Deal with multiple possible callables (#121)

* 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 wala#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/.pydevproject

@@ -19,6 +19,7 @@
         <path>/${PROJECT_DIR_NAME}/data/proj35</path>
         <path>/${PROJECT_DIR_NAME}/data/proj37</path>
         <path>/${PROJECT_DIR_NAME}/data/proj45</path>
+        <path>/${PROJECT_DIR_NAME}/data/proj66</path>
     </pydev_pathproperty>
 
 

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

@@ -43,6 +43,7 @@
 import com.ibm.wala.util.collections.HashMapFactory;
 import com.ibm.wala.util.collections.Pair;
 import com.ibm.wala.util.intset.OrdinalSet;
+import java.util.HashMap;
 import java.util.Map;
 import java.util.logging.Logger;
 
@@ -223,6 +224,8 @@ private IClass getCallable(CGNode caller, IClassHierarchy cha, PythonInvokeInstr
     PointerKey receiver = pkf.getPointerKeyForLocal(caller, call.getUse(0));
     OrdinalSet<InstanceKey> objs = builder.getPointerAnalysis().getPointsToSet(receiver);
 
+    Map<InstanceKey, IClass> instanceToCallable = new HashMap<>();
+
     for (InstanceKey o : objs) {
       AllocationSiteInNode instanceKey = getAllocationSiteInNode(o);
       if (instanceKey != null) {
@@ -254,10 +257,29 @@ 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;
+        if (callable != null) {
+          if (instanceToCallable.containsKey(instanceKey))
+            throw new IllegalStateException("Exisitng mapping found for: " + instanceKey);
+
+          IClass previousValue = instanceToCallable.put(instanceKey, callable);
+          assert previousValue == null : "Not expecting a previous mapping.";
+        }
       }
     }
 
+    // if there's only one possible option.
+    if (instanceToCallable.values().size() == 1) {
+      IClass callable = instanceToCallable.values().iterator().next();
+      assert callable != null : "Callable should be non-null.";
+      return callable;
+    }
+
+    // if we have multiple candidates.
+    if (instanceToCallable.values().size() > 1)
+      // we cannot accurately select one.
+      LOGGER.warning(
+          "Multiple (" + instanceToCallable.values().size() + ") callable targets found.");
+
     return null;
   }