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


.. 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

.. _language-reference:

TorchScript Language Reference
==============================

TorchScript is a statically typed subset of Python that can either be written directly (using
the :func:`@torch.jit.script <torch.jit.script>` decorator) or generated automatically from Python code via
tracing. When using tracing, code is automatically converted into this subset of
Python by recording only the actual operators on tensors and simply executing and
discarding the other surrounding Python code.

When writing TorchScript directly using ``@torch.jit.script`` decorator, the programmer must
only use the subset of Python supported in TorchScript. This section documents
what is supported in TorchScript as if it were a language reference for a stand
alone language. Any features of Python not mentioned in this reference are not
part of TorchScript. See `Builtin Functions`_ for a complete reference of available
Pytorch tensor methods, modules, and functions.

As a subset of Python, any valid TorchScript function is also a valid Python
function. This makes it possible to `disable TorchScript`_ and debug the
function using standard Python tools like ``pdb``. The reverse is not true: there
are many valid Python programs that are not valid TorchScript programs.
Instead, TorchScript focuses specifically on the features of Python that are
needed to represent neural network models in PyTorch.

.. _types:
.. _supported type:

Types
~~~~~

The largest difference between TorchScript and the full Python language is that
TorchScript only supports a small set of types that are needed to express neural
net models. In particular, TorchScript supports:

.. csv-table::
   :header: "Type", "Description"

   "``Tensor``", "A PyTorch tensor of any dtype, dimension, or backend"
   "``Tuple[T0, T1, ...]``", "A tuple containing subtypes ``T0``, ``T1``, etc. (e.g. ``Tuple[Tensor, Tensor]``)"
   "``bool``", "A boolean value"
   "``int``", "A scalar integer"
   "``float``", "A scalar floating point number"
   "``str``", "A string"
   "``List[T]``", "A list of which all members are type ``T``"
   "``Optional[T]``", "A value which is either None or type ``T``"
   "``Dict[K, V]``", "A dict with key type ``K`` and value type ``V``. Only ``str``, ``int``, and ``float`` are allowed as key types."
   "``T``", "A `TorchScript Class`_"
   "``NamedTuple[T0, T1, ...]``", "A :func:`collections.namedtuple <collections.namedtuple>` tuple type"

Unlike Python, each variable in TorchScript function must have a single static type.
This makes it easier to optimize TorchScript functions.

Example (a type mismatch)

.. testcode::

    import torch

    @torch.jit.script
    def an_error(x):
        if x:
            r = torch.rand(1)
        else:
            r = 4
        return r


.. testoutput::

     Traceback (most recent call last):
       ...
     RuntimeError: ...

     Type mismatch: r is set to type Tensor in the true branch and type int in the false branch:
     @torch.jit.script
     def an_error(x):
         if x:
         ~~~~~...  <--- HERE
             r = torch.rand(1)
         else:
     and was used here:
         else:
             r = 4
         return r
                ~ <--- HERE
     ...


Unsupported Typing Constructs
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TorchScript does not support all features and types of the :mod:`typing` module. Some of these
are more fundamental things that are unlikely to be added in the future while others
may be added if there is enough user demand to make it a priority.

These types and features from the :mod:`typing` module are unavailble in TorchScript.

.. csv-table::
   :header: "Item", "Description"

   ":any:`typing.Any`", ":any:`typing.Any` is currently in development but not yet released"
   ":any:`typing.NoReturn`", "Not implemented"
   ":any:`typing.Union`", "Unlikely to be implemented (however :any:`typing.Optional` is supported)"
   ":any:`typing.Callable`", "Not implemented"
   ":any:`typing.Literal`", "Not implemented"
   ":any:`typing.ClassVar`", "Not implemented"
   ":any:`typing.Final`", "This is supported for :any:`module attributes <Module Attributes>` class attribute annotations but not for functions"
   ":any:`typing.AnyStr`", "TorchScript does not support :any:`bytes` so this type is not used"
   ":any:`typing.overload`", ":any:`typing.overload` is currently in development but not yet released"
   "Type aliases", "Not implemented"
   "Nominal vs structural subtyping", "Nominal typing is in development, but structural typing is not"
   "NewType", "Unlikely to be implemented"
   "Generics", "Unlikely to be implemented"

Any other functionality from the :any:`typing` module not explitily listed in this documentation is unsupported.

Default Types
^^^^^^^^^^^^^

By default, all parameters to a TorchScript function are assumed to be Tensor.
To specify that an argument to a TorchScript function is another type, it is possible to use
MyPy-style type annotations using the types listed above.

.. testcode::

    import torch

    @torch.jit.script
    def foo(x, tup):
        # type: (int, Tuple[Tensor, Tensor]) -> Tensor
        t0, t1 = tup
        return t0 + t1 + x

    print(foo(3, (torch.rand(3), torch.rand(3))))

.. testoutput::
    :hide:

    ...

.. note::
  It is also possible to annotate types with Python 3 type hints from the
  ``typing`` module.

  .. testcode::

    import torch
    from typing import Tuple

    @torch.jit.script
    def foo(x: int, tup: Tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor:
        t0, t1 = tup
        return t0 + t1 + x

    print(foo(3, (torch.rand(3), torch.rand(3))))

  .. testoutput::
    :hide:

    ...

  In our examples, we use comment-based type hints to ensure Python 2
  compatibility as well.


An empty list is assumed to be ``List[Tensor]`` and empty dicts
``Dict[str, Tensor]``. To instantiate an empty list or dict of other types,
