Python Custom Operators

What you will learn
  • How to integrate custom operators written in Python with PyTorch

  • How to test custom operators using torch.library.opcheck

  • PyTorch 2.4 or later

PyTorch offers a large library of operators that work on Tensors (e.g. torch.add, torch.sum, etc). However, you might wish to use a new customized operator with PyTorch, perhaps written by a third-party library. This tutorial shows how to wrap Python functions so that they behave like PyTorch native operators. Reasons why you may wish to create a custom operator in PyTorch include:

  • Treating an arbitrary Python function as an opaque callable with respect to torch.compile (that is, prevent torch.compile from tracing into the function).

  • Adding training support to an arbitrary Python function

Please note that if your operation can be expressed as a composition of existing PyTorch operators, then there is usually no need to use the custom operator API – everything (for example torch.compile, training support) should just work.

Example: Wrapping PIL’s crop into a custom operator

Let’s say that we are using PIL’s crop operation.

import torch
from torchvision.transforms.functional import to_pil_image, pil_to_tensor
import PIL
import IPython
import matplotlib.pyplot as plt

def crop(pic, box):
    img = to_pil_image(pic.cpu())
    cropped_img = img.crop(box)
    return pil_to_tensor(cropped_img).to(pic.device) / 255.

def display(img):
    plt.imshow(img.numpy().transpose((1, 2, 0)))

img = torch.ones(3, 64, 64)
img *= torch.linspace(0, 1, steps=64) * torch.linspace(0, 1, steps=64).unsqueeze(-1)
cropped_img = crop(img, (10, 10, 50, 50))

crop is not handled effectively out-of-the-box by torch.compile: torch.compile induces a “graph break” on functions it is unable to handle and graph breaks are bad for performance. The following code demonstrates this by raising an error (torch.compile with fullgraph=True raises an error if a graph break occurs).

def f(img):
    return crop(img, (10, 10, 50, 50))

# The following raises an error. Uncomment the line to see it.
# cropped_img = f(img)

In order to black-box crop for use with torch.compile, we need to do two things:

  1. wrap the function into a PyTorch custom operator.

  2. add a “FakeTensor kernel” (aka “meta kernel”) to the operator. Given the metadata (e.g. shapes) of the input Tensors, this function says how to compute the metadata of the output Tensor(s).

from typing import Sequence

# Use torch.library.custom_op to define a new custom operator.
# If your operator mutates any input Tensors, their names must be specified
# in the ``mutates_args`` argument.
@torch.library.custom_op("mylib::crop", mutates_args=())
def crop(pic: torch.Tensor, box: Sequence[int]) -> torch.Tensor:
    img = to_pil_image(pic.cpu())
    cropped_img = img.crop(box)
    return (pil_to_tensor(cropped_img) / 255.).to(pic.device, pic.dtype)

# Use register_fake to add a ``FakeTensor`` kernel for the operator
def _(pic, box):
    channels = pic.shape[0]
    x0, y0, x1, y1 = box
    return pic.new_empty(channels, y1 - y0, x1 - x0)

After this, crop now works without graph breaks:

def f(img):
    return crop(img, (10, 10, 50, 50))

cropped_img = f(img)

Adding training support for crop

Use torch.library.register_autograd to add training support for an operator. Prefer this over directly using torch.autograd.Function; some compositions of autograd.Function with PyTorch operator registration APIs can lead to (and has led to) silent incorrectness when composed with torch.compile.

The gradient formula for crop is essentially PIL.paste (we’ll leave the derivation as an exercise to the reader). Let’s first wrap paste into a custom operator:

@torch.library.custom_op("mylib::paste", mutates_args=())
def paste(im1: torch.Tensor, im2: torch.Tensor, coord: Sequence[int]) -> torch.Tensor:
    assert im1.device == im2.device
    assert im1.dtype == im2.dtype
    im1_pil = to_pil_image(im1.cpu())
    im2_pil = to_pil_image(im2.cpu())
    PIL.Image.Image.paste(im1_pil, im2_pil, coord)
    return (pil_to_tensor(im1_pil) / 255.).to(im1.device, im1.dtype)

def _(im1, im2, coord):
    assert im1.device == im2.device
    assert im1.dtype == im2.dtype
    return torch.empty_like(im1)

And now let’s use register_autograd to specify the gradient formula for crop:

def backward(ctx, grad_output):
    grad_input = grad_output.new_zeros(ctx.pic_shape)
    grad_input = paste(grad_input, grad_output, ctx.coords)
    return grad_input, None

def setup_context(ctx, inputs, output):
    pic, box = inputs
    ctx.coords = box[:2]
    ctx.pic_shape = pic.shape

crop.register_autograd(backward, setup_context=setup_context)

Note that the backward must be a composition of PyTorch-understood operators, which is why we wrapped paste into a custom operator instead of directly using PIL’s paste.

img = img.requires_grad_()
result = crop(img, (10, 10, 50, 50))

This is the correct gradient, with 1s (white) in the cropped region and 0s (black) in the unused region.

Testing Python Custom operators

Use torch.library.opcheck to test that the custom operator was registered correctly. This does not test that the gradients are mathematically correct; please write separate tests for that (either manual ones or torch.autograd.gradcheck).

To use opcheck, pass it a set of example inputs to test against. If your operator supports training, then the examples should include Tensors that require grad. If your operator supports multiple devices, then the examples should include Tensors from each device.

examples = [
    [torch.randn(3, 64, 64), [0, 0, 10, 10]],
    [torch.randn(3, 91, 91, requires_grad=True), [10, 0, 20, 10]],
    [torch.randn(3, 60, 60, dtype=torch.double), [3, 4, 32, 20]],
    [torch.randn(3, 512, 512, requires_grad=True, dtype=torch.double), [3, 4, 32, 45]],

for example in examples:
    torch.library.opcheck(crop, example)

Mutable Python Custom operators

You can also wrap a Python function that mutates its inputs into a custom operator. Functions that mutate inputs are common because that is how many low-level kernels are written; for example, a kernel that computes sin may take in the input and an output tensor and write input.sin() to the output tensor.

We’ll use numpy.sin to demonstrate an example of a mutable Python custom operator.

import numpy as np

@torch.library.custom_op("mylib::numpy_sin", mutates_args={"output"}, device_types="cpu")
def numpy_sin(input: torch.Tensor, output: torch.Tensor) -> None:
    assert input.device == output.device
    assert input.device.type == "cpu"
    input_np = input.numpy()
    output_np = output.numpy()
    np.sin(input_np, out=output_np)

Because the operator doesn’t return anything, there is no need to register a FakeTensor kernel (meta kernel) to get it to work with torch.compile.

def f(x):
    out = torch.empty(3)
    numpy_sin(x, out)
    return out

x = torch.randn(3)
y = f(x)
assert torch.allclose(y, x.sin())

And here’s an opcheck run telling us that we did indeed register the operator correctly. opcheck would error out if we forgot to add the output to mutates_args, for example.

example_inputs = [
    [torch.randn(3), torch.empty(3)],
    [torch.randn(0, 3), torch.empty(0, 3)],
    [torch.randn(1, 2, 3, 4, dtype=torch.double), torch.empty(1, 2, 3, 4, dtype=torch.double)],

for example in example_inputs:
    torch.library.opcheck(numpy_sin, example)


In this tutorial, we learned how to use torch.library.custom_op to create a custom operator in Python that works with PyTorch subsystems such as torch.compile and autograd.

This tutorial provides a basic introduction to custom operators. For more detailed information, see:

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