Backward compatibility fix for the Conversation class (huggingface#27176

)

* Backward compatibility fix for the Conversation class

* Explain what's going on in the conditional

src/transformers/pipelines/conversational.py

@@ -54,6 +54,7 @@ def __init__(
 
         # This block deals with the legacy args - new code should just totally
         # avoid past_user_inputs and generated_responses
+        self._num_processed_user_inputs = 0
         generated_responses = deprecated_kwargs.pop("generated_responses", None)
         past_user_inputs = deprecated_kwargs.pop("past_user_inputs", None)
         if generated_responses is not None and past_user_inputs is None:
@@ -114,10 +115,11 @@ def append_response(self, response: str):
 
     def mark_processed(self):
         """
-        This is a legacy method that no longer has any effect, as the Conversation no longer distinguishes between
-        processed and unprocessed user input.
+        This is a legacy method, as the Conversation no longer distinguishes between processed and unprocessed user
+        input. We set a counter here to keep behaviour mostly backward-compatible, but in general you should just read
+        the messages directly when writing new code.
         """
-        pass
+        self._num_processed_user_inputs = len(self._user_messages)
 
     def __iter__(self):
         for message in self.messages:
@@ -163,7 +165,17 @@ def _user_messages(self):
     @property
     def past_user_inputs(self):
         # This is a legacy property for backwards compatibility. It is recommended to just directly access
-        # conversation.messages instead.
+        # conversation.messages instead. The modern class does not care about which messages are "processed"
+        # or not.
+        if not self._user_messages:
+            return []
+        # In the past, the most recent user message had to be mark_processed() before being included
+        # in past_user_messages. The class essentially had a single-message buffer, representing messages that
+        # had not yet been replied to. This is no longer the case, but we mimic the behaviour in this property
+        # for backward compatibility.
+        if self.messages[-1]["role"] != "user" or self._num_processed_user_inputs == len(self._user_messages):
+            return self._user_messages
+
         return self._user_messages[:-1]
 
     @property

tests/pipelines/test_pipelines_conversational.py

@@ -136,8 +136,8 @@ def test_integration_torch_conversation(self):
         conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
         conversation_2 = Conversation("What's the last book you have read?")
         # Then
-        self.assertEqual(len(conversation_1.past_user_inputs), 1)
-        self.assertEqual(len(conversation_2.past_user_inputs), 1)
+        self.assertEqual(len(conversation_1.past_user_inputs), 0)
+        self.assertEqual(len(conversation_2.past_user_inputs), 0)
         # When
         result = conversation_agent([conversation_1, conversation_2], do_sample=False, max_length=1000)
         # Then
@@ -167,7 +167,7 @@ def test_integration_torch_conversation_truncated_history(self):
         conversation_agent = pipeline(task="conversational", min_length_for_response=24, device=torch_device)
         conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
         # Then
-        self.assertEqual(len(conversation_1.past_user_inputs), 1)
+        self.assertEqual(len(conversation_1.past_user_inputs), 0)
         # When
         result = conversation_agent(conversation_1, do_sample=False, max_length=36)
         # Then
@@ -375,8 +375,8 @@ def test_integration_torch_conversation_encoder_decoder(self):
         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), 1)
-        self.assertEqual(len(conversation_2.past_user_inputs), 1)
+        self.assertEqual(len(conversation_1.past_user_inputs), 0)
+        self.assertEqual(len(conversation_2.past_user_inputs), 0)
         # When
         result = conversation_agent([conversation_1, conversation_2], do_sample=False, max_length=1000)
         # Then