.. toctree:: :maxdepth: 1 :caption: Builtin Functions :hidden: torch.jit.supported_ops <jit_builtin_functions>
.. automodule:: torch.jit
.. currentmodule:: torch.jit
TorchScript is a way to create serializable and optimizable models from PyTorch code. Any TorchScript program can be saved from a Python process and loaded in a process where there is no Python dependency.
We provide tools to incrementally transition a model from a pure Python program to a TorchScript program that can be run independently from Python, such as in a standalone C++ program. This makes it possible to train models in PyTorch using familiar tools in Python and then export the model via TorchScript to a production environment where Python programs may be disadvantageous. for performance and multi-threading reasons.
For a gentle introduction to TorchScript, see the Introduction to TorchScript tutorial.
For an end-to-end example of converting a PyTorch model to TorchScript and running it in C++, see the Loading a PyTorch Model in C++ tutorial.
.. autoclass:: ScriptModule()
:members:
.. autofunction:: script(obj)
.. autofunction:: trace(func, example_inputs, optimize=None, check_trace=True, check_inputs=None, check_tolerance=1e-5)
.. autofunction:: trace_module(mod, inputs, optimize=None, check_trace=True, check_inputs=None, check_tolerance=1e-5)
.. autofunction:: save
.. autofunction:: load
In many cases either tracing or scripting is an easier approach for converting a model to TorchScript. Tracing and scripting can be composed to suit the particular requirements of a part of a model.
Scripted functions can call traced functions. This is particularly useful when you need to use control-flow around a simple feed-forward model. For instance the beam search of a sequence to sequence model will typically be written in script but can call an encoder module generated using tracing.
.. testsetup::
# These are hidden from the docs, but these are necessary for `doctest`
# since the `inspect` module doesn't play nicely with the execution
# environment for `doctest`
import torch
original_script = torch.jit.script
def script_wrapper(obj, *args, **kwargs):
obj.__module__ = 'FakeMod'
return original_script(obj, *args, **kwargs)
torch.jit.script = script_wrapper
original_trace = torch.jit.trace
def trace_wrapper(obj, *args, **kwargs):
obj.__module__ = 'FakeMod'
return original_trace(obj, *args, **kwargs)
torch.jit.trace = trace_wrapper
Example (calling a traced function in script):
.. testcode::
import torch
def foo(x, y):
return 2 * x + y
traced_foo = torch.jit.trace(foo, (torch.rand(3), torch.rand(3)))
@torch.jit.script
def bar(x):
return traced_foo(x, x)
Traced functions can call script functions. This is useful when a small part of a model requires some control-flow even though most of the model is just a feed-forward network. Control-flow inside of a script function called by a traced function is preserved correctly.
Example (calling a script function in a traced function):
.. testcode::
import torch
@torch.jit.script
def foo(x, y):
if x.max() > y.max():
r = x
else:
r = y
return r
def bar(x, y, z):
return foo(x, y) + z
traced_bar = torch.jit.trace(bar, (torch.rand(3), torch.rand(3), torch.rand(3)))
This composition also works for nn.Modules as well, where it can be used to generate
a submodule using tracing that can be called from the methods of a script module.
Example (using a traced module):
.. testcode::
:skipif: torchvision is None
import torch
import torchvision
class MyScriptModule(torch.nn.Module):
def __init__(self):
super(MyScriptModule, self).__init__()
self.means = torch.nn.Parameter(torch.tensor([103.939, 116.779, 123.68])
.resize_(1, 3, 1, 1))
self.resnet = torch.jit.trace(torchvision.models.resnet18(),
torch.rand(1, 3, 224, 224))
def forward(self, input):
return self.resnet(input - self.means)
my_script_module = torch.jit.script(MyScriptModule())
This section details the changes to TorchScript in PyTorch 1.2. If you are new to TorchScript you can skip this section. There are two main changes to the TorchScript API with PyTorch 1.2.
