New API and tests for missing operator overloading (#137)

- New API and new operator overloading issue (#81)
- New gradient test.
- Reduce epochs in test.
  - Easier to run.

com.ibm.wala.cast.python.ml.test/source/com/ibm/wala/cast/python/ml/test/TestTensorflowModel.java

@@ -217,7 +217,11 @@ public void testTf2()
     testTf2("tf2_test_dataset10.py", "add", 2, 2, 2, 3);
     testTf2("tf2_test_tensor_list.py", "add", 2, 2, 2, 3);
     testTf2("tf2_test_tensor_list2.py", "add", 0, 0);
-    testTf2("tf2_test_tensor_list3.py", "add", 0, 0);
+    testTf2(
+        "tf2_test_tensor_list3.py",
+        "add",
+        0,
+        0); // NOTE: Change to 2, 2, 2, 3 once https://github.com/wala/ML/issues/136 is fixed.
     testTf2("tf2_test_tensor_list4.py", "add", 0, 0);
     testTf2("tf2_test_tensor_list5.py", "add", 0, 0);
     testTf2("tf2_test_model_call.py", "SequentialModel.__call__", 1, 1, 3);
@@ -234,10 +238,10 @@ public void testTf2()
         "neural_network.py",
         "cross_entropy_loss",
         1,
-        4,
+        8,
         3); // NOTE: Change to 2 tensor parameters once https://github.com/wala/ML/issues/127 is
     // fixed. Values 2 and 3 will correspond to the tensor parameters.
-    testTf2("neural_network.py", "run_optimization", 2, 2, 2, 3);
+    testTf2("neural_network.py", "run_optimization", 2, 3, 2, 3);
     testTf2(
         "neural_network.py",
         "accuracy",
@@ -259,10 +263,19 @@ public void testTf2()
     testTf2("tf2_test_add5.py", "f", 1, 1, 2);
     testTf2("tf2_test_add6.py", "f", 1, 1, 2);
     testTf2("multigpu_training.py", "run_optimization", 2, 4, 2, 3);
+    testTf2(
+        "multigpu_training.py",
+        "average_gradients",
+        0,
+        0); // NOTE: Change to 1, 1, 2 once https://github.com/wala/ML/issues/136 is fixed.
     testTf2("tf2_test_reduce_mean.py", "f", 1, 1, 2);
     testTf2("tf2_test_reduce_mean.py", "g", 1, 1, 2);
     testTf2("tf2_test_reduce_mean.py", "h", 1, 1, 2);
     testTf2("tf2_test_gradient.py", "f", 1, 1, 2);
+    testTf2("tf2_test_gradient2.py", "f", 1, 1, 2);
+    testTf2("tf2_test_multiply.py", "f", 1, 1, 2);
+    testTf2("tf2_test_multiply2.py", "f", 1, 1, 2);
+    testTf2("tf2_test_sparse_softmax_cross_entropy_with_logits.py", "f", 1, 1, 2);
   }
 
