[EncoderDecoder] Fix initialization and save/load bug (#4680)

* fix bug

* add more tests

src/transformers/modeling_encoder_decoder.py

@@ -35,6 +35,7 @@ class EncoderDecoderModel(PreTrainedModel):
         class method for the encoder and `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)` class method for the decoder.
     """
     config_class = EncoderDecoderConfig
+    base_model_prefix = "encoder_decoder"
 
     def __init__(
         self,
@@ -158,12 +159,26 @@ class EncoderDecoderModel(PreTrainedModel):
             ), "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has to be defined"
             from .modeling_auto import AutoModelWithLMHead
 
+            if "config" not in kwargs_decoder:
+                from transformers import AutoConfig
+
+                decoder_config = AutoConfig.from_pretrained(decoder_pretrained_model_name_or_path)
+                if decoder_config.is_decoder is False:
+                    logger.info(
+                        f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers."
+                    )
+                    decoder_config.is_decoder = True
+
+                kwargs_decoder["config"] = decoder_config
+
+            if kwargs_decoder["config"].is_decoder is False:
+                logger.warning(
+                    f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, make sure that the attribute `is_decoder` of `decoder_config` passed to `.from_encoder_decoder_pretrained(...)` is set to `True` or do not pass a `decoder_config` to `.from_encoder_decoder_pretrained(...)`"
+                )
+
             decoder = AutoModelWithLMHead.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
-        decoder.config.is_decoder = True
 
-        model = cls(encoder=encoder, decoder=decoder)
-
-        return model
+        return cls(encoder=encoder, decoder=decoder)
 
     def forward(
         self,

tests/test_modeling_encoder_decoder.py

@@ -22,6 +22,7 @@ from transformers import is_torch_available
 # TODO(PVP): this line reruns all the tests in BertModelTest; not sure whether this can be prevented
 # for now only run module with pytest tests/test_modeling_encoder_decoder.py::EncoderDecoderModelTest
 from .test_modeling_bert import BertModelTester
+from .test_modeling_common import ids_tensor
 from .utils import require_torch, slow, torch_device
 
 
@@ -331,3 +332,33 @@ class EncoderDecoderModelTest(unittest.TestCase):
     def test_real_bert_model_from_pretrained(self):
         model = EncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-uncased", "bert-base-uncased")
         self.assertIsNotNone(model)
+
+    @slow
+    def test_real_bert_model_from_pretrained_has_cross_attention(self):
+        model = EncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-uncased", "bert-base-uncased")
+        self.assertTrue(hasattr(model.decoder.bert.encoder.layer[0], "crossattention"))
+
+    @slow
+    def test_real_bert_model_save_load_from_pretrained(self):
+        model_2 = EncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-uncased", "bert-base-uncased")
+        model_2.to(torch_device)
+        input_ids = ids_tensor([13, 5], model_2.config.encoder.vocab_size)
+        decoder_input_ids = ids_tensor([13, 1], model_2.config.encoder.vocab_size)
+        attention_mask = ids_tensor([13, 5], vocab_size=2)
+        with torch.no_grad():
+            outputs = model_2(input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask,)
+            out_2 = outputs[0].cpu().numpy()
+            out_2[np.isnan(out_2)] = 0
+
+            with tempfile.TemporaryDirectory() as tmp_dirname:
+                model_2.save_pretrained(tmp_dirname)
+                model_1 = EncoderDecoderModel.from_pretrained(tmp_dirname)
+                model_1.to(torch_device)
+
+                after_outputs = model_1(
+                    input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask,
+                )
+                out_1 = after_outputs[0].cpu().numpy()
+                out_1[np.isnan(out_1)] = 0
+                max_diff = np.amax(np.abs(out_1 - out_2))
+                self.assertLessEqual(max_diff, 1e-5)