Check to see if the models have the same results when in eval mode (pt) or when training=False (tf)

pytorch_transformers/tests/modeling_common_test.py

@@ -68,6 +68,16 @@ class CommonTestCases:
                         self.assertIn(param.data.mean().item(), [0.0, 1.0],
                         msg="Parameter {} of model {} seems not properly initialized".format(name, model_class))
 
+        def test_determinism(self):
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+            for model_class in self.all_model_classes:
+                model = model_class(config)
+                model.eval()
+                first, second = model(inputs_dict["input_ids"])[0], model(inputs_dict["input_ids"])[0]
+                self.assertEqual(first.ne(second).sum().item(), 0)
+
+
         def test_attention_outputs(self):
             config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 

pytorch_transformers/tests/modeling_tf_common_test.py

@@ -298,6 +298,14 @@ class TFCommonTestCases:
             #     self.assertGreater(len(params_not_tied), len(params_tied))
             #     self.assertEqual(len(params_tied_2), len(params_tied))
 
+        def test_determinism(self):
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+            for model_class in self.all_model_classes:
+                model = model_class(config)
+                first, second = model(inputs_dict, training=False)[0], model(inputs_dict, training=False)[0]
+                self.assertTrue(tf.math.equal(first, second).numpy().all())
+
 
 def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None):
     """Creates a random int32 tensor of the shape within the vocab size."""