1. :func:`torch.jit.script <torch.jit.script>` will now attempt to recursively compile functions,
methods, and classes that it encounters. Once you call torch.jit.script,
compilation is "opt-out", rather than "opt-in".
2. torch.jit.script(nn_module_instance) is now the preferred way to create
ScriptModules, instead of inheriting from torch.jit.ScriptModule.
These changes combine to provide a simpler, easier-to-use API for converting
your nn.Modules into ScriptModules, ready to be optimized and executed in a
non-Python environment.
The new usage looks like this:
.. testcode::
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv1 = nn.Conv2d(1, 20, 5)
self.conv2 = nn.Conv2d(20, 20, 5)
def forward(self, x):
x = F.relu(self.conv1(x))
return F.relu(self.conv2(x))
my_model = Model()
my_scripted_model = torch.jit.script(my_model)
- The module's
forwardis compiled by default. Methods called fromforwardare lazily compiled in the order they are used inforward. - To compile a method other than
forwardthat is not called fromforward, add@torch.jit.export. - To stop the compiler from compiling a method and leave it as a call to Python, add
@torch.jit.ignore. - Most attribute types can be inferred, so
torch.jit.Attributeis not necessary. For empty container types, annotate their types using PEP 526-style class annotations. - Constants can be marked with a
Finalclass annotation instead of adding the name of the member to__constants__. - Python 3 type hints can be used in place of
torch.jit.annotate
- As a result of these changes, the following items are considered deprecated and should not appear in new code:
- The
@torch.jit.script_methoddecorator - Classes that inherit from
torch.jit.ScriptModule - The
torch.jit.Attributewrapper class - The
__constants__array - The
torch.jit.annotatefunction
- The
Warning
The :func:`@torch.jit.ignore <torch.jit.ignore>` annotation's behavior changes in
PyTorch 1.2. Before PyTorch 1.2 the @ignore decorator was used to make a function
or method callable from code that is exported. To get this functionality back,
use @torch.jit.unused(). @torch.jit.ignore is now equivalent
to @torch.jit.ignore(drop=False). See :func:`@torch.jit.ignore <torch.jit.ignore>`
and :func:`@torch.jit.unused<torch.jit.unused>` for details.
When passed to the :func:`torch.jit.script <torch.jit.script>` function, a torch.nn.Module's data is
copied to a ScriptModule and the TorchScript compiler compiles the module.
The module's forward is compiled by default. Methods called from forward are
lazily compiled in the order they are used in forward, as well as any
@torch.jit.export methods.
.. autofunction:: export
Functions don't change much, they can be decorated with :func:`@torch.jit.ignore <torch.jit.ignore>` if needed.
.. testcode::
# Same behavior as pre-PyTorch 1.2
@torch.jit.script
def some_fn():
return 2
# Marks a function as ignored, if nothing
# ever calls it then this has no effect
@torch.jit.ignore
def some_fn2():
return 2
# Doesn't do anything, this function is already
# the main entry point
@torch.jit.export
def some_fn3():
return 2
Everything in a user defined TorchScript Class is exported by default, functions can be decorated with :func:`@torch.jit.ignore <torch.jit.ignore>` if needed.
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 class annotations.
Old API:
.. testcode::
from typing import Dict
import torch
class MyModule(torch.jit.ScriptModule):
def __init__(self):
super(MyModule, self).__init__()
self.my_dict = torch.jit.Attribute({}, Dict[str, int])
self.my_int = torch.jit.Attribute(20, int)
m = MyModule()
New API:
.. testcode::
from typing import Dict
class MyModule(torch.nn.Module):
my_dict: Dict[str, int]
def __init__(self):
super(MyModule, self).__init__()
# This type cannot be inferred and must be specified
self.my_dict = {}
# The attribute type here is inferred to be `int`
self.my_int = 20
def forward(self):
pass
m = torch.jit.script(MyModule())
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::
from typing import Dict
class MyModule(torch.jit.ScriptModule):
__annotations__ = {'my_dict': Dict[str, int]}
def __init__(self):
super(MyModule, self).__init__()
self.my_dict = {}
self.my_int = 20
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 ScriptModule as an attribute. Using Final opens opportunities for optimization if the value is known to be fixed and gives additional type safety.
