# TorchScript Language Reference¶

TorchScript is a statically typed subset of Python that can either be written directly (using the @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¶

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

E

NamedTuple[T0, T1, ...]

A 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)

import torch

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

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...


### Unsupported Typing Constructs¶

TorchScript does not support all features and types of the 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 typing module are unavailble in TorchScript.

Item

Description

typing.Any

typing.Any is currently in development but not yet released

typing.NoReturn

Not implemented

typing.Union

Unlikely to be implemented (however typing.Optional is supported)

typing.Sequence

Not implemented

typing.Callable

Not implemented

typing.Literal

Not implemented

typing.ClassVar

Not implemented

typing.Final

This is supported for module attributes class attribute annotations but not for functions

typing.AnyStr

TorchScript does not support bytes so this type is not used

typing.overload

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 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.

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))))


Note

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

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))))


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):

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())


### 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):

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 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 @torch.jit.script, similar to how you would declare a TorchScript function:

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

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))


### TorchScript Enums¶

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.

### Named Tuples¶

Types produced by collections.namedtuple can be used in TorchScript.

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))


### Iterables¶

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

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 @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)).

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


### 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.

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:

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

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.

## 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.

torch.jit.is_scripting()[source]

Function that returns True when in compilation and False otherwise. This is useful especially with the @unused decorator to leave code in your model that is not yet TorchScript compatible. .. testcode:

import torch

@torch.jit.unused
def unsupported_linear_op(x):
return x

def linear(x):
if not torch.jit.is_scripting():
else:
return unsupported_linear_op(x)


### 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.

### 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:

import math
import torch

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

1. 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

### 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 class annotations. If a type cannot be inferred and is not explicilty annotated, it will not be added as an attribute to the resulting ScriptModule.

Example:

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

# ints 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}))