TorchScript
===========

.. toctree::
   :maxdepth: 1
   :caption: Builtin Functions
   :hidden:

   torch.jit.supported_ops <jit_builtin_functions>


.. toctree::
    :maxdepth: 1
    :caption: Language Reference
    :hidden:

    jit_language_reference

.. contents:: :local:
    :depth: 2

.. automodule:: torch.jit
.. currentmodule:: torch.jit

TorchScript is a way to create serializable and optimizable models from PyTorch code.
Any TorchScript program can be saved from a Python
process and loaded in a process where there is no Python dependency.

We provide tools to incrementally transition a model from a pure Python program
to a TorchScript program that can be run independently from Python, such as in a standalone C++ program.
This makes it possible to train models in PyTorch using familiar tools in Python and then export
the model via TorchScript to a production environment where Python programs may be disadvantageous
for performance and multi-threading reasons.

For a gentle introduction to TorchScript, see the `Introduction to TorchScript <https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html>`_ tutorial.

For an end-to-end example of converting a PyTorch model to TorchScript and running it in C++, see the
`Loading a PyTorch Model in C++ <https://pytorch.org/tutorials/advanced/cpp_export.html>`_ tutorial.

Creating TorchScript Code
--------------------------

.. autosummary::
    :toctree: generated

    script
    trace
    script_if_tracing
    trace_module
    fork
    wait
    ScriptModule
    ScriptFunction
    freeze
    save
    load
    ignore
    unused
    isinstance

Mixing Tracing and Scripting
----------------------------

In many cases either tracing or scripting is an easier approach for converting a model to TorchScript.
Tracing and scripting can be composed to suit the particular requirements
of a part of a model.

Scripted functions can call traced functions. This is particularly useful when you need
to use control-flow around a simple feed-forward model. For instance the beam search
of a sequence to sequence model will typically be written in script but can call an
encoder module generated using tracing.


.. testsetup::

    # These are hidden from the docs, but these are necessary for `doctest`
    # since the `inspect` module doesn't play nicely with the execution
    # environment for `doctest`
    import torch

    original_script = torch.jit.script
    def script_wrapper(obj, *args, **kwargs):
        obj.__module__ = 'FakeMod'
        return original_script(obj, *args, **kwargs)

    torch.jit.script = script_wrapper

    original_trace = torch.jit.trace
    def trace_wrapper(obj, *args, **kwargs):
        obj.__module__ = 'FakeMod'
        return original_trace(obj, *args, **kwargs)

    torch.jit.trace = trace_wrapper


Example (calling a traced function in script):

.. testcode::

    import torch

    def foo(x, y):
        return 2 * x + y

    traced_foo = torch.jit.trace(foo, (torch.rand(3), torch.rand(3)))

    @torch.jit.script
    def bar(x):
        return traced_foo(x, x)

Traced functions can call script functions. This is useful when a small part of
a model requires some control-flow even though most of the model is just a feed-forward
network. Control-flow inside of a script function called by a traced function is
preserved correctly.

Example (calling a script function in a traced function):

.. testcode::

    import torch

    @torch.jit.script
    def foo(x, y):
        if x.max() > y.max():
            r = x
        else:
            r = y
        return r


    def bar(x, y, z):
        return foo(x, y) + z

    traced_bar = torch.jit.trace(bar, (torch.rand(3), torch.rand(3), torch.rand(3)))

This composition also works for ``nn.Module``\s as well, where it can be used to generate
a submodule using tracing that can be called from the methods of a script module.

Example (using a traced module):

.. testcode::
    :skipif: torchvision is None

    import torch
    import torchvision

    class MyScriptModule(torch.nn.Module):
        def __init__(self):
            super(MyScriptModule, self).__init__()
            self.means = torch.nn.Parameter(torch.tensor([103.939, 116.779, 123.68])
                                            .resize_(1, 3, 1, 1))
            self.resnet = torch.jit.trace(torchvision.models.resnet18(),
                                          torch.rand(1, 3, 224, 224))

        def forward(self, input):
            return self.resnet(input - self.means)

    my_script_module = torch.jit.script(MyScriptModule())


TorchScript Language
--------------------

TorchScript is a statically typed subset of Python, so many Python features apply
directly to TorchScript. See the full :ref:`language-reference` for details.


