TorchScript
============
.. contents:: :local:
.. 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.
Creating TorchScript Code
--------------------------
.. autofunction:: script
.. autofunction:: trace
.. autoclass:: ScriptModule
:members:
.. autofunction:: save
.. autofunction:: load
Mixing Tracing and Scripting
----------------------------
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.
Example::
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::
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.Module``\s 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::
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())
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.
As a subset of Python any valid TorchScript function is also a valid Python
function. This makes it possible to remove the ``@torch.jit.script`` decorator 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 Torch.
.. envvar:: PYTORCH_JIT=1
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
ScriptModules, you can use this flag to force everything to run using native
Python. This allows the use of tools like ``pdb`` to debug code.
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"
"``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."
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)::
@torch.jit.script
def an_error(x):
if x:
r = torch.rand(1)
else:
r = 4
return r # Type mismatch: r is set to type Tensor in the true branch
# and type int in the false branch
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:
Example::
@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 annotations.
In our examples, we use comment-based annotations 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 ``torch.jit.annotate``.
Example::
import torch
from torch.jit import Tensor
from typing import List, Tuple
class EmptyDataStructures(torch.jit.ScriptModule):
def __init__(self):
super(EmptyDataStructures, self).__init__()
@torch.jit.script_method
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((x, x))
my_dict = torch.jit.annotate(Dict[str, int], {})
return my_list, my_dict
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.
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 expression must be emitted within the conditional; assigning
a ``None`` check to a variable and using it in the conditional will not refine types.
An attribute like `self.x` will not be refined, but assigning `self.x` to a local
variable first will work.
Example::
@torch.jit.script_method
def optional_unwrap(self, x, y):
# type: (Optional[int], Optional[int]) -> int
if x is None:
x = 1
x = x + 1
z = self.z
if y is not None and z is not None:
x = y + z
return x
User Defined Types
^^^^^^^^^^^^^^^^^^^^^^^^
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
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 are declared by assigning to
self in the ``__init__()`` method
For example, assigning 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:
::
@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))
Expressions
~~~~~~~~~~~
The following Python Expressions are supported
Literals
^^^^^^^^
``True``, ``False``, ``None``, ``'string literals'``, ``"string literals"``,
number literals ``3`` (interpreted as int) ``3.4`` (interpreted as a float)
List Construction
"""""""""""""""""
``[3, 4]``, ``[]``, ``[torch.rand(3), torch.rand(4)]``
.. note::
An empty list is assumed have type ``List[Tensor]``.
The types of other list literals are derived from the type of the members.
To denote an empty list of another type, use ``torch.jit.annotate``.
Tuple Construction
""""""""""""""""""
``(3, 4)``, ``(3,)``
Dict Construction
"""""""""""""""""
``{'hello': 3}``, ``{}``, ``{'a': torch.rand(3), 'b': torch.rand(4)}``
.. note::
An empty dict is assumed have type ``Dict[str, Tensor]``.
The types of other dict literals are derived from the type of the members.
To denote an empty dict of another type, use ``torch.jit.annotate``.
Variables
^^^^^^^^^
``my_variable_name``
.. note::
See `Variable Resolution`_ for how variables are resolved.
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
^^^^^^^^^^
``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 built-in 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)``
When defining a Script method inside of a ScriptModule, the ``@script_method``
annotation is used. Inside of these methods it is possible to call other methods
of this class or access methods on the submodules.
Calling a submodule directly (e.g. ``self.resnet(input)``) is equivalent to
calling its ``forward`` method (e.g. ``self.resnet.forward(input)``)
::
import torch
class MyScriptModule(torch.jit.ScriptModule):
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))
@torch.jit.script_method
def helper(self, input):
return self.resnet(input - self.means)
@torch.jit.script_method
def forward(self, input):
return self.helper(input)
Ternary Expressions
^^^^^^^^^^^^^^^^^^^
``x if x > y else y``
Casts
^^^^^
``float(ten)``
``int(3.5)``
``bool(ten)``
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
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:
::
tup = (3, torch.rand(4))
for x in tup:
print(x)
.. note::
for loops over tuples will unroll the loop, generating a body for
each member of the tuple. The body must type-check correctly for each member.
