GraphDef Editor

A port of the TensorFlow contrib.graph_editor package that operates over serialized graphs

TensorFlow versions prior to version 2.0 had a Python graph editor in contrib.graph_editor. This functionality is slated to be removed in TensorFlow 2.0, along with the rest of the contrib package (see the RFC). This project brings back the graph editor as a standalone Python package.

The original graph editor operated over TensorFlow's Python classes Graph, Variable, Operator, etc., often poking into the internals of these classes. As a result of this design, the graph editor needed to be updated whenever the underlying classes changed.

The GraphDef Editor operates over serialized TensorFlow graphs represented as GraphDef protocol buffer messages. Although TensorFlow's serialization format is technically not a public API, there is public documentation for its structure, and the format changes much less frequently than the Python classes that the original graph editor depended on. TensorFlow's C++ Graph Transform Tool also operates over serialized graphs.

Example usage:

import numpy as np
import tensorflow as tf
import graph_def_editor as gde
# Create a graph
tf_g = tf.Graph()
with tf_g.as_default():
  a = tf.constant(1.0, shape=[2, 3], name="a")
  c = tf.add(
      tf.placeholder(dtype=np.float32),
      tf.placeholder(dtype=np.float32),
      name="c")

# Serialize the graph
g = gde.Graph(tf_g.as_graph_def())

# Modify the graph.
# In this case we replace the two input placeholders with constants.
# One of the constants (a) is a node that was in the original graph.
# The other one (b) we create here.
b = gde.make_const(g, "b", np.full([2, 3], 2.0, dtype=np.float32))
gde.swap_inputs(g[c.op.name], [g[a.name], b.output(0)])

# Reconstitute the modified serialized graph as TensorFlow graph
with g.to_tf_graph().as_default():

  # Run a session using the modified graph and print the value of c
  with tf.Session() as sess:
    res = sess.run(c.name)
    print("Result is:\n{}".format(res))

Result is:
[[3. 3. 3.]
 [3. 3. 3.]]

Project status

This project is a work in progress.

Current status:

• All of the original project's regression tests pass. We have added 20 additional regression tests to cover new functionality.
• We have added new features to support graph rewrites, including structural pattern matching and fixed-point graph modification.
• We have implemented several new graph rewrites.
• The simple example script from the original project runs. We have also added new examples of new functionality; see the examples directory.

Contents of root directory:

• LICENSE: This project is released under an Apache v2 license
• env: Not in git repo; create by running scripts/env.sh. Anaconda virtualenv for running tests and notebooks in this project.
• examples: Example scripts. To run these scripts from the root directory of this project, first run scripts/env.sh to create an Anaconda environment, then use the command

  PYTHONPATH=$PWD env/bin/python examples/script_name.py
  

  where script_name.py is the name of the example script.
• notebooks: Jupyter notebooks.
• graph_def_editor: Source code for the Python package
• scripts: Useful shell scripts for development.
• setup.py: Setup script to make this project pip-installable with setuptools
• tests: pytest tests. To run these tests, create env and run scripts/test.sh

Pip install instructions

We have not yet posted a binary release of this library, but you can pip install this project directly from the source tree. We recommend using a virtualenv or an Anaconda environment for this purpose. Here is an example series of shell commands to create an Anaconda environment and pip install this project from source:

$ conda create -y --prefix ./myenv python=3.6 numpy tensorflow
$ conda activate ./myenv
$ git clone https://github.com/CODAIT/graph_def_editor.git
$ pip install ./graph_def_editor

IDE setup instructions

1. Install IntelliJ and the community Python plugin.
2. Run the script scripts/env.sh to create an Anaconda enviroment under env.
3. Import the root directory of this repository as a new project. Use the Anaconda environment at env/bin/python as the Python for the project.
4. In the "Project" view of IntelliJ, right-click on env and select Mark directory as ==> Excluded. env shoud turn red.
5. Configure your editor to use 2 spaces for indents. Disable the PEP8 warnings in IntelliJ about indents not being a multiple of 4.
6. To run tests from within IntelliJ, open up the Terminal pane and type ./scripts/test.sh. The outputs of the test run will be teed to the file test.out at the root of the project.

TensorFlow versions compatibility

GraphDef Editor is fully supported for TensorFlow versions 1.14.x and 1.15.x. For TensorFlow 2.x some transforms might not work.

To execute tests for a specific TensorFlow version run the following command from the repository root:

docker run -v ${PWD}:/v -w /v tensorflow/tensorflow:<version>[-py3] \
  bash -c "pip3 install -U pytest && pytest"

Pre 2.2.0 TensorFlow versions have -py3 suffix indicating that Python3 should be used.

To execute a specific test:

docker run -v ${PWD}:/v -w /v tensorflow/tensorflow:<version>[-py3] python -m tests.transform_test