[RAG, Bart] Align RAG, Bart cache with T5 and other models of transformers (#9098)

* fix rag

* fix slow test

* fix past in bart

tests/test_modeling_rag.py

@@ -535,7 +535,6 @@ class RagDPRBartTest(RagTestMixin, unittest.TestCase):
             n_docs=self.n_docs,
             retrieval_vector_size=self.retrieval_vector_size,
             max_combined_length=self.max_combined_length,
-            use_cache=False,
         )
 
         return {
@@ -565,7 +564,6 @@ class RagDPRT5Test(RagTestMixin, unittest.TestCase):
             n_docs=self.n_docs,
             retrieval_vector_size=self.retrieval_vector_size,
             max_combined_length=self.max_combined_length,
-            use_cache=False,
         )
 
         return {
@@ -758,8 +756,8 @@ class RagModelIntegrationTests(unittest.TestCase):
             generator_tokenizer=rag_decoder_tokenizer,
         )
 
-        rag_token = self.sequence_model
-        rag_token.set_retriever(rag_retriever)
+        rag_sequence = self.sequence_model
+        rag_sequence.set_retriever(rag_retriever)
 
         input_ids = rag_question_encoder_tokenizer(
             "who sings does he love me with reba", return_tensors="pt"
@@ -767,9 +765,9 @@ class RagModelIntegrationTests(unittest.TestCase):
 
         input_ids = input_ids.to(torch_device)
 
-        output_ids = rag_token.generate(
+        output_ids = rag_sequence.generate(
             input_ids,
-            decoder_start_token_id=rag_token.generator.config.decoder_start_token_id,
+            decoder_start_token_id=rag_sequence.generator.config.decoder_start_token_id,
             num_beams=2,
             num_return_sequences=2,
         )
@@ -810,7 +808,7 @@ class RagModelIntegrationTests(unittest.TestCase):
         retriever = RagRetriever.from_pretrained(
             "facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True
         )
-        rag_sequence = RagTokenForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever).to(
+        rag_sequence = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever).to(
             torch_device
         )
 
@@ -844,9 +842,9 @@ class RagModelIntegrationTests(unittest.TestCase):
             " walls of the abdomen",
             " spodumene",
             " obama",
-            " grainger's compound",
+            " new orleans",
             " japan",
-            " old trafford stadium",
+            " old trafford",
         ]
         self.assertListEqual(outputs, EXPECTED_OUTPUTS)