.. _builtin functions:

Built-in Functions and Modules
------------------------------

TorchScript supports the use of most PyTorch functions and many Python built-ins.
See :ref:`builtin-functions` for a full reference of supported functions.

PyTorch Functions and Modules
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

TorchScript supports a subset of the tensor and neural network
functions that PyTorch provides. Most methods on Tensor as well as functions in
the ``torch`` namespace, all functions in ``torch.nn.functional`` and
most modules from ``torch.nn`` are supported in TorchScript.

See :ref:`jit_unsupported` for a list of unsupported PyTorch functions and modules.


Python Functions and Modules
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Many of Python's `built-in functions <https://docs.python.org/3/library/functions.html>`_ are supported in TorchScript.
The :any:`math` module is also supported (see :ref:`math-module` for details), but no other Python modules
(built-in or third party) are supported.


Python Language Reference Comparison
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

For a full listing of supported Python features, see :ref:`python-language-reference`.

Debugging
---------

.. _`disable TorchScript`:

Disable JIT for Debugging
~~~~~~~~~~~~~~~~~~~~~~~~~
.. envvar:: PYTORCH_JIT

Setting the environment variable ``PYTORCH_JIT=0`` will disable all script
and tracing annotations. If there is hard-to-debug error in one of your
TorchScript models, you can use this flag to force everything to run using native
Python. Since TorchScript (scripting and tracing) is disabled with this flag,
you can use tools like ``pdb`` to debug the model code.  For example::

    @torch.jit.script
    def scripted_fn(x : torch.Tensor):
        for i in range(12):
            x = x + x
        return x

    def fn(x):
        x = torch.neg(x)
        import pdb; pdb.set_trace()
        return scripted_fn(x)

    traced_fn = torch.jit.trace(fn, (torch.rand(4, 5),))
    traced_fn(torch.rand(3, 4))

Debugging this script with ``pdb`` works except for when we invoke the
:func:`@torch.jit.script <torch.jit.script>` function. We can globally disable
JIT, so that we can call the :func:`@torch.jit.script <torch.jit.script>`
function as a normal Python function and not compile it. If the above script
is called ``disable_jit_example.py``, we can invoke it like so::

    $ PYTORCH_JIT=0 python disable_jit_example.py

and we will be able to step into the :func:`@torch.jit.script
<torch.jit.script>` function as a normal Python function. To disable the
TorchScript compiler for a specific function, see
:func:`@torch.jit.ignore <torch.jit.ignore>`.

.. _inspecting-code:

Inspecting Code
~~~~~~~~~~~~~~~

TorchScript provides a code pretty-printer for all :class:`ScriptModule` instances. This
pretty-printer gives an interpretation of the script method's code as valid
Python syntax. For example:

.. testcode::

    @torch.jit.script
    def foo(len):
        # type: (int) -> torch.Tensor
        rv = torch.zeros(3, 4)
        for i in range(len):
            if i < 10:
                rv = rv - 1.0
            else:
                rv = rv + 1.0
        return rv

    print(foo.code)

.. testoutput::
    :hide:

    ...

A :class:`ScriptModule` with a single ``forward`` method will have an attribute
``code``, which you can use to inspect the :class:`ScriptModule`'s code.
If the :class:`ScriptModule` has more than one method, you will need to access
``.code`` on the method itself and not the module. We can inspect the
code of a method named ``foo`` on a :class:`ScriptModule` by accessing ``.foo.code``.
The example above produces this output: ::

    def foo(len: int) -> Tensor:
        rv = torch.zeros([3, 4], dtype=None, layout=None, device=None, pin_memory=None)
        rv0 = rv
        for i in range(len):
            if torch.lt(i, 10):
                rv1 = torch.sub(rv0, 1., 1)
            else:
                rv1 = torch.add(rv0, 1., 1)
            rv0 = rv1
        return rv0

This is TorchScript's compilation of the code for the ``forward`` method.
You can use this to ensure TorchScript (tracing or scripting) has captured
your model code correctly.


