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Inconsistent Jython versions #33

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khatchad opened this issue May 3, 2023 · 0 comments · Fixed by #34
Closed

Inconsistent Jython versions #33

khatchad opened this issue May 3, 2023 · 0 comments · Fixed by #34

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@khatchad
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khatchad commented May 3, 2023

Commit b4b6e8e says to use Python 2 for now, but it references jython3:

ML/com.ibm.wala.cast.python.ml/pom.xml

Line 12 in bb086b5

<artifactId>jython3</artifactId>

That results in an inconsistency with the follow:

ML/com.ibm.wala.cast.python.ml/pom.xml

Line 46 in bb086b5

<artifactId>com.ibm.wala.cast.python.jython</artifactId>

If we are using Jython 3, I believe we should be using com.ibm.wala.cast.python.jython3 instead of com.ibm.wala.cast.python.jython.

What happens here as a result is that we load up Jython 3 code that calls methods that don't exist in Jython 2, thus throwing a NoSuchMethod exception. An example is the PythonTree class, which has the method getLineno() in Jython (Python 2) but getLine() in Jython 3 (Python 3).
khatchad added a commit to khatchad/wala-python that referenced this issue May 3, 2023
@khatchad
Fix wala#33.
e4ae405 
Make consistent by using Jython 3 as a dependency.
@khatchad khatchad mentioned this issue May 3, 2023
Merged
khatchad added a commit to ponder-lab/ML that referenced this issue May 3, 2023
@khatchad
Fix wala#33.
6a10efb
msridhar pushed a commit that referenced this issue May 3, 2023
@khatchad
Fix #33. (#34)
70d5380 
Make consistent by using Jython 3 as a dependency.
khatchad added a commit to ponder-lab/ML that referenced this issue May 23, 2023
@khatchad
Issue 4 (#14)
e4a073f 
* Address wala#28. (#29)

See https://maven.apache.org/plugins/maven-shade-plugin/examples/attached-artifact.html for reference.

* Remove unused footnote. (#31)

Forgot to remove this one with the last update.

* Lowercase parent pom artifact ID. (#32)

Fixes wala#30.

* Fix wala#33. (#34)

Make consistent by using Jython 3 as a dependency.

* Metadata, build script, and warnings (#35)

* Remove obsolete bundles.

These are being used as JARs now.

* Export missing packages.

Since wala/ML is now using JAR packaging, this probably doesn't matter.
However, it fixes the warnings. And, if it ever gets OSGi component
packaged again, we can use it.

* Fix build.properities file.

Source and data directiories don't exist for these subprojects.

* Fix more warnings.

* Use verify to run the tests.

* Export more packages.

* Fix generics hiding warning.

* Fix Jython and Jython3 test directory metadata.

* Add GH actions file (#40)

* Add GH actions file.

* Remove optional dependency analysis.

Was failing.

* Fix path.

* Switch Jython repos.

* Revert "Switch Jython repos."

This reverts commit 20052de.

* Print out the stack trace.

* Switch to Jython3 for testing ML.

* Revert "Print out the stack trace."

This reverts commit 8fb7951.

* Update run.sh

Fix broken resolution.

* Ignore failing test.

Reinstate once wala#42 is fixed.

* Revert "Work around https://github.com/ponder-lab/ML/issues/4."

This reverts commit 0e5c334.
@khatchad khatchad mentioned this issue May 24, 2023
Open
@khatchad khatchad mentioned this issue Jun 28, 2023
Closed
@tatianacv tatianacv mentioned this issue Aug 3, 2023
Merged
@tatianacv tatianacv mentioned this issue Aug 10, 2023
Merged
khatchad pushed a commit that referenced this issue Aug 14, 2023
@tatianacv
Adding initial support for decorator tf.function (#86)
7aaa4dc 
Currently, we are using jython2 in `com.ibm.wala.cast.python.ml.test`,
where we don't encounter decorator problems when running the tests. But
when we use jython3, we do encounter problems with decorator
`tf.function`. Therefore we are adding initial support for the decorator
`tf.function` in `tensorflow.xml`. Initial support for `tf.function`
because after this change, in jython3, can now process the decorator
that has parenthesis e.g. `tf.function()` or `tf.function(...)`.

Refer to #33 for the issue with
inconsistent jython versions.

