[Bart/Memory] don't create lm_head (#3323)

* delete lm_head, skips weight tying
* Fixed s3

tests/test_modeling_bart.py

@@ -113,7 +113,8 @@ class BARTModelTest(ModelTesterMixin, unittest.TestCase):
     test_pruning = False
     test_torchscript = False
     test_head_masking = False
-    test_resize_embeddings = False  # This requires inputs_dict['input_ids']
+    test_resize_embeddings = True  # This requires inputs_dict['input_ids']
+    test_missing_keys = False  # because BartForConditionalGeneration and BartModel now have identical state_dict
 
     def setUp(self):
         self.model_tester = ModelTester(self)
@@ -371,6 +372,22 @@ class BartHeadTests(unittest.TestCase):
         )
         self.assertTrue(torch.eq(decoder_attn_mask_no_padding_no_causal_mask, 0).all())
 
+    def test_resize_tokens_embeddings_more(self):
+        config, input_ids, _ = self._get_config_and_data()
+
+        def _get_embs(m):
+            return (m.get_input_embeddings().weight.data.clone(), m.get_output_embeddings().weight.data.clone())
+
+        model = BartForConditionalGeneration(config).eval().to(torch_device)
+        input, output = _get_embs(model)
+        self.assertTrue(torch.eq(input, output).all())
+        new_vocab_size = 45
+        model.resize_token_embeddings(new_vocab_size)
+        input_new, output_new = _get_embs(model)
+        self.assertEqual(input_new.shape, (new_vocab_size, config.d_model))
+        self.assertEqual(output_new.shape, (new_vocab_size, config.d_model))
+        self.assertTrue(torch.eq(input_new, output_new).all())
+
 
 def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
     """If tensors not close, or a and b arent both tensors, raise a nice Assertion error."""