.. _interpreting-graphs:

Interpreting Graphs
~~~~~~~~~~~~~~~~~~~
TorchScript also has a representation at a lower level than the code pretty-
printer, in the form of IR graphs.

TorchScript uses a static single assignment (SSA) intermediate representation
(IR) to represent computation. The instructions in this format consist of
ATen (the C++ backend of PyTorch) operators and other primitive operators,
including control flow operators for loops and conditionals. As an example:

.. testcode::

    @torch.jit.script
    def foo(len):
        # type: (int) -> torch.Tensor
        rv = torch.zeros(3, 4)
        for i in range(len):
            if i < 10:
                rv = rv - 1.0
            else:
                rv = rv + 1.0
        return rv

    print(foo.graph)

.. testoutput::
    :hide:

    ...

``graph`` follows the same rules described in the :ref:`inspecting-code` section
with regard to ``forward`` method lookup.

The example script above produces the graph::

    graph(%len.1 : int):
      %24 : int = prim::Constant[value=1]()
      %17 : bool = prim::Constant[value=1]() # test.py:10:5
      %12 : bool? = prim::Constant()
      %10 : Device? = prim::Constant()
      %6 : int? = prim::Constant()
      %1 : int = prim::Constant[value=3]() # test.py:9:22
      %2 : int = prim::Constant[value=4]() # test.py:9:25
      %20 : int = prim::Constant[value=10]() # test.py:11:16
      %23 : float = prim::Constant[value=1]() # test.py:12:23
      %4 : int[] = prim::ListConstruct(%1, %2)
      %rv.1 : Tensor = aten::zeros(%4, %6, %6, %10, %12) # test.py:9:10
      %rv : Tensor = prim::Loop(%len.1, %17, %rv.1) # test.py:10:5
        block0(%i.1 : int, %rv.14 : Tensor):
          %21 : bool = aten::lt(%i.1, %20) # test.py:11:12
          %rv.13 : Tensor = prim::If(%21) # test.py:11:9
            block0():
              %rv.3 : Tensor = aten::sub(%rv.14, %23, %24) # test.py:12:18
              -> (%rv.3)
            block1():
              %rv.6 : Tensor = aten::add(%rv.14, %23, %24) # test.py:14:18
              -> (%rv.6)
          -> (%17, %rv.13)
      return (%rv)


Take the instruction ``%rv.1 : Tensor = aten::zeros(%4, %6, %6, %10, %12) # test.py:9:10`` for
example.

* ``%rv.1 : Tensor`` means we assign the output to a (unique) value named ``rv.1``, that value is of ``Tensor`` type and that we do not know its concrete shape.
* ``aten::zeros`` is the operator (equivalent to ``torch.zeros``) and the input list ``(%4, %6, %6, %10, %12)`` specifies which values in scope should be passed as inputs. The schema for built-in functions like ``aten::zeros`` can be found at `Builtin Functions`_.
* ``# test.py:9:10`` is the location in the original source file that generated this instruction. In this case, it is a file named `test.py`, on line 9, and at character 10.

Notice that operators can also have associated ``blocks``, namely the
``prim::Loop`` and ``prim::If`` operators. In the graph print-out, these
operators are formatted to reflect their equivalent source code forms
to facilitate easy debugging.

Graphs can be inspected as shown to confirm that the computation described
by a :class:`ScriptModule` is correct, in both automated and manual fashion, as
described below.

Tracer
~~~~~~


Tracing Edge Cases
^^^^^^^^^^^^^^^^^^
There are some edge cases that exist where the trace of a given Python
function/module will not be representative of the underlying code. These
cases can include:

* Tracing of control flow that is dependent on inputs (e.g. tensor shapes)
* Tracing of in-place operations of tensor views (e.g. indexing on the left-hand side of an assignment)

Note that these cases may in fact be traceable in the future.


