TensorFlow Custom Op

This is a guide for users who want to write custom c++ op for TensorFlow and distribute the op as a pip package. This repository serves as both a working example of the op building and packaging process, as well as a template/starting point for writing your own ops. The way this repository is set up allow you to build your custom ops from TensorFlow's pip package instead of building TensorFlow from scratch. This guarantee that the shared library you build will work with TensorFlow's pip packages.

This guide currently supports Ubuntu and Windows custom ops, and it includes examples for both cpu and gpu ops.

Starting from Aug 1, 2019, nightly previews tf-nightly and tf-nightly-gpu, as well as official releases tensorflow and tensorflow-gpu past version 1.14.0 are now built with a different environment (Ubuntu 16.04 compared to Ubuntu 14.04, for example) as part of our effort to make TensorFlow's pip pacakges manylinux2010 compatible. We have also updated this guide to accommodate both sets of pip packages. Please check the version of TensorFlow pip package you are trying to build against, and follow the corresponding guide below.

Build Example zero_out Op (CPU only)

If you want to try out the process of building a pip package for custom op, you can use the source code from this repository following the instructions below.

For Windows Users

You can skip this section if you are not building on Windows. If you are building custom ops for Windows platform, you will need similar setup as building TensorFlow from source mentioned here. Additionally, you can skip all the Docker steps from the instructions below. Otherwise, the bazel commands to build and test custom ops stay the same.

Setup Docker Container

You are going to build the op inside a Docker container. Pull the provided Docker image from TensorFlow's Docker hub and start a container.

Use the following command if the TensorFlow pip package you are building against is not yet manylinux2010 compatible:

  docker pull tensorflow/tensorflow:custom-op-ubuntu14
  docker run -it tensorflow/tensorflow:custom-op-ubuntu14 /bin/bash

And the following instead if it is manylinux2010 compatible:

  docker pull tensorflow/tensorflow:custom-op-ubuntu16
  docker run -it tensorflow/tensorflow:custom-op-ubuntu16 /bin/bash

Inside the Docker container, clone this repository. The code in this repository came from the Adding an op guide.

git clone https://github.com/tensorflow/custom-op.git
cd custom-op

Build PIP Package

You can build the pip package with either Bazel or make.

With bazel:

  ./configure.sh
  bazel build build_pip_pkg
  bazel-bin/build_pip_pkg artifacts

With Makefile:

  make zero_out_pip_pkg

Install and Test PIP Package

Once the pip package has been built, you can install it with,

pip install artifacts/*.whl

Then test out the pip package

cd ..
python -c "import tensorflow as tf;import tensorflow_custom_ops as custom_ops_module;print(custom_ops_module.zero_out([[1,2], [3,4]]).eval(session=tf.Session()))"

And you should see the op zeroed out all input elements except the first one:

[[1 0]
 [0 0]]

Create and Distribute Custom Ops

Now you are ready to write and distribute your own ops. The example in this repository has done the boiling plate work for setting up build systems and package files needed for creating a pip package. We recommend using this repository as a template.

Template Overview

First let's go through a quick overview of the folder structure of this template repository.

├── gpu  # Set up crosstool and CUDA libraries for Nvidia GPU, only needed for GPU ops
│   ├── crosstool/
│   ├── cuda/
│   ├── BUILD
│   └── cuda_configure.bzl
|
├── tensorflow_zero_out  # A CPU only op
│   ├── cc
│   │   ├── kernels  # op kernel implementation
│   │   │   └── zero_out_kernels.cc
│   │   └── ops  # op interface definition
│   │       └── zero_out_ops.cc
│   ├── python
│   │   ├── ops
│   │   │   ├── __init__.py
│   │   │   ├── zero_out_ops.py   # Load and extend the ops in python
│   │   │   └── zero_out_ops_test.py  # tests for ops
│   │   └── __init__.py
|   |
│   ├── BUILD  # BUILD file for all op targets
│   └── __init__.py  # top level __init__ file that imports the custom op
│
├── tensorflow_time_two  # A GPU op
│   ├── cc
│   │   ├── kernels  # op kernel implementation
│   │   │   |── time_two.h
│   │   │   |── time_two_kernels.cc
│   │   │   └── time_two_kernels.cu.cc  # GPU kernel
│   │   └── ops  # op interface definition
│   │       └── time_two_ops.cc
│   ├── python
│   │   ├── ops
│   │   │   ├── __init__.py
│   │   │   ├── time_two_ops.py   # Load and extend the ops in python
│   │   │   └── time_two_ops_test.py  # tests for ops
│   │   └── __init__.py
|   |
│   ├── BUILD  # BUILD file for all op targets
│   └── __init__.py  # top level __init__ file that imports the custom op
|
├── tf  # Set up TensorFlow pip package as external dependency for Bazel
│   ├── BUILD
│   ├── BUILD.tpl
│   └── tf_configure.bzl
|
├── BUILD  # top level Bazel BUILD file that contains pip package build target
├── build_pip_pkg.sh  # script to build pip package for Bazel and Makefile
├── configure.sh  # script to install TensorFlow and setup action_env for Bazel
├── LICENSE
├── Makefile  # Makefile for building shared library and pip package
├── setup.py  # file for creating pip package
├── MANIFEST.in  # files for creating pip package
├── README.md
└── WORKSPACE  # Used by Bazel to specify tensorflow pip package as an external dependency