   private void testTf2(

com.ibm.wala.cast.python.ml/data/tensorflow.xml

@@ -75,12 +75,17 @@
         <putfield class="LRoot" field="conv3d" fieldType="LRoot" ref="nn" value="conv3d" />
         <new def="softmax" class="Ltensorflow/functions/softmax" />
         <putfield class="LRoot" field="softmax" fieldType="LRoot" ref="nn" value="softmax" />
+        <new def="sparse_softmax_cross_entropy_with_logits" class="Ltensorflow/functions/sparse_softmax_cross_entropy_with_logits" />
+        <putfield class="LRoot" field="sparse_softmax_cross_entropy_with_logits" fieldType="LRoot" ref="nn" value="sparse_softmax_cross_entropy_with_logits" />
         <new def="sigmoid" class="Ltensorflow/math/sigmoid" />
         <putfield class="LRoot" field="sigmoid" fieldType="LRoot" ref="nn" value="sigmoid" />
         <putfield class="LRoot" field="sigmoid" fieldType="LRoot" ref="math" value="sigmoid" />
         <new def="add" class="Ltensorflow/math/add" />
         <putfield class="LRoot" field="add" fieldType="LRoot" ref="x" value="add" />
         <putfield class="LRoot" field="add" fieldType="LRoot" ref="math" value="add" />
+        <new def="multiply" class="Ltensorflow/math/multiply" />
+        <putfield class="LRoot" field="multiply" fieldType="LRoot" ref="x" value="multiply" />
+        <putfield class="LRoot" field="multiply" fieldType="LRoot" ref="math" value="multiply" />
         <new def="reduce_mean" class="Ltensorflow/math/reduce_mean" />
         <putfield class="LRoot" field="reduce_mean" fieldType="LRoot" ref="x" value="reduce_mean" />
         <putfield class="LRoot" field="reduce_mean" fieldType="LRoot" ref="math" value="reduce_mean" />
@@ -285,6 +290,18 @@
           <return value="xx" />
         </method>
       </class>
+      <class name="multiply" allocatable="true">
+        <!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/multiply -->
+        <method name="read_data" descriptor="()LRoot;">
+          <new def="x" class="Ltensorflow/math/multiply" />
+          <return value="x" />
+        </method>
+        <method name="do" descriptor="()LRoot;" numArgs="4" paramNames="self x y name">
+          <!-- Even though tf.multiply() isn't a tensor "generator," it can convert its non-tensor arguments to tensors. -->
+          <call class="LRoot" name="read_data" descriptor="()LRoot;" type="virtual" arg0="arg0" def="xx" />
+          <return value="xx" />
+        </method>
+      </class>
       <class name="reduce_mean" allocatable="true">
         <!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/reduce_mean -->
         <method name="do" descriptor="()LRoot;" numArgs="5" paramNames="self input_tensor axis keepdims name">
@@ -682,6 +699,12 @@
           <return value="logits" />
         </method>
       </class>
+      <class name="sparse_softmax_cross_entropy_with_logits" allocatable="true">
+        <!-- https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/nn/sparse_softmax_cross_entropy_with_logits -->
+        <method name="do" descriptor="()LRoot;" numArgs="4" paramNames="self labels logits name">
+          <return value="labels" />
+        </method>
+      </class>
     </package>
     <package name="tensorflow/estimator">
       <class name="Estimator" allocatable="true">

com.ibm.wala.cast.python.test/data/multigpu_training.py

@@ -42,11 +42,11 @@
 
 # Training parameters.
 learning_rate = 0.001
-training_steps = 1000
+training_steps = 1
 # Split batch size equally between GPUs.
 # Note: Reduce batch size if you encounter OOM Errors.
 batch_size = 1024 * num_gpus
-display_step = 20
+display_step = 1
 
 # Network parameters.
 conv1_filters = 64  # number of filters for 1st conv layer.

com.ibm.wala.cast.python.test/data/tf2_test_gradient2.py

@@ -0,0 +1,14 @@
+# From https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/GradientTape#gradient.
+
+import tensorflow as tf
+
+
+def f(a):
+    pass
+
+
+x = tf.ragged.constant([[1.0, 2.0], [3.0]])
+with tf.GradientTape() as g:
+    g.watch(x)
+    y = tf.multiply(x, x)
+f(g.gradient(y, x))

com.ibm.wala.cast.python.test/data/tf2_test_multiply.py

@@ -0,0 +1,11 @@
+# From https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/math/multiply#for_example/
+
+import tensorflow as tf
+
+
+def f(a):
+    pass
+
+
+x = tf.constant(([1, 2, 3, 4]))
+f(tf.math.multiply(x, x))

com.ibm.wala.cast.python.test/data/tf2_test_multiply2.py

@@ -0,0 +1,10 @@
+# From https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/math/multiply#for_example/
+
+import tensorflow as tf
+
+
+def f(a):
+    pass
+
+
+f(tf.math.multiply(7, 6))

com.ibm.wala.cast.python.test/data/tf2_test_sparse_softmax_cross_entropy_with_logits.py

@@ -0,0 +1,14 @@
+# from https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/nn/sparse_softmax_cross_entropy_with_logits
+
+import tensorflow as tf
+
+
+def f(a):
+    pass
+
+
+logits = tf.constant(
+    [[2.0, -5.0, 0.5, -0.1], [0.0, 0.0, 1.9, 1.4], [-100.0, 100.0, -100.0, -100.0]]
+)
+labels = tf.constant([0, 3, 1])
+f(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits.numpy()))

com.ibm.wala.cast.python.test/data/tf2_test_tensor_list3.py

@@ -1,3 +1,5 @@
+# Test https://github.com/wala/ML/issues/136.
+
 import tensorflow as tf