Old API:
.. testcode::
class MyModule(torch.jit.ScriptModule):
__constants__ = ['my_constant']
def __init__(self):
super(MyModule, self).__init__()
self.my_constant = 2
def forward(self):
pass
m = MyModule()
New API:
try:
from typing_extensions import Final
except:
# If you don't have `typing_extensions` installed, you can use a
# polyfill from `torch.jit`.
from torch.jit import Final
class MyModule(torch.nn.Module):
my_constant: Final[int]
def __init__(self):
super(MyModule, self).__init__()
self.my_constant = 2
def forward(self):
pass
m = torch.jit.script(MyModule())
Containers are assumed to have type Tensor and be non-optional (see
Default Types for more information). Previously, torch.jit.annotate was used to
tell the TorchScript compiler what the type should be. Python 3 style type hints are
now supported.
.. testcode::
import torch
from typing import Dict, Optional
@torch.jit.script
def make_dict(flag: bool):
x: Dict[str, int] = {}
x['hi'] = 2
b: Optional[int] = None
if flag:
b = 2
return x, b
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, ...] |
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:
r = 4
return r
...
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())
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))
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))
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:
...
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(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())
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(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())
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:
~~~~~~~~~... <--- 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.
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:: ignore
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(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
intfloatbooltorch.devicetorch.layouttorch.dtype- tuples containing supported types
torch.nn.ModuleListwhich 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())
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 containers and None
values cannot be inferred and must be specified via
PEP 526-style class annotations.
Example:
.. testcode::
from typing import List, Dict
class Foo(nn.Module):
# `words` is initialzed 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}))
.. envvar:: PYTORCH_JIT
Setting the environment variable ``PYTORCH_JIT=0`` will disable all script
and tracing annotations. If there is hard-to-debug error in one of your
TorchScript model, you can use this flag to force everything to run using native
Python. Since TorchScript (scripting and tracing) are disabled with this flag,
you can use tools like ``pdb`` to debug the model code.
Given an example script::
@torch.jit.script
def scripted_fn(x : torch.Tensor):
for i in range(12):
x = x + x
return x
def fn(x):
x = torch.neg(x)
import pdb; pdb.set_trace()
return scripted_fn(x)
traced_fn = torch.jit.trace(fn, (torch.rand(4, 5),))
traced_fn(torch.rand(3, 4))
Debugging this script with ``pdb`` works except for when we invoke the :func:`@torch.jit.script <torch.jit.script>`
function. We can globally disable JIT, so that we can call the ``@torch.jit.script``
function as a normal Python function and not compile it. If the above script
is called ``disable_jit_example.py``, we can invoke it like so::
$ PYTORCH_JIT=0 python disable_jit_example.py
and we will be able to step into the ``@torch.jit.script`` function as a normal Python
function. To disable the TorchScript compiler for a specific function, see
:func:`@torch.jit.ignore <torch.jit.ignore>`.
TorchScript provides a code pretty-printer for all ScriptModule instances. This
pretty-printer gives an interpretation of the script method's code as valid
Python syntax. For example:
.. testcode::
@torch.jit.script
def foo(len):
# type: (int) -> torch.Tensor
rv = torch.zeros(3, 4)
for i in range(len):
if i < 10:
rv = rv - 1.0
else:
rv = rv + 1.0
return rv
print(foo.code)
.. testoutput::
:hide:
...
A ScriptModule with a single forward method will have an attribute
code, which you can use to inspect the ScriptModule's code.
