torch.jit.script

torch.jit.script(obj, optimize=None, _frames_up=0, _rcb=None, example_inputs=None)

Scripting a function or nn.Module will inspect the source code, compile it as TorchScript code using the TorchScript compiler, and return a ScriptModule or ScriptFunction. TorchScript itself is a subset of the Python language, so not all features in Python work, but we provide enough functionality to compute on tensors and do control-dependent operations. For a complete guide, see the TorchScript Language Reference.

Scripting a dictionary or list copies the data inside it into a TorchScript instance than can be subsequently passed by reference between Python and TorchScript with zero copy overhead.

torch.jit.script can be used as a function for modules, functions, dictionaries and lists

and as a decorator @torch.jit.script for TorchScript Classes and functions.

Parameters:

• obj (Callable, class, or nn.Module) – The nn.Module, function, class type, dictionary, or list to compile.

• example_inputs (Union[List[Tuple], Dict[Callable, List[Tuple]], None]) – Provide example inputs to annotate the arguments for a function or nn.Module.

Returns:

If obj is nn.Module, script returns a ScriptModule object. The returned ScriptModule will have the same set of sub-modules and parameters as the original nn.Module. If obj is a standalone function, a ScriptFunction will be returned. If obj is a dict, then script returns an instance of torch._C.ScriptDict. If obj is a list, then script returns an instance of torch._C.ScriptList.

Scripting a function

The @torch.jit.script decorator will construct a ScriptFunction by compiling the body of the function.

Example (scripting a function):

import torch

@torch.jit.script
def foo(x, y):
    if x.max() > y.max():
        r = x
    else:
        r = y
    return r

print(type(foo))  # torch.jit.ScriptFunction

# See the compiled graph as Python code
print(foo.code)

# Call the function using the TorchScript interpreter
foo(torch.ones(2, 2), torch.ones(2, 2))

**Scripting a function using example_inputs

Example inputs can be used to annotate a function arguments.

Example (annotating a function before scripting):

import torch

def test_sum(a, b):
    return a + b

# Annotate the arguments to be int
scripted_fn = torch.jit.script(test_sum, example_inputs=[(3, 4)])

print(type(scripted_fn))  # torch.jit.ScriptFunction

# See the compiled graph as Python code
print(scripted_fn.code)

# Call the function using the TorchScript interpreter
scripted_fn(20, 100)

Scripting an nn.Module

Scripting an nn.Module by default will compile the forward method and recursively compile any methods, submodules, and functions called by forward. If a nn.Module only uses features supported in TorchScript, no changes to the original module code should be necessary. script will construct ScriptModule that has copies of the attributes, parameters, and methods of the original module.

Example (scripting a simple module with a Parameter):

import torch

class MyModule(torch.nn.Module):
    def __init__(self, N, M):
        super().__init__()
        # This parameter will be copied to the new ScriptModule
        self.weight = torch.nn.Parameter(torch.rand(N, M))

        # When this submodule is used, it will be compiled
        self.linear = torch.nn.Linear(N, M)

    def forward(self, input):
        output = self.weight.mv(input)

        # This calls the `forward` method of the `nn.Linear` module, which will
        # cause the `self.linear` submodule to be compiled to a `ScriptModule` here
        output = self.linear(output)
        return output

scripted_module = torch.jit.script(MyModule(2, 3))

Example (scripting a module with traced submodules):

import torch
import torch.nn as nn
import torch.nn.functional as F

class MyModule(nn.Module):
    def __init__(self):
        super().__init__()
        # torch.jit.trace produces a ScriptModule's conv1 and conv2
        self.conv1 = torch.jit.trace(nn.Conv2d(1, 20, 5), torch.rand(1, 1, 16, 16))
        self.conv2 = torch.jit.trace(nn.Conv2d(20, 20, 5), torch.rand(1, 20, 16, 16))

    def forward(self, input):
        input = F.relu(self.conv1(input))
        input = F.relu(self.conv2(input))
        return input

scripted_module = torch.jit.script(MyModule())

To compile a method other than forward (and recursively compile anything it calls), add the @torch.jit.export decorator to the method. To opt out of compilation use @torch.jit.ignore or @torch.jit.unused.

Example (an exported and ignored method in a module):

import torch
import torch.nn as nn

class MyModule(nn.Module):
    def __init__(self):
        super().__init__()

    @torch.jit.export
    def some_entry_point(self, input):
        return input + 10

    @torch.jit.ignore
    def python_only_fn(self, input):
        # This function won't be compiled, so any
        # Python APIs can be used
        import pdb
        pdb.set_trace()

    def forward(self, input):
        if self.training:
            self.python_only_fn(input)
        return input * 99

scripted_module = torch.jit.script(MyModule())
print(scripted_module.some_entry_point(torch.randn(2, 2)))
print(scripted_module(torch.randn(2, 2)))

Example ( Annotating forward of nn.Module using example_inputs):

import torch
import torch.nn as nn
from typing import NamedTuple

class MyModule(NamedTuple):
result: List[int]

class TestNNModule(torch.nn.Module):
    def forward(self, a) -> MyModule:
        result = MyModule(result=a)
        return result

pdt_model = TestNNModule()

# Runs the pdt_model in eager model with the inputs provided and annotates the arguments of forward
scripted_model = torch.jit.script(pdt_model, example_inputs={pdt_model: [([10, 20, ], ), ], })

# Run the scripted_model with actual inputs
print(scripted_model([20]))