torch.jit.script

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

torch.jit.script can be used as a function for modules and functions, and as a decorator @torch.jit.script for TorchScript Classes and functions.

Parameters

obj (callable, class, or nn.Module) – compile.

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.

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.ScriptFuncion

# 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 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(MyModule, self).__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(MyModule, self).__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(MyModule, self).__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)))