If the ScriptModule has more than one method, you will need to access
.code on the method itself and not the module. We can inspect the
code of a method named bar on a ScriptModule by accessing .bar.code.
The example above produces this output:
def foo(len: int) -> Tensor:
rv = torch.zeros([3, 4], dtype=None, layout=None, device=None, pin_memory=None)
rv0 = rv
for i in range(len):
if torch.lt(i, 10):
rv1 = torch.sub(rv0, 1., 1)
else:
rv1 = torch.add(rv0, 1., 1)
rv0 = rv1
return rv0
This is TorchScript's compilation of the code for the forward method.
You can use this to ensure TorchScript (tracing or scripting) has captured
your model code correctly.
TorchScript also has a representation at a lower level than the code pretty- printer, in the form of IR graphs.
TorchScript uses a static single assignment (SSA) intermediate representation (IR) to represent computation. The instructions in this format consist of ATen (the C++ backend of PyTorch) operators and other primitive operators, including control flow operators for loops and conditionals. As an example:
.. testcode::
@torch.jit.script
def foo(len):
# type: (int) -> torch.Tensor
rv = torch.zeros(3, 4)
for i in range(len):
if i < 10:
rv = rv - 1.0
else:
rv = rv + 1.0
return rv
print(foo.graph)
.. testoutput::
:hide:
...
graph follows the same rules described in the Inspecting Code section
with regard to forward method lookup.
The example script above produces the graph:
graph(%len.1 : int):
%24 : int = prim::Constant[value=1]()
%17 : bool = prim::Constant[value=1]() # test.py:10:5
%12 : bool? = prim::Constant()
%10 : Device? = prim::Constant()
%6 : int? = prim::Constant()
%1 : int = prim::Constant[value=3]() # test.py:9:22
%2 : int = prim::Constant[value=4]() # test.py:9:25
%20 : int = prim::Constant[value=10]() # test.py:11:16
%23 : float = prim::Constant[value=1]() # test.py:12:23
%4 : int[] = prim::ListConstruct(%1, %2)
%rv.1 : Tensor = aten::zeros(%4, %6, %6, %10, %12) # test.py:9:10
%rv : Tensor = prim::Loop(%len.1, %17, %rv.1) # test.py:10:5
block0(%i.1 : int, %rv.14 : Tensor):
%21 : bool = aten::lt(%i.1, %20) # test.py:11:12
%rv.13 : Tensor = prim::If(%21) # test.py:11:9
block0():
%rv.3 : Tensor = aten::sub(%rv.14, %23, %24) # test.py:12:18
-> (%rv.3)
block1():
%rv.6 : Tensor = aten::add(%rv.14, %23, %24) # test.py:14:18
-> (%rv.6)
-> (%17, %rv.13)
return (%rv)
Take the instruction %rv.1 : Tensor = aten::zeros(%4, %6, %6, %10, %12) # test.py:9:10 for
example.
%rv.1 : Tensormeans we assign the output to a (unique) value namedrv.1, that value is ofTensortype and that we do not know its concrete shape.aten::zerosis the operator (equivalent totorch.zeros) and the input list(%4, %6, %6, %10, %12)specifies which values in scope should be passed as inputs. The schema for built-in functions likeaten::zeroscan be found at Builtin Functions.# test.py:9:10is the location in the original source file that generated this instruction. In this case, it is a file named test.py, on line 9, and at character 10.
Notice that operators can also have associated blocks, namely the
prim::Loop and prim::If operators. In the graph print-out, these
operators are formatted to reflect their equivalent source code forms
to facilitate easy debugging.
Graphs can be inspected as shown to confirm that the computation described
by a ScriptModule is correct, in both automated and manual fashion, as
described below.
There are some edge cases that exist where the trace of a given Python function/module will not be representative of the underlying code. These cases can include:
- Tracing of control flow that is dependent on inputs (e.g. tensor shapes)
- Tracing of in-place operations of tensor views (e.g. indexing on the left-hand side of an assignment)
Note that these cases may in fact be traceable in the future.