use `Python 3 type hints`_. If you are on Python 2, you can use ``torch.jit.annotate``.

Example (type annotations for Python 3):

.. testcode::

    import torch
    import torch.nn as nn
    from typing import Dict, List, Tuple

    class EmptyDataStructures(torch.nn.Module):
        def __init__(self):
            super(EmptyDataStructures, self).__init__()

        def forward(self, x: torch.Tensor) -> Tuple[List[Tuple[int, float]], Dict[str, int]]:
            # This annotates the list to be a `List[Tuple[int, float]]`
            my_list: List[Tuple[int, float]] = []
            for i in range(10):
                my_list.append((i, x.item()))

            my_dict: Dict[str, int] = {}
            return my_list, my_dict

    x = torch.jit.script(EmptyDataStructures())


Example (``torch.jit.annotate`` for Python 2):

.. testcode::

    import torch
    import torch.nn as nn
    from typing import Dict, List, Tuple

    class EmptyDataStructures(torch.nn.Module):
        def __init__(self):
            super(EmptyDataStructures, self).__init__()

        def forward(self, x):
            # type: (Tensor) -> Tuple[List[Tuple[int, float]], Dict[str, int]]

            # This annotates the list to be a `List[Tuple[int, float]]`
            my_list = torch.jit.annotate(List[Tuple[int, float]], [])
            for i in range(10):
                my_list.append((i, float(x.item())))

            my_dict = torch.jit.annotate(Dict[str, int], {})
            return my_list, my_dict

    x = torch.jit.script(EmptyDataStructures())



Optional Type Refinement
^^^^^^^^^^^^^^^^^^^^^^^^

TorchScript will refine the type of a variable of type ``Optional[T]`` when
a comparison to ``None`` is made inside the conditional of an if-statement or checked in an ``assert``.
The compiler can reason about multiple ``None`` checks that are combined with
``and``, ``or``, and ``not``. Refinement will also occur for else blocks of if-statements
that are not explicitly written.

The ``None`` check must be within the if-statement's condition; assigning
a ``None`` check to a variable and using it in the if-statement's condition will
not refine the types of variables in the check.
Only local variables will be refined, an attribute like ``self.x`` will not and must assigned to
a local variable to be refined.


Example (refining types on parameters and locals):

.. testcode::

    import torch
    import torch.nn as nn
    from typing import Optional

    class M(nn.Module):
        z: Optional[int]

        def __init__(self, z):
            super(M, self).__init__()
            # If `z` is None, its type cannot be inferred, so it must
            # be specified (above)
            self.z = z

        def forward(self, x, y, z):
            # type: (Optional[int], Optional[int], Optional[int]) -> int
            if x is None:
                x = 1
                x = x + 1

            # Refinement for an attribute by assigning it to a local
            z = self.z
            if y is not None and z is not None:
                x = y + z

            # Refinement via an `assert`
            assert z is not None
            x += z
            return x

    module = torch.jit.script(M(2))
    module = torch.jit.script(M(None))

.. _TorchScript Class:
.. _TorchScript Classes:
.. _torchscript-classes:

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

.. warning::

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

Python classes can be used in TorchScript if they are annotated with :func:`@torch.jit.script <torch.jit.script>`,
similar to how you would declare a TorchScript function:

.. testcode::
    :skipif: True  # TODO: fix the source file resolving so this can be tested

    @torch.jit.script
    class Foo:
      def __init__(self, x, y):
        self.x = x

      def aug_add_x(self, inc):
        self.x += inc


This subset is restricted:

* All functions must be valid TorchScript functions (including ``__init__()``).
* Classes must be new-style classes, as we use ``__new__()`` to construct them with pybind11.
* TorchScript classes are statically typed. Members can only be declared by assigning to
  self in the ``__init__()`` method.

    For example, assigning to ``self`` outside of the ``__init__()`` method: ::

        @torch.jit.script
        class Foo:
          def assign_x(self):
            self.x = torch.rand(2, 3)

    Will result in: ::

        RuntimeError:
        Tried to set nonexistent attribute: x. Did you forget to initialize it in __init__()?:
        def assign_x(self):
          self.x = torch.rand(2, 3)
          ~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE

* No expressions except method definitions are allowed in the body of the class.
* No support for inheritance or any other polymorphism strategy, except for inheriting
  from ``object`` to specify a new-style class.

After a class is defined, it can be used in both TorchScript and Python interchangeably
like any other TorchScript type:

::

    # Declare a TorchScript class
    @torch.jit.script
    class Pair:
      def __init__(self, first, second):
        self.first = first
        self.second = second

    @torch.jit.script
    def sum_pair(p):
      # type: (Pair) -> Tensor
      return p.first + p.second

    p = Pair(torch.rand(2, 3), torch.rand(2, 3))
    print(sum_pair(p))


Named Tuples
^^^^^^^^^^^^
Types produced by :func:`collections.namedtuple <collections.namedtuple>` can be used in TorchScript.

.. testcode::

    import torch
    import collections

    Point = collections.namedtuple('Point', ['x', 'y'])

    @torch.jit.script
    def total(point):
        # type: (Point) -> Tensor
        return point.x + point.y

    p = Point(x=torch.rand(3), y=torch.rand(3))
    print(total(p))

.. testoutput::
    :hide:

    ...


.. _jit_iterables:

Iterables
^^^^^^^^^

Some functions (for example, :any:`zip` and :any:`enumerate`) can only operate on iterable types.
Iterable types in TorchScript include ``Tensor``\s, lists, tuples, dictionaries, strings,
:any:`torch.nn.ModuleList` and :any:`torch.nn.ModuleDict`.


Expressions
~~~~~~~~~~~

