[FIX] TextGenerationPipeline is currently broken.

src/transformers/pipelines.py

@@ -836,6 +836,8 @@ def __call__(
               -- The token ids of the generated text.
         """
 
+        if isinstance(text_inputs, str):
+            text_inputs = [text_inputs]
         results = []
         for prompt_text in text_inputs:
             # