Deal with multiple possible callables (#208)

* Return `null` when there are multiple possible callables.
* Add test to exercise call string imprecision. Based on the call string
length. See
wala/WALA#1417 (reply in thread).
* Expect the test to fail. In the past, we could add 0's to the
parameters, but since we are not enforcing the existing of the node in
the CG, we can no longer do that. Still, this test should now fail if
#207 is fixed.

com.ibm.wala.cast.python.ml.test/source/com/ibm/wala/cast/python/ml/test/TestTensorflow2Model.java

@@ -1204,6 +1204,41 @@ public void testModelCall4()
     test("tf2_test_model_call4.py", "SequentialModel.__call__", 1, 1, 3);
   }
 
+  /**
+   * Test call string imprecision as described in
+   * https://github.com/wala/WALA/discussions/1417#discussioncomment-10085680. This should fail due
+   * to https://github.com/wala/ML/issues/207.
+   */
+  @Test(expected = java.lang.AssertionError.class)
+  public void testModelCall5()
+      throws ClassHierarchyException, IllegalArgumentException, CancelException, IOException {
+    test(
+        new String[] {
+          "proj66/src/tf2_test_model_call5b.py",
+          "proj66/tf2_test_model_call5.py",
+          "proj66/tf2_test_model_call5a.py"
+        },
+        "tf2_test_model_call5.py",
+        "SequentialModel.__call__",
+        "proj66",
+        1,
+        1,
+        3);
+
+    test(
+        new String[] {
+          "proj66/src/tf2_test_model_call5b.py",
+          "proj66/tf2_test_model_call5.py",
+          "proj66/tf2_test_model_call5a.py"
+        },
+        "tf2_test_model_call5a.py",
+        "SequentialModel.__call__",
+        "proj66",
+        1,
+        1,
+        3);
+  }
+
   @Test
   public void testModelAttributes()
       throws ClassHierarchyException, IllegalArgumentException, CancelException, IOException {

com.ibm.wala.cast.python.test/data/proj66/src/__init__.py

@@ -0,0 +1 @@
+

com.ibm.wala.cast.python.test/data/proj66/src/tf2_test_model_call5b.py

@@ -0,0 +1,9 @@
+# Test https://github.com/wala/WALA/discussions/1417#discussioncomment-10085680.
+
+
+def f(m, d):
+    return m.predict(d)
+
+
+def g(m, d):
+    return f(m, d)

com.ibm.wala.cast.python.test/data/proj66/tf2_test_model_call5.py

@@ -0,0 +1,44 @@
+# Test https://github.com/wala/WALA/discussions/1417#discussioncomment-10085680.
+
+import tensorflow as tf
+from src.tf2_test_model_call5b import g
+
+# Create an override model to classify pictures
+
+
+class SequentialModel(tf.keras.Model):
+
+    def __init__(self, **kwargs):
+        super(SequentialModel, self).__init__(**kwargs)
+
+        self.flatten = tf.keras.layers.Flatten(input_shape=(28, 28))
+
+        # Add a lot of small layers
+        num_layers = 100
+        self.my_layers = [
+            tf.keras.layers.Dense(64, activation="relu") for n in range(num_layers)
+        ]
+
+        self.dropout = tf.keras.layers.Dropout(0.2)
+        self.dense_2 = tf.keras.layers.Dense(10)
+
+    def __call__(self, x):
+        print("Raffi 1")
+        x = self.flatten(x)
+
+        for layer in self.my_layers:
+            x = layer(x)
+
+        x = self.dropout(x)
+        x = self.dense_2(x)
+
+        return x
+
+    def predict(self, x):
+        return self(x)
+
+
+input_data = tf.random.uniform([20, 28, 28])
+
+model = SequentialModel()
+result = g(model, input_data)

com.ibm.wala.cast.python.test/data/proj66/tf2_test_model_call5a.py

@@ -0,0 +1,44 @@
+# Test https://github.com/wala/WALA/discussions/1417#discussioncomment-10085680.
+
+import tensorflow as tf
+from src.tf2_test_model_call5b import g
+
+# Create an override model to classify pictures
+
+
+class SequentialModel(tf.keras.Model):
+
+    def __init__(self, **kwargs):
+        super(SequentialModel, self).__init__(**kwargs)
+
+        self.flatten = tf.keras.layers.Flatten(input_shape=(28, 28))
+
+        # Add a lot of small layers
+        num_layers = 100
+        self.my_layers = [
+            tf.keras.layers.Dense(64, activation="relu") for n in range(num_layers)
+        ]
+
+        self.dropout = tf.keras.layers.Dropout(0.2)
+        self.dense_2 = tf.keras.layers.Dense(10)
+
+    def __call__(self, x):
+        print("Raffi 2")
+        x = self.flatten(x)
+
+        for layer in self.my_layers:
+            x = layer(x)
+
+        x = self.dropout(x)
+        x = self.dense_2(x)
+
+        return x
+
+    def predict(self, x):
+        return self(x)
+
+
+input_data = tf.random.uniform([20, 28, 28])
+
+model = SequentialModel()
+result = g(model, input_data)

com.ibm.wala.cast.python/source/com/ibm/wala/cast/python/ipa/callgraph/PythonInstanceMethodTrampolineTargetSelector.java

@@ -43,7 +43,9 @@
 import com.ibm.wala.util.collections.HashMapFactory;
 import com.ibm.wala.util.collections.Pair;
 import com.ibm.wala.util.intset.OrdinalSet;
+import java.util.HashSet;
 import java.util.Map;
+import java.util.Set;
 import java.util.logging.Logger;
 
 public class PythonInstanceMethodTrampolineTargetSelector<T>
@@ -87,6 +89,8 @@ protected boolean shouldProcess(CGNode caller, CallSiteReference site, IClass re
 
   @Override
   public IMethod getCalleeTarget(CGNode caller, CallSiteReference site, IClass receiver) {
+    // TODO: Callable detection may need to be moved. See https://github.com/wala/ML/issues/207. If
+    // it stays here, we should further document the receiver swapping process.
     if (isCallable(receiver)) {
       LOGGER.fine("Encountered callable.");
 
@@ -223,6 +227,9 @@ private IClass getCallable(CGNode caller, IClassHierarchy cha, PythonInvokeInstr
     PointerKey receiver = pkf.getPointerKeyForLocal(caller, call.getUse(0));
     OrdinalSet<InstanceKey> objs = builder.getPointerAnalysis().getPointsToSet(receiver);
 
+    // The set of potential callables to be returned.
+    Set<IClass> callableSet = new HashSet<>();
+
     for (InstanceKey o : objs) {
       AllocationSiteInNode instanceKey = getAllocationSiteInNode(o);
       if (instanceKey != null) {
@@ -254,10 +261,22 @@ private IClass getCallable(CGNode caller, IClassHierarchy cha, PythonInvokeInstr
             LOGGER.info("Applying callable workaround for https://github.com/wala/ML/issues/118.");
         }
 
-        if (callable != null) return callable;
+        callableSet.add(callable);
       }
     }
 
+    // if there's only one possible option.
+    if (callableSet.size() == 1) {
+      IClass callable = callableSet.iterator().next();
+      assert callable != null : "Callable should be non-null.";
+      return callable;
+    }
+
+    // if we have multiple candidates.
+    if (callableSet.size() > 1)
+      // we cannot accurately select one.
+      LOGGER.warning("Multiple (" + callableSet.size() + ") callable targets found.");
+
     return null;
   }