STT-tensorflow/tensorflow/tools/compatibility
Ruoxin Sang 39a2286a0e Graduate TPUStrategy from experimental.
RELNOTES=Make TPUStrategy symbol non experimental.
PiperOrigin-RevId: 317482072
Change-Id: I8bf596729699cb02fa275dfb63855c2dc68c1d42
2020-06-20 13:10:50 -07:00
..
testdata Remove test to un-red nightly builds as suggested in b/151378056 2020-03-19 09:26:43 -07:00
update
BUILD
README.md
all_renames_v2.py
all_renames_v2_test.py
ast_edits.py Added an `--upgrade_compat_v1_import` flag to the upgrade script that allows it to upgrade `import tensorflow.compat.v1 as tf`imports to `import tensorflow as tf` imports. Note that this flag does not upgrade tf.compat.v1 when imported under other aliases, such as `import tensorflow.compat.v1 as tfv1` 2020-04-01 21:31:15 -07:00
ast_edits_test.py
ipynb.py
module_deprecations_v2.py
renames_v2.py Add Registration for non-stateful scatter_nd_min and scatter_nd_max. 2020-06-03 11:05:54 -07:00
reorders_v2.py
tf_upgrade.py
tf_upgrade_test.py
tf_upgrade_v2.py Graduate TPUStrategy from experimental. 2020-06-20 13:10:50 -07:00
tf_upgrade_v2_main.py Added an `--upgrade_compat_v1_import` flag to the upgrade script that allows it to upgrade `import tensorflow.compat.v1 as tf`imports to `import tensorflow as tf` imports. Note that this flag does not upgrade tf.compat.v1 when imported under other aliases, such as `import tensorflow.compat.v1 as tfv1` 2020-04-01 21:31:15 -07:00
tf_upgrade_v2_safety.py
tf_upgrade_v2_safety_test.py
tf_upgrade_v2_test.py Graduate TPUStrategy from experimental. 2020-06-20 13:10:50 -07:00

README.md

TensorFlow Python API Upgrade Utility

This tool allows you to upgrade your existing TensorFlow Python scripts, specifically:

  • tf_upgrade_v2.py: Upgrade code from TensorFlow 1.x to TensorFlow 2.0 preview.
  • tf_upgrade.py: Upgrade code to TensorFlow 1.0 from TensorFlow 0.11.

Running the script from pip package

First, install TensorFlow pip package*. See https://www.tensorflow.org/install/pip.

Upgrade script can be run on a single Python file:

tf_upgrade_v2 --infile foo.py --outfile foo-upgraded.py

It will print a list of errors it finds that it can't fix. You can also run it on a directory tree:

# upgrade the .py files and copy all the other files to the outtree
tf_upgrade_v2 --intree coolcode --outtree coolcode-upgraded

# just upgrade the .py files
tf_upgrade_v2 --intree coolcode --outtree coolcode-upgraded --copyotherfiles False

*Note: tf_upgrade_v2 is installed automatically as a script by the pip install after TensorFlow 1.12.

Report

The script will also dump out a report e.g. which will detail changes e.g.:

'tensorflow/tools/compatibility/testdata/test_file_v1_12.py' Line 65
--------------------------------------------------------------------------------

Added keyword 'input' to reordered function 'tf.argmax'
Renamed keyword argument from 'dimension' to 'axis'

    Old:         tf.argmax([[1, 3, 2]], dimension=0)
                                        ~~~~~~~~~~
    New:         tf.argmax(input=[[1, 3, 2]], axis=0)

Caveats

  • Don't update parts of your code manually before running this script. In particular, functions that have had reordered arguments like tf.argmax or tf.batch_to_space will cause the script to incorrectly add keyword arguments that mismap arguments.

  • This script wouldn't actually reorder arguments. Instead, the script will add keyword arguments to functions that had their arguments reordered.

  • The script assumes that tensorflow is imported using import tensorflow as tf.

  • Note for upgrading to 2.0: Check out tf2up.ml for a convenient tool to upgrade Jupyter notebooks and Python files in a GitHub repository.

  • Note for upgrading to 1.0: There are some syntaxes that are not handleable with this script as this script was designed to use only standard python packages. If the script fails with "A necessary keyword argument failed to be inserted." or "Failed to find keyword lexicographically. Fix manually.", you can try @machrisaa's fork of this script. @machrisaa has used the RedBaron Python refactoring engine which is able to localize syntactic elements more reliably than the built-in ast module this script is based upon. Note that the alternative script is not available for TensorFlow 2.0 upgrade.