The op implementation, including both c++ and python code, goes under tensorflow_zero_out dir for CPU only ops, or tensorflow_time_two dir for GPU ops. You will want to replace either directory with the corresponding content of your own ops. tf folder contains the code for setting up TensorFlow pip package as an external dependency for Bazel only. You shouldn't need to change the content of this folder. You also don't need this folder if you are using other build systems, such as Makefile. The gpu folder contains the code for setting up CUDA libraries and toolchain. You only need the gpu folder if you are writing a GPU op and using bazel. To build a pip package for your op, you will also need to update a few files at the top level of the template, for example, setup.py, MANIFEST.in and build_pip_pkg.sh.

Setup

First, clone this template repo.

git clone https://github.com/tensorflow/custom-op.git my_op
cd my_op

Docker

Next, set up a Docker container using the provided Docker image for building and testing the ops. We provide two sets of Docker images for different versions of pip packages. If the pip package you are building against was released before Aug 1, 2019 and has manylinux1 tag, please use Docker images tensorflow/tensorflow:custom-op-ubuntu14 and tensorflow/tensorflow:custom-op-gpu-ubuntu14, which are based on Ubuntu 14.04. Otherwise, for the newer manylinux2010 packages, please use Docker images tensorflow/tensorflow:custom-op-ubuntu16 and tensorflow/tensorflow:custom-op-gpu-ubuntu16 instead. All Docker images come with Bazel pre-installed, as well as the corresponding toolchain used for building the released TensorFlow pacakges. We have seen many cases where dependency version differences and ABI incompatibilities cause the custom op extension users build to not work properly with TensorFlow's released pip packages. Therefore, it is highly recommended to use the provided Docker image to build your custom op. To get the CPU Docker image, run one of the following command based on which pip package you are building against:

# For pip packages labeled manylinux1
docker pull tensorflow/tensorflow:custom-op-ubuntu14

# For manylinux2010
docker pull tensorflow/tensorflow:custom-op-ubuntu16

For GPU, run

# For pip packages labeled manylinux1
docker pull tensorflow/tensorflow:custom-op-gpu-ubuntu14

# For manylinux2010
docker pull tensorflow/tensorflow:custom-op-gpu-ubuntu16

You might want to use Docker volumes to map a work_dir from host to the container, so that you can edit files on the host, and build with the latest changes in the Docker container. To do so, run the following for CPU

# For pip packages labeled manylinux1
docker run -it -v ${PWD}:/working_dir -w /working_dir  tensorflow/tensorflow:custom-op-ubuntu14

# For manylinux2010
docker run -it -v ${PWD}:/working_dir -w /working_dir  tensorflow/tensorflow:custom-op-ubuntu16

For GPU, you want to use nvidia-docker:

# For pip packages labeled manylinux1
docker run --runtime=nvidia --privileged  -it -v ${PWD}:/working_dir -w /working_dir  tensorflow/tensorflow:custom-op-gpu-ubuntu14

# For manylinux2010
docker run --runtime=nvidia --privileged  -it -v ${PWD}:/working_dir -w /working_dir  tensorflow/tensorflow:custom-op-gpu-ubuntu16

Run configure.sh

Last step before starting implementing the ops, you want to set up the build environment. The custom ops will need to depend on TensorFlow headers and shared library libtensorflow_framework.so, which are distributed with TensorFlow official pip package. If you would like to use Bazel to build your ops, you might also want to set a few action_envs so that Bazel can find the installed TensorFlow. We provide a configure script that does these for you. Simply run ./configure.sh in the docker container and you are good to go.

Add Op Implementation

Now you are ready to implement your op. Following the instructions at Adding a New Op, add definition of your op interface under <your_op>/cc/ops/ and kernel implementation under <your_op>/cc/kernels/.

Build and Test CPU Op

Bazel

To build the custom op shared library with Bazel, follow the cc_binary example in tensorflow_zero_out/BUILD. You will need to depend on the header files and libtensorflow_framework.so from TensorFlow pip package to build your op. Earlier we mentioned that the template has already setup TensorFlow pip package as an external dependency in tf directory, and the pip package is listed as local_config_tf in WORKSPACE file. Your op can depend directly on TensorFlow header files and 'libtensorflow_framework.so' with the following:

    deps = [
        "@local_config_tf//:libtensorflow_framework",
        "@local_config_tf//:tf_header_lib",
    ],

