torch.jit.ignore — PyTorch 2.7 documentation (original) (raw)

torch.jit.ignore(drop=False, **kwargs)[source][source]¶

This decorator indicates to the compiler that a function or method should be ignored and left as a Python function. This allows you to leave code in your model that is not yet TorchScript compatible. If called from TorchScript, ignored functions will dispatch the call to the Python interpreter. Models with ignored functions cannot be exported; use @torch.jit.unused instead.

Example (using @torch.jit.ignore on a method):

import torch import torch.nn as nn

class MyModule(nn.Module): @torch.jit.ignore def debugger(self, x): import pdb

    pdb.set_trace()

def forward(self, x):
    x += 10
    # The compiler would normally try to compile `debugger`,
    # but since it is `@ignore`d, it will be left as a call
    # to Python
    self.debugger(x)
    return x

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

Error! The call debugger cannot be saved since it calls into Python

m.save("m.pt")

Example (using @torch.jit.ignore(drop=True) on a method):

import torch import torch.nn as nn

class MyModule(nn.Module): @torch.jit.ignore(drop=True) def training_method(self, x): import pdb pdb.set_trace()

def forward(self, x):
    if self.training:
        self.training_method(x)
    return x

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

This is OK since training_method is not saved, the call is replaced

with a raise.

m.save("m.pt")