[EncoderDecoderConfig] automatically set decoder config to decoder (#4809)

* automatically set decoder config to decoder

* add more tests

src/transformers/configuration_encoder_decoder.py

@@ -85,6 +85,9 @@ class EncoderDecoderConfig(PretrainedConfig):
         Returns:
             :class:`EncoderDecoderConfig`: An instance of a configuration object
         """
+        logger.info("Set `config.is_decoder=True` for decoder_config")
+        decoder_config.is_decoder = True
+
         return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict())
 
     def to_dict(self):

tests/test_modeling_encoder_decoder.py

@@ -27,7 +27,7 @@ from .utils import require_torch, slow, torch_device
 
 
 if is_torch_available():
-    from transformers import BertModel, BertForMaskedLM, EncoderDecoderModel
+    from transformers import BertModel, BertForMaskedLM, EncoderDecoderModel, EncoderDecoderConfig
     import numpy as np
     import torch
 
@@ -74,6 +74,36 @@ class EncoderDecoderModelTest(unittest.TestCase):
             "labels": decoder_token_labels,
         }
 
+    def create_and_check_bert_encoder_decoder_model_from_pretrained_configs(
+        self,
+        config,
+        input_ids,
+        attention_mask,
+        encoder_hidden_states,
+        decoder_config,
+        decoder_input_ids,
+        decoder_attention_mask,
+        **kwargs
+    ):
+        encoder_decoder_config = EncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
+        self.assertTrue(encoder_decoder_config.decoder.is_decoder)
+
+        enc_dec_model = EncoderDecoderModel(encoder_decoder_config)
+        enc_dec_model.to(torch_device)
+        enc_dec_model.eval()
+
+        self.assertTrue(enc_dec_model.config.is_encoder_decoder)
+
+        outputs_encoder_decoder = enc_dec_model(
+            input_ids=input_ids,
+            decoder_input_ids=decoder_input_ids,
+            attention_mask=attention_mask,
+            decoder_attention_mask=decoder_attention_mask,
+        )
+
+        self.assertEqual(outputs_encoder_decoder[0].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)))
+        self.assertEqual(outputs_encoder_decoder[1].shape, (input_ids.shape + (config.hidden_size,)))
+
     def create_and_check_bert_encoder_decoder_model(
         self,
         config,
@@ -88,6 +118,8 @@ class EncoderDecoderModelTest(unittest.TestCase):
         encoder_model = BertModel(config)
         decoder_model = BertForMaskedLM(decoder_config)
         enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
+        self.assertTrue(enc_dec_model.config.decoder.is_decoder)
+        self.assertTrue(enc_dec_model.config.is_encoder_decoder)
         enc_dec_model.to(torch_device)
         outputs_encoder_decoder = enc_dec_model(
             input_ids=input_ids,
@@ -304,6 +336,10 @@ class EncoderDecoderModelTest(unittest.TestCase):
         input_ids_dict = self.prepare_config_and_inputs_bert()
         self.create_and_check_bert_encoder_decoder_model(**input_ids_dict)
 
+    def test_bert_encoder_decoder_model_from_pretrained_configs(self):
+        input_ids_dict = self.prepare_config_and_inputs_bert()
+        self.create_and_check_bert_encoder_decoder_model_from_pretrained_configs(**input_ids_dict)
+
     def test_bert_encoder_decoder_model_from_pretrained(self):
         input_ids_dict = self.prepare_config_and_inputs_bert()
         self.create_and_check_bert_encoder_decoder_model_from_pretrained(**input_ids_dict)