Automatic Trace Checking
^^^^^^^^^^^^^^^^^^^^^^^^
One way to automatically catch many errors in traces is by using ``check_inputs``
on the ``torch.jit.trace()`` API. ``check_inputs`` takes a list of tuples
of inputs that will be used to re-trace the computation and verify the
results. For example::

    def loop_in_traced_fn(x):
        result = x[0]
        for i in range(x.size(0)):
            result = result * x[i]
        return result

    inputs = (torch.rand(3, 4, 5),)
    check_inputs = [(torch.rand(4, 5, 6),), (torch.rand(2, 3, 4),)]

    traced = torch.jit.trace(loop_in_traced_fn, inputs, check_inputs=check_inputs)

Gives us the following diagnostic information::

    ERROR: Graphs differed across invocations!
    Graph diff:

                graph(%x : Tensor) {
                %1 : int = prim::Constant[value=0]()
                %2 : int = prim::Constant[value=0]()
                %result.1 : Tensor = aten::select(%x, %1, %2)
                %4 : int = prim::Constant[value=0]()
                %5 : int = prim::Constant[value=0]()
                %6 : Tensor = aten::select(%x, %4, %5)
                %result.2 : Tensor = aten::mul(%result.1, %6)
                %8 : int = prim::Constant[value=0]()
                %9 : int = prim::Constant[value=1]()
                %10 : Tensor = aten::select(%x, %8, %9)
            -   %result : Tensor = aten::mul(%result.2, %10)
            +   %result.3 : Tensor = aten::mul(%result.2, %10)
            ?          ++
                %12 : int = prim::Constant[value=0]()
                %13 : int = prim::Constant[value=2]()
                %14 : Tensor = aten::select(%x, %12, %13)
            +   %result : Tensor = aten::mul(%result.3, %14)
            +   %16 : int = prim::Constant[value=0]()
            +   %17 : int = prim::Constant[value=3]()
            +   %18 : Tensor = aten::select(%x, %16, %17)
            -   %15 : Tensor = aten::mul(%result, %14)
            ?     ^                                 ^
            +   %19 : Tensor = aten::mul(%result, %18)
            ?     ^                                 ^
            -   return (%15);
            ?             ^
            +   return (%19);
            ?             ^
                }


This message indicates to us that the computation differed between when
we first traced it and when we traced it with the ``check_inputs``. Indeed,
the loop within the body of ``loop_in_traced_fn`` depends on the shape
of the input ``x``, and thus when we try another ``x`` with a different
shape, the trace differs.

In this case, data-dependent control flow like this can be captured using
:func:`torch.jit.script` instead:

.. testcode::

    def fn(x):
        result = x[0]
        for i in range(x.size(0)):
            result = result * x[i]
        return result

    inputs = (torch.rand(3, 4, 5),)
    check_inputs = [(torch.rand(4, 5, 6),), (torch.rand(2, 3, 4),)]

    scripted_fn = torch.jit.script(fn)
    print(scripted_fn.graph)
    #print(str(scripted_fn.graph).strip())

    for input_tuple in [inputs] + check_inputs:
        torch.testing.assert_allclose(fn(*input_tuple), scripted_fn(*input_tuple))

.. testoutput::
    :hide:

    ...


Which produces::

    graph(%x : Tensor) {
        %5 : bool = prim::Constant[value=1]()
        %1 : int = prim::Constant[value=0]()
        %result.1 : Tensor = aten::select(%x, %1, %1)
        %4 : int = aten::size(%x, %1)
        %result : Tensor = prim::Loop(%4, %5, %result.1)
        block0(%i : int, %7 : Tensor) {
            %10 : Tensor = aten::select(%x, %1, %i)
            %result.2 : Tensor = aten::mul(%7, %10)
            -> (%5, %result.2)
        }
        return (%result);
    }

Tracer Warnings
^^^^^^^^^^^^^^^
The tracer produces warnings for several problematic patterns in traced
computation. As an example, take a trace of a function that contains an
in-place assignment on a slice (a view) of a Tensor:

.. testcode::

    def fill_row_zero(x):
        x[0] = torch.rand(*x.shape[1:2])
        return x

    traced = torch.jit.trace(fill_row_zero, (torch.rand(3, 4),))
    print(traced.graph)

.. testoutput::
    :hide:

    ...

