Updated ConversationalPipeline to work with encoder-decoder models (#8207)

* Updated ConversationalPipeline to work with encoder-decoder models (e.g. BlenderBot)

* Addition of integration test for EncoderDecoder conversation model

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

tests/test_pipelines_conversational.py

@@ -1,6 +1,6 @@
 import unittest
 
-from transformers import Conversation, pipeline
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, Conversation, ConversationalPipeline, pipeline
 from transformers.testing_utils import require_torch, slow, torch_device
 
 from .test_pipelines_common import MonoInputPipelineCommonMixin
@@ -15,8 +15,9 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
     large_models = ["microsoft/DialoGPT-medium"]  # Models tested with the @slow decorator
     invalid_inputs = ["Hi there!", Conversation()]
 
-    def _test_pipeline(self, nlp):
-        # e overide the default test method to check that the output is a `Conversation` object
+    def _test_pipeline(
+        self, nlp
+    ):  # override the default test method to check that the output is a `Conversation` object
         self.assertIsNotNone(nlp)
 
         # We need to recreate conversation for successive tests to pass as
@@ -95,3 +96,50 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
         self.assertEqual(len(result.generated_responses), 2)
         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_encoder_decoder(self):
+        # When
+        tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-90M")
+        model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-90M")
+        nlp = ConversationalPipeline(model=model, tokenizer=tokenizer, device=DEFAULT_DEVICE_NUM)
+
+        conversation_1 = Conversation("My name is Sarah and I live in London")
+        conversation_2 = Conversation("Going to the movies tonight, What movie would you recommend? ")
+        # Then
+        self.assertEqual(len(conversation_1.past_user_inputs), 0)
+        self.assertEqual(len(conversation_2.past_user_inputs), 0)
+        # When
+        result = nlp([conversation_1, conversation_2], do_sample=False, max_length=1000)
+        # Then
+        self.assertEqual(result, [conversation_1, conversation_2])
+        self.assertEqual(len(result[0].past_user_inputs), 1)
+        self.assertEqual(len(result[1].past_user_inputs), 1)
+        self.assertEqual(len(result[0].generated_responses), 1)
+        self.assertEqual(len(result[1].generated_responses), 1)
+        self.assertEqual(result[0].past_user_inputs[0], "My name is Sarah and I live in London")
+        self.assertEqual(
+            result[0].generated_responses[0],
+            "hi sarah, i live in london as well. do you have any plans for the weekend?",
+        )
+        self.assertEqual(
+            result[1].past_user_inputs[0], "Going to the movies tonight, What movie would you recommend? "
+        )
+        self.assertEqual(
+            result[1].generated_responses[0], "i don't know... i'm not really sure. what movie are you going to see?"
+        )
+        # When
+        conversation_1.add_user_input("Not yet, what about you?")
+        conversation_2.add_user_input("What's your name?")
+        result = nlp([conversation_1, conversation_2], do_sample=False, max_length=1000)
+        # Then
+        self.assertEqual(result, [conversation_1, conversation_2])
+        self.assertEqual(len(result[0].past_user_inputs), 2)
+        self.assertEqual(len(result[1].past_user_inputs), 2)
+        self.assertEqual(len(result[0].generated_responses), 2)
+        self.assertEqual(len(result[1].generated_responses), 2)
+        self.assertEqual(result[0].past_user_inputs[1], "Not yet, what about you?")
+        self.assertEqual(result[0].generated_responses[1], "i don't have any plans yet. i'm not sure what to do yet.")
+        self.assertEqual(result[1].past_user_inputs[1], "What's your name?")
+        self.assertEqual(result[1].generated_responses[1], "i don't have a name, but i'm going to see a horror movie.")