The following Python Expressions are supported.

Literals
^^^^^^^^
::

    True
    False
    None
    'string literals'
    "string literals"
    3  # interpreted as int
    3.4  # interpreted as a float

List Construction
"""""""""""""""""
An empty list is assumed have type ``List[Tensor]``.
The types of other list literals are derived from the type of the members.
See `Default Types`_ for more details.

::

    [3, 4]
    []
    [torch.rand(3), torch.rand(4)]



Tuple Construction
""""""""""""""""""
::

    (3, 4)
    (3,)


Dict Construction
"""""""""""""""""
An empty dict is assumed have type ``Dict[str, Tensor]``.
The types of other dict literals are derived from the type of the members.
See `Default Types`_ for more details.

::

    {'hello': 3}
    {}
    {'a': torch.rand(3), 'b': torch.rand(4)}


Variables
^^^^^^^^^
See `Variable Resolution`_ for how variables are resolved.

::

    my_variable_name

Arithmetic Operators
^^^^^^^^^^^^^^^^^^^^
::

    a + b
    a - b
    a * b
    a / b
    a ^ b
    a @ b

Comparison Operators
^^^^^^^^^^^^^^^^^^^^
::

    a == b
    a != b
    a < b
    a > b
    a <= b
    a >= b

Logical Operators
^^^^^^^^^^^^^^^^^
::

    a and b
    a or b
    not b

Subscripts and Slicing
^^^^^^^^^^^^^^^^^^^^^^
::

    t[0]
    t[-1]
    t[0:2]
    t[1:]
    t[:1]
    t[:]
    t[0, 1]
    t[0, 1:2]
    t[0, :1]
    t[-1, 1:, 0]
    t[1:, -1, 0]
    t[i:j, i]

Function Calls
^^^^^^^^^^^^^^
Calls to `builtin functions`_

::

    torch.rand(3, dtype=torch.int)

Calls to other script functions:

.. testcode::

    import torch

    @torch.jit.script
    def foo(x):
        return x + 1

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

Method Calls
^^^^^^^^^^^^
Calls to methods of builtin types like tensor: ``x.mm(y)``

On modules, methods must be compiled before they can be called. The TorchScript
compiler recursively compiles methods it sees when compiling other methods. By default,
compilation starts on the ``forward`` method. Any methods called by ``forward`` will
be compiled, and any methods called by those methods, and so on. To start compilation at
a method other than ``forward``, use the :func:`@torch.jit.export <torch.jit.export>` decorator
(``forward`` implicitly is marked ``@torch.jit.export``).