Produces several warnings and a graph which simply returns the input::

    fill_row_zero.py:4: TracerWarning: There are 2 live references to the data region being modified when tracing in-place operator copy_ (possibly due to an assignment). This might cause the trace to be incorrect, because all other views that also reference this data will not reflect this change in the trace! On the other hand, if all other views use the same memory chunk, but are disjoint (e.g. are outputs of torch.split), this might still be safe.
        x[0] = torch.rand(*x.shape[1:2])
    fill_row_zero.py:6: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
    Not within tolerance rtol=1e-05 atol=1e-05 at input[0, 1] (0.09115803241729736 vs. 0.6782537698745728) and 3 other locations (33.00%)
        traced = torch.jit.trace(fill_row_zero, (torch.rand(3, 4),))
    graph(%0 : Float(3, 4)) {
        return (%0);
    }

We can fix this by modifying the code to not use the in-place update, but
rather build up the result tensor out-of-place with ``torch.cat``:

.. testcode::

    def fill_row_zero(x):
        x = torch.cat((torch.rand(1, *x.shape[1:2]), x[1:2]), dim=0)
        return x

    traced = torch.jit.trace(fill_row_zero, (torch.rand(3, 4),))
    print(traced.graph)

.. testoutput::
    :hide:

    ...

Frequently Asked Questions
--------------------------

Q: I would like to train a model on GPU and do inference on CPU. What are the
best practices?

   First convert your model from GPU to CPU and then save it, like so: ::

      cpu_model = gpu_model.cpu()
      sample_input_cpu = sample_input_gpu.cpu()
      traced_cpu = torch.jit.trace(cpu_model, sample_input_cpu)
      torch.jit.save(traced_cpu, "cpu.pt")

      traced_gpu = torch.jit.trace(gpu_model, sample_input_gpu)
      torch.jit.save(traced_gpu, "gpu.pt")

      # ... later, when using the model:

      if use_gpu:
        model = torch.jit.load("gpu.pt")
      else:
        model = torch.jit.load("cpu.pt")

      model(input)

   This is recommended because the tracer may witness tensor creation on a
   specific device, so casting an already-loaded model may have unexpected
   effects. Casting the model *before* saving it ensures that the tracer has
   the correct device information.


Q: How do I store attributes on a :class:`ScriptModule`?

    Say we have a model like:

    .. testcode::

        import torch

        class Model(torch.nn.Module):
            def __init__(self):
                super(Model, self).__init__()
                self.x = 2

            def forward(self):
                return self.x

        m = torch.jit.script(Model())



    If ``Model`` is instantiated it will result in a compilation error
    since the compiler doesn't know about ``x``. There are 4 ways to inform the
    compiler of attributes on :class:`ScriptModule`:

    1. ``nn.Parameter`` - Values wrapped in ``nn.Parameter`` will work as they
    do on ``nn.Module``\s

    2. ``register_buffer`` - Values wrapped in ``register_buffer`` will work as
    they do on ``nn.Module``\s. This is equivalent to an attribute (see 4) of type
    ``Tensor``.

    3. Constants - Annotating a class member as ``Final`` (or adding it to a list called
    ``__constants__`` at the class definition level) will mark the contained names
    as constants. Constants are saved directly in the code of the model. See
    `builtin-constants` for details.

    4. Attributes - Values that are a `supported type` can be added as mutable
    attributes. Most types can be inferred but some may need to be specified, see
    `module attributes` for details.

Q: I would like to trace module's method but I keep getting this error:

``RuntimeError: Cannot insert a Tensor that requires grad as a constant. Consider making it a parameter or input, or detaching the gradient``

    This error usually means that the method you are tracing uses a module's parameters and
    you are passing the module's method instead of the module instance (e.g. ``my_module_instance.forward`` vs ``my_module_instance``).