You will need to keep both above dependencies for your op. To build the shared library with Bazel, run the following command in your Docker container

bazel build tensorflow_zero_out:python/ops/_zero_out_ops.so

Makefile

To build the custom op shared library with make, follow the example in Makefile for _zero_out_ops.so and run the following command in your Docker container:

make op

Extend and Test the Op in Python

Once you have built your custom op shared library, you can follow the example in tensorflow_zero_out/python/ops, and instructions here to create a module in Python for your op. Both guides use TensorFlow API tf.load_op_library, which loads the shared library and registers the ops with the TensorFlow framework.

from tensorflow.python.framework import load_library
from tensorflow.python.platform import resource_loader

_zero_out_ops = load_library.load_op_library(
    resource_loader.get_path_to_datafile('_zero_out_ops.so'))
zero_out = _zero_out_ops.zero_out

You can also add Python tests like what we have done in tensorflow_zero_out/python/ops/zero_out_ops_test.py to check that your op is working as intended.

Run Tests with Bazel

To add the python library and tests targets to Bazel, please follow the examples for py_library target tensorflow_zero_out:zero_out_ops_py and py_test target tensorflow_zero_out:zero_out_ops_py_test in tensorflow_zero_out/BUILD file. To run your test with bazel, do the following in Docker container,

bazel test tensorflow_zero_out:zero_out_ops_py_test

Run Tests with Make

To add the test target to make, please follow the example in Makefile. To run your python test, simply run the following in Docker container,

make test_zero_out

Build and Test GPU Op

Bazel

To build the custom GPU op shared library with Bazel, follow the cc_binary example in tensorflow_time_two/BUILD. Similar to CPU custom ops, you can directly depend on TensorFlow header files and 'libtensorflow_framework.so' with the following:

    deps = [
        "@local_config_tf//:libtensorflow_framework",
        "@local_config_tf//:tf_header_lib",
    ],

Additionally, when you ran configure inside the GPU container, config=cuda will be set for bazel command, which will also automatically include cuda shared library and cuda headers as part of the dependencies only for GPU version of the op: if_cuda_is_configured([":cuda", "@local_config_cuda//cuda:cuda_headers"]).

To build the shared library with Bazel, run the following command in your Docker container

bazel build tensorflow_time_two:python/ops/_time_two_ops.so

Makefile

To build the custom op shared library with make, follow the example in Makefile for _time_two_ops.so and run the following command in your Docker container:

make time_two_op

Extend and Test the Op in Python

Once you have built your custom op shared library, you can follow the example in tensorflow_time_two/python/ops, and instructions here to create a module in Python for your op. This part is the same as CPU custom op as shown above.

Run Tests with Bazel

Similar to CPU custom op, to run your test with bazel, do the following in Docker container,

bazel test tensorflow_time_two:time_two_ops_py_test

Run Tests with Make

To add the test target to make, please follow the example in Makefile. To run your python test, simply run the following in Docker container,

make time_two_test

Build PIP Package

Now your op works, you might want to build a pip package for it so the community can also benefit from your work. This template provides the basic setup needed to build your pip package. First, you will need to update the following top level files based on your op.

• setup.py contains information about your package (such as the name and version) as well as which code files to include.
• MANIFEST.in contains the list of additional files you want to include in the source distribution. Here you want to make sure the shared library for your custom op is included in the pip package.
• build_pip_pkg.sh creates the package hierarchy, and calls bdist_wheel to assemble your pip package.

You can use either Bazel or Makefile to build the pip package.

Build with Bazel

You can find the target for pip package in the top level BUILD file. Inside the data list of this build_pip_pkg target, you want to include the python library target //tensorflow_zero_out:zero_out_py in addition to the top level files. To build the pip package builder, run the following command in Docker container,

bazel build :build_pip_pkg

The bazel build command creates a binary named build_pip_package, which you can use to build the pip package. For example, the following builds your .whl package in the artifacts directory:

bazel-bin/build_pip_pkg artifacts

Build with make

Building with make also invoke the same build_pip_pkg.sh script. You can run,

make pip_pkg

Test PIP Package

Before publishing your pip package, test your pip package.

pip install artifacts/*.whl
python -c "import tensorflow as tf;import tensorflow_custom_ops as custom_ops_module;print(custom_ops_module.zero_out([[1,2], [3,4]]).eval(session=tf.Session()))"

Publish PIP Package

Once your pip package has been thoroughly tested, you can distribute your package by uploading your package to the Python Package Index. Please follow the official instruction from Pypi.

FAQ

Here are some issues our users have ran into and possible solutions. Feel free to send us a PR to add more entries.

Issue	How to?
Do I need both the toolchain and the docker image?	Yes, you will need both to get the same setup we use to build TensorFlow's official pip package.
How do I also create a manylinux2010 binary?	You can use auditwheel version 2.0.0 or newer.
What do I do if I get ValueError: Cannot repair wheel, because required library "libtensorflow_framework.so.1" could not be located or ValueError: Cannot repair wheel, because required library "libtensorflow_framework.so.2" could not be located with auditwheel?	Please see this related issue.