After this change we see:
Test code with parenthesis: 

```python
import tensorflow as tf

@tf.function()
def returned(a):
  return a

a = tf.range(5)
b = returned(a)
```
The analysis of the test with parenthesis:
```
answer:
[Node: <Code body of function Lscript tf2_testing_decorator.py> Context: CallStringContext: [ com.ibm.wala.FakeRootClass.fakeRootMethod()V@2 ], v251][{[D:Symbolic,n, D:Compound,[D:Constant,28, D:Constant,28]] of pixel}]
[Ret-V:Node: synthetic < PythonLoader, Ltensorflow/functions/range, do()LRoot; > Context: CallStringContext: [ script tf2_testing_decorator.py.do()LRoot;@96 ]][{[D:Symbolic,n, D:Compound,[D:Constant,28, D:Constant,28]] of pixel}]
[Node: synthetic < PythonLoader, Ltensorflow/functions/range, do()LRoot; > Context: CallStringContext: [ script tf2_testing_decorator.py.do()LRoot;@96 ], v6][{[D:Symbolic,n, D:Compound,[D:Constant,28, D:Constant,28]] of pixel}]
[Node: <Code body of function Lscript tf2_testing_decorator.py/returned> Context: CallStringContext: [ script tf2_testing_decorator.py.do()LRoot;@98 ], v2][{[D:Symbolic,n, D:Compound,[D:Constant,28, D:Constant,28]] of pixel}]
[Node: <Code body of function Lscript tf2_testing_decorator.py> Context: CallStringContext: [ com.ibm.wala.FakeRootClass.fakeRootMethod()V@2 ], v257][{[D:Symbolic,n, D:Compound,[D:Constant,28, D:Constant,28]] of pixel}]
[Ret-V:Node: <Code body of function Lscript tf2_testing_decorator.py/returned> Context: CallStringContext: [ script tf2_testing_decorator.py.do()LRoot;@98 ]][{[D:Symbolic,n, D:Compound,[D:Constant,28, D:Constant,28]] of pixel}]
```

Test code with parenthesis: 

```python
import tensorflow as tf

@tf.function
def returned(a):
  return a

a = tf.range(5)
b = returned(a)
```

The analysis of the test without parenthesis:
```
answer:
[Node: <Code body of function Lscript tf2_testing_decorator.py> Context: CallStringContext: [ com.ibm.wala.FakeRootClass.fakeRootMethod()V@2 ], v249][{[D:Symbolic,n, D:Compound,[D:Constant,28, D:Constant,28]] of pixel}]
[Ret-V:Node: synthetic < PythonLoader, Ltensorflow/functions/range, do()LRoot; > Context: CallStringContext: [ script tf2_testing_decorator.py.do()LRoot;@95 ]][{[D:Symbolic,n, D:Compound,[D:Constant,28, D:Constant,28]] of pixel}]
[Node: synthetic < PythonLoader, Ltensorflow/functions/range, do()LRoot; > Context: CallStringContext: [ script tf2_testing_decorator.py.do()LRoot;@95 ], v6][{[D:Symbolic,n, D:Compound,[D:Constant,28, D:Constant,28]] of pixel}]
[Node: synthetic < PythonLoader, Ltensorflow/class/Function, do()LRoot; > Context: CallStringContext: [ script tf2_testing_decorator.py.do()LRoot;@97 ], v2][{[D:Symbolic,n, D:Compound,[D:Constant,28, D:Constant,28]] of pixel}]
[Node: <Code body of function Lscript tf2_testing_decorator.py> Context: CallStringContext: [ com.ibm.wala.FakeRootClass.fakeRootMethod()V@2 ], v255][{[D:Symbolic,n, D:Compound,[D:Constant,28, D:Constant,28]] of pixel}]
[Ret-V:Node: synthetic < PythonLoader, Ltensorflow/class/Function, do()LRoot; > Context: CallStringContext: [ script tf2_testing_decorator.py.do()LRoot;@97 ]][{[D:Symbolic,n, D:Compound,[D:Constant,28, D:Constant,28]] of pixel}]
```

We see that with the decorator, `expr` is classified as a `CALL`, and
without it is classified as an `Attribute`.
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Make consistent by using Jython 3 as a dependency khatchad/wala-python
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