      - Invoking ``trace`` with a module's method captures module parameters (which may require gradients) as **constants**.
      - On the other hand, invoking ``trace`` with module's instance (e.g. ``my_module``) creates a new module and correctly copies parameters into the new module, so they can accumulate gradients if required.

    To trace a specific method on a module, see :func:`torch.jit.trace_module <torch.jit.trace_module>`

Appendix
--------

Migrating to PyTorch 1.2 Recursive Scripting API
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This section details the changes to TorchScript in PyTorch 1.2. If you are new to TorchScript you can
skip this section. There are two main changes to the TorchScript API with PyTorch 1.2.

1. :func:`torch.jit.script <torch.jit.script>` will now attempt to recursively compile functions,
methods, and classes that it encounters. Once you call ``torch.jit.script``,
compilation is "opt-out", rather than "opt-in".

2. ``torch.jit.script(nn_module_instance)`` is now the preferred way to create
:class:`ScriptModule`\s, instead of inheriting from ``torch.jit.ScriptModule``.
These changes combine to provide a simpler, easier-to-use API for converting
your ``nn.Module``\s into :class:`ScriptModule`\s, ready to be optimized and executed in a
non-Python environment.

The new usage looks like this:

.. testcode::

    import torch
    import torch.nn as nn
    import torch.nn.functional as F

    class Model(nn.Module):
        def __init__(self):
            super(Model, self).__init__()
            self.conv1 = nn.Conv2d(1, 20, 5)
            self.conv2 = nn.Conv2d(20, 20, 5)

        def forward(self, x):
            x = F.relu(self.conv1(x))
            return F.relu(self.conv2(x))

    my_model = Model()
    my_scripted_model = torch.jit.script(my_model)


* The module's ``forward`` is compiled by default. Methods called from ``forward`` are lazily compiled in the order they are used in ``forward``.
* To compile a method other than ``forward`` that is not called from ``forward``, add ``@torch.jit.export``.
* To stop the compiler from compiling a method, add :func:`@torch.jit.ignore <torch.jit.ignore>` or :func:`@torch.jit.unused <torch.jit.unused>`. ``@ignore`` leaves the
* method as a call to python, and ``@unused`` replaces it with an exception. ``@ignored`` cannot be exported; ``@unused`` can.
* Most attribute types can be inferred, so ``torch.jit.Attribute`` is not necessary. For empty container types, annotate their types using `PEP 526-style <https://www.python.org/dev/peps/pep-0526/#class-and-instance-variable-annotations>`_ class annotations.
* Constants can be marked with a ``Final`` class annotation instead of adding the name of the member to ``__constants__``.
* Python 3 type hints can be used in place of ``torch.jit.annotate``

As a result of these changes, the following items are considered deprecated and should not appear in new code:
  * The ``@torch.jit.script_method`` decorator
  * Classes that inherit from ``torch.jit.ScriptModule``
  * The ``torch.jit.Attribute`` wrapper class
  * The ``__constants__`` array
  * The ``torch.jit.annotate`` function

Modules
^^^^^^^
.. warning::

    The :func:`@torch.jit.ignore <torch.jit.ignore>` annotation's behavior changes in
    PyTorch 1.2. Before PyTorch 1.2 the @ignore decorator was used to make a function
    or method callable from code that is exported. To get this functionality back,
    use ``@torch.jit.unused()``. ``@torch.jit.ignore`` is now equivalent
    to ``@torch.jit.ignore(drop=False)``. See :func:`@torch.jit.ignore <torch.jit.ignore>`
    and :func:`@torch.jit.unused<torch.jit.unused>` for details.

When passed to the :func:`torch.jit.script <torch.jit.script>` function, a ``torch.nn.Module``\'s data is
copied to a :class:`ScriptModule` and the TorchScript compiler compiles the module.
The module's ``forward`` is compiled by default. Methods called from ``forward`` are
lazily compiled in the order they are used in ``forward``, as well as any
``@torch.jit.export`` methods.

