.. 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
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.
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:
| Type | Description |
|---|---|
Tensor |
A PyTorch tensor of any dtype, dimension, or backend |
Tuple[T0, T1, ..., TN] |
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 |
E |
A TorchScript Enum |
NamedTuple[T0, T1, ...] |
A :func:`collections.namedtuple <collections.namedtuple>` tuple type |
Union[T0, T1, ...] |
One of the subtypes T0, T1, etc. |
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:
~~~~~
r = torch.rand(1)
~~~~~~~~~~~~~~~~~
else:
~~~~~
r = 4
~~~~~ <--- HERE
return r
and was used here:
else:
r = 4
return r
~ <--- HERE...
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 unavailable in TorchScript.
| Item | Description |
|---|---|
| :any:`typing.Any` | :any:`typing.Any` is currently in development but not yet released |
| :any:`typing.NoReturn` | Not implemented |
| :any:`typing.Sequence` | Not implemented |
| :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 explicitly listed in this documentation is unsupported.
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: ...
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.
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().__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())
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().__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))
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
selfoutside 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
objectto 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))
Python enums can be used in TorchScript without any extra annotation or code:
from enum import Enum
class Color(Enum):
RED = 1
GREEN = 2
@torch.jit.script
def enum_fn(x: Color, y: Color) -> bool:
if x == Color.RED:
return True
return x == y
After an enum is defined, it can be used in both TorchScript and Python interchangeably
like any other TorchScript type. The type of the values of an enum must be int,
float, or str. All values must be of the same type; heterogenous types for enum
values are not supported.
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:
...
Some functions (for example, :any:`zip` and :any:`enumerate`) can only operate on iterable types.
Iterable types in TorchScript include Tensors, lists, tuples, dictionaries, strings,
:any:`torch.nn.ModuleList` and :any:`torch.nn.ModuleDict`.
The following Python Expressions are supported.
True False None 'string literals' "string literals" 3 # interpreted as int 3.4 # interpreted as a float
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)]
(3, 4) (3,)
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)}
See Variable Resolution for how variables are resolved.
my_variable_name
a + b a - b a * b a / b a ^ b a @ b
a == b a != b a < b a > b a <= b a >= b
a and b a or b not b
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]
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)
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().__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())
x if x > y else y
float(ten) int(3.5) bool(ten) str(2)``
self.my_parameter self.my_submodule.my_parameter
TorchScript supports the following types of statements:
a = b a += b # short-hand for a = a + b, does not operate in-place on a a -= b
a, b = tuple_or_list a, b, *c = a_tuple
Multiple Assignments
a = b, c = tup
print("the result of an add:", a + b)
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.
a = 0
while a < 4:
print(a)
a += 1
x = 0
for i in range(10):
x *= i
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)
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().__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().__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())
for i in range(5):
if i == 1:
continue
if i == 3:
break
print(i)
return a, b
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:
~~~~~~~~~
y = 4
~~~~~ <--- HERE
print(y)
and was used here:
if x < 0:
y = 4
print(y)
~ <--- HERE...
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.
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.
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:: torch.jit.is_scripting
.. autofunction:: torch.jit.is_tracing
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.
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.
- 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
- 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().__init__()
self.a = 1 + 4
def forward(self, input):
return self.a + input
f = torch.jit.script(Foo())
Supported constant Python types are
intfloatbooltorch.devicetorch.layouttorch.dtype- tuples containing supported types
torch.nn.ModuleListwhich can be used in a TorchScript for loop
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 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`.
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().__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}))