Adding new encoder_no_repeat_ngram_size to generate. (#9984)

Adding new `encoder_no_repeat_ngram_size` to `generate`.

Blenderbot results seemed off compared to original ParlAI script:
`https://parl.ai/projects/recipes/`. Notably the model seems
to repeat a lot what was said during the conversation.

The actual problem was that `no_repeat_ngram_size` actually applies
to the `encoder_input_ids` but HF's `no_repeat_ngram_size` applies
to the previously generated ids (within the decoder). The history
conversation of blenderbot is within the `encoder` part so that
explains why HF's implementation had the repetitions.

This fix was focused on blenderbot *not* small and added tests
for those because they are quite different in configuration.

This change includes:

- Adding a new EncoderNoRepeatLogitProcessor.
- Adding 1 new arg to `generate` (`encoder_no_repeat_ngram_size`)
- Adding 1 new config parameter `encoder_no_repeat_ngram_size`.
- Adding 2 tests, one for the pipeline (high level, inputs exhibited
repeat behavior, one low level for EncoderNoRepeatLogitProcessor)
- Factored NoRepeatLogitProcessor so that logic could be reused.

Further work:

- Blenderbot conversational pipeline still does not behave correctly
 as they way input is prepared within the pipeline is still incorrect
(follow up PR)
- Blenderbot allows the bot to have personas, which is done by
prepending "your personna: XXXX" to the input, this could be explored
too in a follow up PR.

@patrickvonplaten
@LysandreJik

* Update src/transformers/generation_logits_process.py

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/transformers/generation_utils.py

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/transformers/generation_utils.py

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/transformers/configuration_utils.py

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Doc quality.

* Fixing test.

* Last fixes.

* Fixing to account for batch_size.

* Update src/transformers/configuration_utils.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/generation_utils.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

tests/test_pipelines_conversational.py

@@ -276,6 +276,47 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
         self.assertEqual(result.past_user_inputs[1], "Is it an action movie?")
         self.assertEqual(result.generated_responses[1], "It's a comedy.")
 
+    @require_torch
+    @slow
+    def test_integration_torch_conversation_blenderbot_400M(self):
+        tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
+        model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-400M-distill")
+        nlp = ConversationalPipeline(model=model, tokenizer=tokenizer)
+
+        conversation_1 = Conversation("hello")
+        result = nlp(
+            conversation_1,
+        )
+        self.assertEqual(
+            result.generated_responses[0],
+            # ParlAI implementation output, we have a different one, but it's our
+            # second best, you can check by using num_return_sequences=10
+            # " Hello! How are you? I'm just getting ready to go to work, how about you?",
+            " Hello! How are you doing today? I just got back from a walk with my dog.",
+        )
+
+        conversation_1 = Conversation(" Lasagne   hello")
+        result = nlp(conversation_1, encoder_no_repeat_ngram_size=3)
+        self.assertEqual(
+            result.generated_responses[0],
+            " Lasagne is my favorite Italian dish. Do you like lasagne?",
+        )
+
+        conversation_1 = Conversation(
+            "Lasagne   hello   Lasagne is my favorite Italian dish. Do you like lasagne?   I like lasagne."
+        )
+        result = nlp(
+            conversation_1,
+            encoder_no_repeat_ngram_size=3,
+        )
+        self.assertEqual(
+            result.generated_responses[0],
+            # ParlAI implementation output, we have a different one, but it's our
+            # second best, you can check by using num_return_sequences=10
+            # " Hello! How are you? I'm just getting ready to go to work, how about you?",
+            " Lasagne is a traditional Italian dish consisting of a yeasted flatbread typically topped with tomato sauce and cheese.",
+        )
+
     @require_torch
     @slow
     def test_integration_torch_conversation_encoder_decoder(self):