.. autofunction:: export

Functions
^^^^^^^^^
Functions don't change much, they can be decorated with :func:`@torch.jit.ignore <torch.jit.ignore>` or :func:`torch.jit.unused <torch.jit.unused>` if needed.

.. testcode::

    # Same behavior as pre-PyTorch 1.2
    @torch.jit.script
    def some_fn():
        return 2

    # Marks a function as ignored, if nothing
    # ever calls it then this has no effect
    @torch.jit.ignore
    def some_fn2():
        return 2

    # As with ignore, if nothing calls it then it has no effect.
    # If it is called in script it is replaced with an exception.
    @torch.jit.unused
    def some_fn3():
      import pdb; pdb.set_trace()
      return 4

    # Doesn't do anything, this function is already
    # the main entry point
    @torch.jit.export
    def some_fn4():
        return 2

TorchScript Classes
^^^^^^^^^^^^^^^^^^^

.. warning::

    TorchScript class support is experimental. Currently it is best suited
    for simple record-like types (think a ``NamedTuple`` with methods
    attached).

Everything in a user defined `TorchScript Class <torchscript-class>`_ is
exported by default, functions can be decorated with :func:`@torch.jit.ignore
<torch.jit.ignore>` if needed.

Attributes
^^^^^^^^^^
The TorchScript compiler needs to know the types of `module attributes`. Most types
can be inferred from the value of the member. Empty lists and dicts cannot have their
types inferred and must have their types annotated with `PEP 526-style <https://www.python.org/dev/peps/pep-0526/#class-and-instance-variable-annotations>`_ class annotations.
If a type cannot be inferred and is not explicitly annotated, it will not be added as an attribute
to the resulting :class:`ScriptModule`


Old API:

.. testcode::

    from typing import Dict
    import torch

    class MyModule(torch.jit.ScriptModule):
        def __init__(self):
            super(MyModule, self).__init__()
            self.my_dict = torch.jit.Attribute({}, Dict[str, int])
            self.my_int = torch.jit.Attribute(20, int)

    m = MyModule()

New API:

.. testcode::

    from typing import Dict

    class MyModule(torch.nn.Module):
        my_dict: Dict[str, int]

        def __init__(self):
            super(MyModule, self).__init__()
            # This type cannot be inferred and must be specified
            self.my_dict = {}

            # The attribute type here is inferred to be `int`
            self.my_int = 20

        def forward(self):
            pass

    m = torch.jit.script(MyModule())


Constants
^^^^^^^^^
The ``Final`` type constructor can be used to mark members as `constant`. If members are not marked constant, they will be copied to the resulting :class:`ScriptModule` as an attribute. Using ``Final`` opens opportunities for optimization if the value is known to be fixed and gives additional type safety.

Old API:

.. testcode::

    class MyModule(torch.jit.ScriptModule):
        __constants__ = ['my_constant']

        def __init__(self):
            super(MyModule, self).__init__()
            self.my_constant = 2

        def forward(self):
            pass
    m = MyModule()

New API:

::

    try:
        from typing_extensions import Final
    except:
        # If you don't have `typing_extensions` installed, you can use a
        # polyfill from `torch.jit`.
        from torch.jit import Final

    class MyModule(torch.nn.Module):

        my_constant: Final[int]

        def __init__(self):
            super(MyModule, self).__init__()
            self.my_constant = 2

        def forward(self):
            pass

    m = torch.jit.script(MyModule())

.. _Python 3 type hints:

Variables
^^^^^^^^^
Containers are assumed to have type ``Tensor`` and be non-optional (see
`Default Types` for more information). Previously, ``torch.jit.annotate`` was used to
tell the TorchScript compiler what the type should be. Python 3 style type hints are
now supported.

.. testcode::

    import torch
    from typing import Dict, Optional

    @torch.jit.script
    def make_dict(flag: bool):
        x: Dict[str, int] = {}
        x['hi'] = 2
        b: Optional[int] = None
        if flag:
            b = 2
        return x, b

References
~~~~~~~~~~
.. toctree::
    :maxdepth: 1

    jit_python_reference
    jit_unsupported
