Move TorchScript language reference to its own page (#31138)
Summary:
Preview: https://driazati.github.io/pytorch_doc_previews/jit.html#torchscript-language
](https://our.intern.facebook.com/intern/diff/18941024/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31138
Pulled By: driazati
Differential Revision: D18941024
fbshipit-source-id: d0ff600870a14c4a7c6ce54867d152072a12c48c
diff --git a/docs/source/jit.rst b/docs/source/jit.rst
index ba77c1a..349ace4 100644
--- a/docs/source/jit.rst
+++ b/docs/source/jit.rst
@@ -8,7 +8,16 @@
torch.jit.supported_ops <jit_builtin_functions>
+
+.. toctree::
+ :maxdepth: 1
+ :caption: Language Reference
+ :hidden:
+
+ language_reference <jit_language_reference>
+
.. contents:: :local:
+ :depth: 2
.. automodule:: torch.jit
.. currentmodule:: torch.jit
@@ -147,936 +156,49 @@
my_script_module = torch.jit.script(MyScriptModule())
-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.
+TorchScript Language
+--------------------
-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.
+Please see the full :ref:`language-reference` for details.
-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:
+.. _Builtin functions:
-Types
-~~~~~
+Built-in Functions and Modules
+------------------------------
-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:
+See :ref:`builtin-functions` for a full reference of supported functions.
-.. csv-table::
- :header: "Type", "Description"
+PyTorch Functions and Modules
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- "``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"
+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 all
+modules from ``torch.nn`` are supported in TorchScript, excluding those in the
+table below. For unsupported modules, we suggest using :meth:`torch.jit.trace`.
-Unlike Python, each variable in TorchScript function must have a single static type.
-This makes it easier to optimize TorchScript functions.
+Unsupported ``torch.nn`` Modules::
-Example (a type mismatch)
+ torch.nn.modules.adaptive.AdaptiveLogSoftmaxWithLoss
+ torch.nn.modules.normalization.CrossMapLRN2d
+ torch.nn.modules.rnn.RNN
-.. 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
-^^^^^^^^^^^^^^^^^^^
-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:: ignore
-
-.. autofunction:: unused
-
-.. 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}))
+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, but no other Python modules
+(built-in or third party) are supported.
- |
Debugging
-~~~~~~~~~
+---------
.. _`disable TorchScript`:
Disable JIT for Debugging
-^^^^^^^^^^^^^^^^^^^^^^^^^
+~~~~~~~~~~~~~~~~~~~~~~~~~
.. envvar:: PYTORCH_JIT
Setting the environment variable ``PYTORCH_JIT=0`` will disable all script
@@ -1115,7 +237,7 @@
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
@@ -1165,7 +287,7 @@
Interpreting Graphs
-^^^^^^^^^^^^^^^^^^^
+~~~~~~~~~~~~~~~~~~~
TorchScript also has a representation at a lower level than the code pretty-
printer, in the form of IR graphs.
@@ -1241,6 +363,9 @@
by a :class:`ScriptModule` is correct, in both automated and manual fashion, as
described below.
+Tracer
+~~~~~~
+
Tracing Edge Cases
^^^^^^^^^^^^^^^^^^
@@ -1406,34 +531,6 @@
...
-.. _Builtin functions:
-
-Built-in Functions and Modules
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-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 all
-modules from ``torch.nn`` are supported in TorchScript, excluding those in the
-table below. For unsupported modules, we suggest using :meth:`torch.jit.trace`.
-
-Unsupported ``torch.nn`` Modules::
-
- torch.nn.modules.adaptive.AdaptiveLogSoftmaxWithLoss
- torch.nn.modules.normalization.CrossMapLRN2d
- torch.nn.modules.rnn.RNN
-
-Python Functions and Modules
-----------------------------
-Many Python's `built-in functions <https://docs.python.org/3/library/functions.html>`_ are supported in TorchScript.
-The :any:`math` module is also supported, but no other Python modules
-(built-in or third party) are supported.
-
Frequently Asked Questions
--------------------------
@@ -1522,7 +619,7 @@
--------
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.
@@ -1574,7 +671,7 @@
* The ``torch.jit.annotate`` function
Modules
-~~~~~~~
+^^^^^^^
.. warning::
The :func:`@torch.jit.ignore <torch.jit.ignore>` annotation's behavior changes in
@@ -1593,7 +690,7 @@
.. 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::
@@ -1623,7 +720,8 @@
return 2
TorchScript Classes
-~~~~~~~~~~~~~~~~~~~
+^^^^^^^^^^^^^^^^^^^
+
.. warning::
TorchScript class support is experimental. Currently it is best suited
@@ -1634,7 +732,7 @@
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.
@@ -1680,7 +778,7 @@
m = torch.jit.script(MyModule())
Python 2
-^^^^^^^^
+""""""""
If you are stuck on Python 2 and cannot use the class annotation syntax, you can use the ``__annotations__`` class member to directly apply type annotations.
.. testcode::
@@ -1696,7 +794,7 @@
self.my_int = 20
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:
@@ -1741,7 +839,7 @@
.. _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
@@ -1760,5 +858,3 @@
if flag:
b = 2
return x, b
-
-
diff --git a/docs/source/jit_language_reference.rst b/docs/source/jit_language_reference.rst
new file mode 100644
index 0000000..c8f6c19
--- /dev/null
+++ b/docs/source/jit_language_reference.rst
@@ -0,0 +1,927 @@
+.. contents:: :local:
+ :depth: 2
+
+.. _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
+^^^^^^^^^^^^^^^^^^^
+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:: ignore
+
+.. autofunction:: unused
+
+.. 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}))
+
+ |