Calling a submodule directly (e.g. ``self.resnet(input)``) is equivalent to
calling its ``forward`` method (e.g. ``self.resnet.forward(input)``).

.. testcode::
    :skipif: torchvision is None

    import torch
    import torch.nn as nn
    import torchvision

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

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

        def forward(self, input):
            return self.helper(input)

        # Since nothing in the model calls `top_level_method`, the compiler
        # must be explicitly told to compile this method
        @torch.jit.export
        def top_level_method(self, input):
            return self.other_helper(input)

        def other_helper(self, input):
            return input + 10

    # `my_script_module` will have the compiled methods `forward`, `helper`,
    # `top_level_method`, and `other_helper`
    my_script_module = torch.jit.script(MyModule())


Ternary Expressions
^^^^^^^^^^^^^^^^^^^
::

    x if x > y else y

Casts
^^^^^
::

    float(ten)
    int(3.5)
    bool(ten)
    str(2)``

Accessing Module Parameters
^^^^^^^^^^^^^^^^^^^^^^^^^^^
::

    self.my_parameter
    self.my_submodule.my_parameter


Statements
~~~~~~~~~~

TorchScript supports the following types of statements:

Simple Assignments
^^^^^^^^^^^^^^^^^^
::

    a = b
    a += b # short-hand for a = a + b, does not operate in-place on a
    a -= b

Pattern Matching Assignments
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
::

    a, b = tuple_or_list
    a, b, *c = a_tuple

Multiple Assignments
::

    a = b, c = tup

Print Statements
^^^^^^^^^^^^^^^^
::

    print("the result of an add:", a + b)

If Statements
^^^^^^^^^^^^^
::

    if a < 4:
        r = -a
    elif a < 3:
        r = a + a
    else:
        r = 3 * a

In addition to bools, floats, ints, and Tensors can be used in a conditional
and will be implicitly casted to a boolean.

While Loops
^^^^^^^^^^^
::

    a = 0
    while a < 4:
        print(a)
        a += 1


For loops with range
^^^^^^^^^^^^^^^^^^^^
::

    x = 0
    for i in range(10):
        x *= i

For loops over tuples
^^^^^^^^^^^^^^^^^^^^^
These unroll the loop, generating a body for
each member of the tuple. The body must type-check correctly for each member.

::

    tup = (3, torch.rand(4))
    for x in tup:
        print(x)


For loops over constant nn.ModuleList
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

To use a ``nn.ModuleList`` inside a compiled method, it must be marked
constant by adding the name of the attribute to the ``__constants__``
list for the type. For loops over a ``nn.ModuleList`` will unroll the body of the
loop at compile time, with each member of the constant module list.

.. testcode::

    class SubModule(torch.nn.Module):
        def __init__(self):
            super(SubModule, self).__init__()
            self.weight = nn.Parameter(torch.randn(2))

        def forward(self, input):
            return self.weight + input

    class MyModule(torch.nn.Module):
        __constants__ = ['mods']

        def __init__(self):
            super(MyModule, self).__init__()
            self.mods = torch.nn.ModuleList([SubModule() for i in range(10)])

        def forward(self, v):
            for module in self.mods:
                v = module(v)
            return v


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



Break and Continue
^^^^^^^^^^^^^^^^^^
::

    for i in range(5):
        if i == 1:
        continue
        if i == 3:
        break
        print(i)

Return
^^^^^^
::

    return a, b

Variable Resolution
~~~~~~~~~~~~~~~~~~~

TorchScript supports a subset of Python's variable resolution (i.e. scoping)
rules. Local variables behave the same as in Python, except for the restriction
that a variable must have the same type along all paths through a function.
If a variable has a different type on different branches of an if statement, it
is an error to use it after the end of the if statement.

Similarly, a variable is not allowed to be used if it is only *defined* along some
paths through the function.

Example:

.. testcode::

    @torch.jit.script
    def foo(x):
        if x < 0:
            y = 4
        print(y)

.. testoutput::

     Traceback (most recent call last):
       ...
     RuntimeError: ...

     y is not defined in the false branch...
     @torch.jit.script...
     def foo(x):
         if x < 0:
         ~~~~~~~~~...  <--- HERE
             y = 4
         print(y)
     ...

Non-local variables are resolved to Python values at compile time when the
function is defined. These values are then converted into TorchScript values using
the rules described in `Use of Python Values`_.