For loops over constant ``torch.nn.ModuleList``
::
class SubModule(torch.jit.ScriptModule):
def __init__(self):
super(Sub, self).__init__()
self.weight = nn.Parameter(torch.randn(2))
@torch.jit.script_method
def forward(self, input):
return self.weight + input
class MyModule(torch.jit.ScriptModule):
__constants__ = ['mods']
def __init__(self):
super(MyModule, self).__init__()
self.mods = torch.nn.ModuleList([SubModule() for i in range(10)])
@torch.jit.script_method
def forward(self, v):
for module in self.mods:
v = m(v)
return v
.. note::
To use a ``nn.ModuleList`` inside a ``@script_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.
Break and Continue
::
for i in range(5):
if i == 1:
continue
if i == 3:
break
print(i)
Return
``return a, b``
.. note::
TorchScript allows returns in the following circumstances:
1. At the end of a function
2. In an if-statement where <true> and <false> both return
3. In an if-statement where <true> returns and <false> is empty (an early return)
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 sides 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) # Error: undefined value 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.
Example::
def foo(x):
print("I am called with {}".format(x))
import pdb; pdb.set_trace()
return x
@torch.jit.script
def bar(x)
return foo(x + 1)
Attempting to call ``save`` on a ScriptModule that contains calls to Python
functions will fail. The intention is that this pathway is used for debugging
and the calls removed or turned into script functions before saving. If you
want to export a module with a Python function, add the ``@torch.jit.ignore``
decorator to the function which will replace these function calls with an
exception when the model is saved: ::
class M(torch.jit.ScriptModule):
def __init__(self):
super(M, self).__init__()
@torch.jit.script_method
def forward(self, x):
self.ignored_code(x)
return x + 2
@torch.jit.ignore
def ignored_code(self, x):
# non-TorchScript code
import pdb; pdb.set_trace()
m = M()
# Runs, makes upcall to Python to run `ignored_code`
m(torch.ones(2, 2))
# Replaces all calls to `ignored_code` with a `raise`
m.save("m.pt")
loaded = torch.jit.load("m.pt")
# This runs `ignored_code` after saving which will raise an Exception!
loaded(torch.ones(2, 2))
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.
Example: ``math.pi``
2. Attributes of a ScriptModule can be marked constant by listing them
as a member of the ``__constants__`` property of the class:
Example::
class Foo(torch.jit.ScriptModule):
__constants__ = ['a']
def __init__(self):
super(Foo, self).__init__(False)
self.a = 1 + 4
@torch.jit.script_method
def forward(self, input):
return self.a + input
Supported constant Python Values 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 ``ScriptModule``. In a similar vein, attributes of any type can be
assign on a ``ScriptModule`` by wrapping them with ``torch.jit.Attribute`` and
specifying the type. All types available in TorchScript are supported. These
attributes are mutable and are saved in a separate archive in the serialized
model binary. Tensor attributes are semantically the same as buffers.
Example::
class Foo(torch.jit.ScriptModule):
def __init__(self, a_dict):
super(Foo, self).__init__(False)
self.words = torch.jit.Attribute([], List[str])
self.some_dict = torch.jit.Attribute(a_dict, Dict[str, int])
@torch.jit.script_method
def forward(self, input):
# type: (str) -> int
self.words.append(input)
return self.some_dict[input]
Debugging
~~~~~~~~~
Disable JIT for Debugging
^^^^^^^^^^^^^^^^^^^^^^^^^
If you want to disable all JIT modes (tracing and scripting) so you can
debug your program in raw Python, you can use the ``PYTORCH_JIT`` environment
variable. ``PYTORCH_JIT`` can be used to globally disable the
JIT by setting its value to ``0``. 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 ``@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.
Inspecting Code
^^^^^^^^^^^^^^^
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::
@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)
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 script above produces the code::
def forward(self,
len: int) -> Tensor:
rv = torch.zeros([3, 4], dtype=None, layout=None, device=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.