One way to automatically catch many errors in traces is by using check_inputs
on the torch.jit.trace() API. check_inputs takes a list of tuples
of inputs that will be used to re-trace the computation and verify the
results. For example:
def loop_in_traced_fn(x):
result = x[0]
for i in range(x.size(0)):
result = result * x[i]
return result
inputs = (torch.rand(3, 4, 5),)
check_inputs = [(torch.rand(4, 5, 6),), (torch.rand(2, 3, 4),)]
traced = torch.jit.trace(loop_in_traced_fn, inputs, check_inputs=check_inputs)
Gives us the following diagnostic information:
ERROR: Graphs differed across invocations!
Graph diff:
graph(%x : Tensor) {
%1 : int = prim::Constant[value=0]()
%2 : int = prim::Constant[value=0]()
%result.1 : Tensor = aten::select(%x, %1, %2)
%4 : int = prim::Constant[value=0]()
%5 : int = prim::Constant[value=0]()
%6 : Tensor = aten::select(%x, %4, %5)
%result.2 : Tensor = aten::mul(%result.1, %6)
%8 : int = prim::Constant[value=0]()
%9 : int = prim::Constant[value=1]()
%10 : Tensor = aten::select(%x, %8, %9)
- %result : Tensor = aten::mul(%result.2, %10)
+ %result.3 : Tensor = aten::mul(%result.2, %10)
? ++
%12 : int = prim::Constant[value=0]()
%13 : int = prim::Constant[value=2]()
%14 : Tensor = aten::select(%x, %12, %13)
+ %result : Tensor = aten::mul(%result.3, %14)
+ %16 : int = prim::Constant[value=0]()
+ %17 : int = prim::Constant[value=3]()
+ %18 : Tensor = aten::select(%x, %16, %17)
- %15 : Tensor = aten::mul(%result, %14)
? ^ ^
+ %19 : Tensor = aten::mul(%result, %18)
? ^ ^
- return (%15);
? ^
+ return (%19);
? ^
}
This message indicates to us that the computation differed between when
we first traced it and when we traced it with the check_inputs. Indeed,
the loop within the body of loop_in_traced_fn depends on the shape
of the input x, and thus when we try another x with a different
shape, the trace differs.
In this case, data-dependent control flow like this can be captured using :func:`torch.jit.script` instead:
.. testcode::
def fn(x):
result = x[0]
for i in range(x.size(0)):
result = result * x[i]
return result
inputs = (torch.rand(3, 4, 5),)
check_inputs = [(torch.rand(4, 5, 6),), (torch.rand(2, 3, 4),)]
scripted_fn = torch.jit.script(fn)
print(scripted_fn.graph)
#print(str(scripted_fn.graph).strip())
for input_tuple in [inputs] + check_inputs:
torch.testing.assert_allclose(fn(*input_tuple), scripted_fn(*input_tuple))
.. testoutput::
:hide:
...
Which produces:
graph(%x : Tensor) {
%5 : bool = prim::Constant[value=1]()
%1 : int = prim::Constant[value=0]()
%result.1 : Tensor = aten::select(%x, %1, %1)
%4 : int = aten::size(%x, %1)
%result : Tensor = prim::Loop(%4, %5, %result.1)
block0(%i : int, %7 : Tensor) {
%10 : Tensor = aten::select(%x, %1, %i)
%result.2 : Tensor = aten::mul(%7, %10)
-> (%5, %result.2)
}
return (%result);
}
The tracer produces warnings for several problematic patterns in traced computation. As an example, take a trace of a function that contains an in-place assignment on a slice (a view) of a Tensor:
.. testcode::
def fill_row_zero(x):
x[0] = torch.rand(*x.shape[1:2])
return x
traced = torch.jit.trace(fill_row_zero, (torch.rand(3, 4),))
print(traced.graph)
.. testoutput::
:hide:
...