Use of Python Values
~~~~~~~~~~~~~~~~~~~~

To make writing TorchScript more convenient, we allow script code to refer
to Python values in the surrounding scope. For instance, any time there is a
reference to ``torch``, the TorchScript compiler is actually resolving it to the
``torch`` Python module when the function is declared.  These Python values are
not a first class part of TorchScript. Instead they are de-sugared at compile-time
into the primitive types that TorchScript supports. This depends
on the dynamic type of the Python valued referenced when compilation occurs.
This section describes the rules that are used when accessing Python values in TorchScript.

Functions
^^^^^^^^^

TorchScript can call Python functions. This functionality is very useful when
incrementally converting a model to TorchScript. The model can be moved function-by-function
to TorchScript, leaving calls to Python functions in place. This way you can incrementally
check the correctness of the model as you go.


.. autofunction:: is_scripting


Attribute Lookup On Python Modules
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TorchScript can lookup attributes on modules. `Builtin functions`_ like ``torch.add``
are accessed this way. This allows TorchScript to call functions defined in
other modules.

.. _constant:

Python-defined Constants
^^^^^^^^^^^^^^^^^^^^^^^^
TorchScript also provides a way to use constants that are defined in Python.
These can be used to hard-code hyper-parameters into the function, or to
define universal constants. There are two ways of specifying that a Python
value should be treated as a constant.

1. Values looked up as attributes of a module are assumed to be constant:

.. testcode::

    import math
    import torch

    @torch.jit.script
    def fn():
        return math.pi

2. Attributes of a ScriptModule can be marked constant by annotating them with ``Final[T]``

::

    import torch
    import torch.nn as nn

    class Foo(nn.Module):
        # `Final` from the `typing_extensions` module can also be used
        a : torch.jit.Final[int]

        def __init__(self):
            super(Foo, self).__init__()
            self.a = 1 + 4

        def forward(self, input):
            return self.a + input

    f = torch.jit.script(Foo())

Supported constant Python types are

* ``int``
* ``float``
* ``bool``
* ``torch.device``
* ``torch.layout``
* ``torch.dtype``
* tuples containing supported types
* ``torch.nn.ModuleList`` which can be used in a TorchScript for loop

.. note::
    If you are on Python 2, you can mark an attribute as a constant by adding
    its name to the ``__constants__`` property of the class:

    .. testcode::

        import torch
        import torch.nn as nn

        class Foo(nn.Module):
            __constants__ = ['a']

            def __init__(self):
                super(Foo, self).__init__()
                self.a = 1 + 4

            def forward(self, input):
                return self.a + input

        f = torch.jit.script(Foo())

    |

.. _module attributes:

Module Attributes
^^^^^^^^^^^^^^^^^

The ``torch.nn.Parameter`` wrapper and ``register_buffer`` can be used to assign
tensors to a module. Other values assigned to a module that is compiled
will be added to the compiled module if their types can be inferred. All `types`_
available in TorchScript can be used as module attributes. Tensor attributes are
semantically the same as buffers. The type of empty lists and dictionaries and ``None``
values cannot be inferred and must be specified via
`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 explicilty annotated, it will not be added as an attribute
to the resulting :class:`ScriptModule`.

Example:

.. testcode::

    from typing import List, Dict

    class Foo(nn.Module):
        # `words` is initialized as an empty list, so its type must be specified
        words: List[str]

        # The type could potentially be inferred if `a_dict` (below) was not
        # empty, but this annotation ensures `some_dict` will be made into the
        # proper type
        some_dict: Dict[str, int]

        def __init__(self, a_dict):
            super(Foo, self).__init__()
            self.words = []
            self.some_dict = a_dict

            # `int`s can be inferred
            self.my_int = 10

        def forward(self, input):
            # type: (str) -> int
            self.words.append(input)
            return self.some_dict[input] + self.my_int

    f = torch.jit.script(Foo({'hi': 2}))


.. note::
    If you are on Python 2, you can mark an attribute's type by adding it to
    the ``__annotations__`` class property as a dictionary of attribute name to
    type

    .. testcode::

        from typing import List, Dict

        class Foo(nn.Module):
            __annotations__ = {'words': List[str], 'some_dict': Dict[str, int]}

            def __init__(self, a_dict):
                super(Foo, self).__init__()
                self.words = []
                self.some_dict = a_dict

                # `int`s can be inferred
                self.my_int = 10

            def forward(self, input):
                # type: (str) -> int
                self.words.append(input)
                return self.some_dict[input] + self.my_int

        f = torch.jit.script(Foo({'hi': 2}))

    |
