torch.serialization.get_unsafe_globals_in_checkpoint` analyzes the checkpoint statically,
some types might be built dynamically during the unpickling process and hence will not be reported by
:func:`torch.serialization.get_unsafe_globals_in_checkpoint`. One such example is ``dtypes`` in numpy. In
``numpy < 1.25`` after allowlisting all the functions/classes reported by
:func:`torch.serialization.get_unsafe_globals_in_checkpoint` you might see an error like

.. code::

    WeightsUnpickler error: Can only build Tensor, Parameter, OrderedDict or types allowlisted via `add_safe_globals`,
    but got <class 'numpy.dtype[float32]'>

This can be allowlisted via ``{add_}safe_globals([type(np.dtype(np.float32))])``.

In ``numpy >=1.25`` you would see

.. code::

    WeightsUnpickler error: Can only build Tensor, Parameter, OrderedDict or types allowlisted via `add_safe_globals`,
    but got <class 'numpy.dtypes.Float32DType'>

This can be allowlisted via ``{add_}safe_globals([np.dtypes.Float32DType])``.

Environment Variables
"""""""""""""""""""""

There are two environment variables that will influence the behavior of ``torch.load``. These can be helpful
if one does not have access to the ``torch.load`` callsites.

* ``TORCH_FORCE_WEIGHTS_ONLY_LOAD=1`` will override all ``torch.load`` callsites to use ``weights_only=True``.
* ``TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1`` will make ``torch.load`` callsites use ``weights_only=False`` **only**
  if ``weights_only`` was not passed as an argument.


.. _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().__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.

.. _utility functions:

Utility functions
-----------------

The following utility functions are related to serialization:

.. currentmodule:: torch.serialization

.. autofunction:: register_package
.. autofunction:: get_crc32_options
.. autofunction:: set_crc32_options
.. autofunction:: get_default_load_endianness
.. autofunction:: set_default_load_endianness
.. autofunction:: get_default_mmap_options
.. autofunction:: set_default_mmap_options
.. autofunction:: add_safe_globals
.. autofunction:: clear_safe_globals
.. autofunction:: get_safe_globals
.. autofunction:: get_unsafe_globals_in_checkpoint
.. autoclass:: safe_globals
.. autoclass:: skip_data

.. _serialization config:

Config
------
.. py:module:: torch.utils.serialization
.. py:module:: torch.utils.serialization.config

``torch.utils.serialization.config`` provides a global config that can control the behavior of
``torch.save`` and ``torch.load``.


``torch.utils.serialization.config.save`` contains options that control the behavior of ``torch.save``.

  * ``compute_crc32``: whether to compute and write the zip file checksum (Default : ``True``).
    See :func:`~torch.serialization.set_crc32_options`.
  * ``use_pinned_memory_for_d2h``: for storages that are on an accelerator when passed to ``torch.save``, whether to
    move storage to pinned memory or pageable memory on CPU within ``torch.save``. (Default: ``False`` (i.e. pageable))

``torch.utils.serialization.config.load`` contains options that control the behavior of ``torch.load``.

  * ``mmap``: See the documentation for ``mmap`` argument in :func:`torch.load`.
    This config will set the behavior of ``mmap`` for ``torch.load`` if it is not
    already explicitly passed to the ``torch.load`` call (Default : ``False``).
  * ``endianness``: See :func:`~torch.serialization.set_default_load_endianness`.
    (Default : ``torch.serialization.LoadEndianness.NATIVE``)
  * ``mmap_flags``: See :class:`~torch.serialization.set_default_mmap_options`.
    (Default : ``MAP_PRIVATE``)