Produces several warnings and a graph which simply returns the input:
fill_row_zero.py:4: TracerWarning: There are 2 live references to the data region being modified when tracing in-place operator copy_ (possibly due to an assignment). This might cause the trace to be incorrect, because all other views that also reference this data will not reflect this change in the trace! On the other hand, if all other views use the same memory chunk, but are disjoint (e.g. are outputs of torch.split), this might still be safe.
x[0] = torch.rand(*x.shape[1:2])
fill_row_zero.py:6: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
Not within tolerance rtol=1e-05 atol=1e-05 at input[0, 1] (0.09115803241729736 vs. 0.6782537698745728) and 3 other locations (33.00%)
traced = torch.jit.trace(fill_row_zero, (torch.rand(3, 4),))
graph(%0 : Float(3, 4)) {
return (%0);
}
We can fix this by modifying the code to not use the in-place update, but
rather build up the result tensor out-of-place with torch.cat:
.. testcode::
def fill_row_zero(x):
x = torch.cat((torch.rand(1, *x.shape[1:2]), x[1:2]), dim=0)
return x
traced = torch.jit.trace(fill_row_zero, (torch.rand(3, 4),))
print(traced.graph)
.. testoutput::
:hide:
...
TorchScript supports a subset of the builtin 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
See :ref:`builtin-functions` for a full reference of supported functions
Q: I would like to train a model on GPU and do inference on CPU. What are the best practices?
First convert your model from GPU to CPU and then save it, like so:
cpu_model = gpu_model.cpu() sample_input_cpu = sample_input_gpu.cpu() traced_cpu = torch.jit.trace(traced_cpu, sample_input_cpu) torch.jit.save(traced_cpu, "cpu.pth") traced_gpu = torch.jit.trace(traced_gpu, sample_input_gpu) torch.jit.save(traced_gpu, "gpu.pth") # ... later, when using the model: if use_gpu: model = torch.jit.load("gpu.pth") else: model = torch.jit.load("cpu.pth") model(input)This is recommended because the tracer may witness tensor creation on a specific device, so casting an already-loaded model may have unexpected effects. Casting the model before saving it ensures that the tracer has the correct device information.
Q: How do I store attributes on a ScriptModule?
Say we have a model like:
.. testcode:: class Model(nn.Module): def __init__(self): super(Model, self).__init__() self.x = 2 def forward(self): return self.x m = torch.jit.script(Model())If
Modelis instantiated it will result in a compilation error since the compiler doesn't know aboutx. There are 4 ways to inform the compiler of attributes onScriptModule:1.
nn.Parameter- Values wrapped innn.Parameterwill work as they do onnn.Modules2.
register_buffer- Values wrapped inregister_bufferwill work as they do onnn.Modules. This is equivalent to an attribute (see 4) of typeTensor.3. Constants - Annotating a class member as
Final(or adding it to a list called__constants__at the class definition level) will mark the contained names as constants. Constants are saved directly in the code of the model. See Python-defined Constants for details.4. Attributes - Values that are a supported type can be added as mutable attributes. Most types can be inferred but some may need to be specified, see Module Attributes for details.
Q: I would like to trace module's method but I keep getting this error:
RuntimeError: Cannot insert a Tensor that requires grad as a constant. Consider making it a parameter or input, or detaching the gradient
This error usually means that the method you are tracing uses a module's parameters and you are passing the module's method instead of the module instance (e.g.
my_module_instance.forwardvsmy_module_instance).
- Invoking
tracewith a module's method captures module parameters (which may require gradients) as constants.- On the other hand, invoking
tracewith module's instance (e.g.my_module) creates a new module and correctly copies parameters into the new module, so they can accumulate gradients if required.To trace a specific method on a module, see :func:`torch.jit.trace_module <torch.jit.trace_module>`