Updated ConversationalPipeline to work with encoder-decoder models

src/transformers/pipelines.py

@@ -2430,18 +2430,31 @@ def __call__(
                 **generate_kwargs,
             )
 
-            cleaned_history = self._clean_padding_history(generated_responses)
+            if self.model.config.is_encoder_decoder:
+                if self.framework == "pt":
+                    history = torch.cat((inputs["input_ids"], generated_responses[:, 1:]), 1)
+                elif self.framework == "tf":
+                    history = tf.concat([inputs["input_ids"], generated_responses[:, 1:]], 1)
+            else:
+                history = generated_responses
+
+            history = self._clean_padding_history(history)
+            if self.model.config.is_encoder_decoder:
+                start_position = 1
+            else:
+                start_position = input_length
+
             output = []
             for conversation_index, conversation in enumerate(conversations):
                 conversation.mark_processed()
                 conversation.generated_responses.append(
                     self.tokenizer.decode(
-                        cleaned_history[conversation_index][input_length:],
+                        generated_responses[conversation_index][start_position:],
                         skip_special_tokens=True,
                         clean_up_tokenization_spaces=clean_up_tokenization_spaces,
                     )
                 )
-                conversation.set_history(cleaned_history[conversation_index])
+                conversation.set_history(history[conversation_index])
                 output.append(conversation)
             if len(output) == 1:
                 return output[0]
@@ -2475,6 +2488,8 @@ def _clean_padding_history(self, generated_tensor) -> List[List[int]]:
             is_previous_pad = False
             for token in sequence:
                 if token == self.tokenizer.pad_token_id:
+                    if self.tokenizer.pad_token_id != self.tokenizer.eos_token_id:
+                        continue
                     if is_previous_pad:
                         continue
                     else:

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 @@ def test_integration_torch_conversation_truncated_history(self):
         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.")