Interpreting Graphs
^^^^^^^^^^^^^^^^^^^
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::
@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)
``.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 : int) {
%15 : int = prim::Constant[value=1]()
%9 : bool = prim::Constant[value=1]()
%7 : Device = prim::Constant[value="cpu"]()
%6 : int = prim::Constant[value=0]()
%5 : int = prim::Constant[value=6]()
%1 : int = prim::Constant[value=3]()
%2 : int = prim::Constant[value=4]()
%11 : int = prim::Constant[value=10]()
%14 : float = prim::Constant[value=1]()
%4 : int[] = prim::ListConstruct(%1, %2)
%rv.1 : Tensor = aten::zeros(%4, %5, %6, %7)
%rv : Tensor = prim::Loop(%len, %9, %rv.1)
block0(%i : int, %13 : Tensor) {
%12 : bool = aten::lt(%i, %11)
%rv.4 : Tensor = prim::If(%12)
block0() {
%rv.2 : Tensor = aten::sub(%13, %14, %15)
-> (%rv.2)
}
block1() {
%rv.3 : Tensor = aten::add(%13, %14, %15)
-> (%rv.3)
}
-> (%9, %rv.4)
}
return (%rv);
}
Take the instruction ``%rv.1 : Dynamic = aten::zeros(%3, %4, %5, %6)`` for
example. ``%rv.1 : Dynamic`` means we assign the output to a (unique)
value named ``rv.1``, and that value is of ``Dynamic`` type, i.e. we do
not know its concrete shape. ``aten::zeros`` is the operator (equivalent
to ``torch.zeros``) and the input list ``(%3, %4, %5, %6)`` specifies which
values in scope should be passed as inputs. The schema for built-in functions
like ``aten::zeros`` can be found at `Builtin Functions`_.
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.
Tracing Edge Cases
^^^^^^^^^^^^^^^^^^
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.
Automatic Trace Checking
^^^^^^^^^^^^^^^^^^^^^^^^
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
script instead::
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)
for input_tuple in [inputs] + check_inputs:
torch.testing.assert_allclose(fn(*input_tuple), scripted_fn(*input_tuple))
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);
}
Tracer Warnings
^^^^^^^^^^^^^^^
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::
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)
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`::
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)
Frequently Asked Questions
--------------------------
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: ::
class Model(torch.jit.ScriptModule):
def __init__(self):
super(Model, self).__init__()
self.x = 2
@torch.jit.script_method
def forward(self):
return self.x
If ``Model`` is instantiated it will result in a compilation error
since the compiler doesn't know about ``x``. There are 4 ways to inform the
compiler of attributes on ``ScriptModule``:
1. ``nn.Parameter`` - values wrapped in ``nn.Parameter`` will work as they
do on ``nn.Module``\s
2. ``register_buffer`` - values wrapped in ``register_buffer`` will work as
they do on ``nn.Module``\s
3. ``__constants__`` - adding 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`_.
4. ``torch.jit.Attribute`` - values wrapped in ``torch.jit.Attribute`` can
be any ``TorchScript`` type, be mutated and are saved outside of the code of
the model. See `Module Attributes`_.
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 module's parameters and
you are passing module's method instead of a module instance (e.g. ``my_module_instance.forward`` vs ``my_module_instance``).
- Invoking ``trace`` with module's method captures module parameters (which may require gradients) as **constants**.
- On the other hand, invoking ``trace`` with 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.
Given that ``trace`` treats ``my_module_instance.forward`` as a standalone function, it also means there is **not** currently a way to trace
arbitrary methods in the module except for ``forward`` that use module's parameters.
Version **1.1.1** will add a new API ``trace_module`` that will allow users to trace any method in the module and more than one method ::
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv = nn.Conv2d(1, 1, 3)
def forward(self, x):
return self.conv(x)
def weighted_kernel_sum(self, weight):
return weight * self.conv.weight
example_weight = torch.rand(1, 1, 3, 3)
example_forward_input = torch.rand(1, 1, 3, 3)
n = Net()
inputs = {'forward' : example_forward_input, 'weighted_kernel_sum' : example_weight}
module = torch.jit.trace_module(n, inputs)
Builtin Functions
~~~~~~~~~~~~~~~~~
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.fold.Fold
torch.nn.modules.fold.Unfold
torch.nn.modules.rnn.GRU
torch.nn.modules.rnn.RNN
.. automodule:: torch.jit.supported_ops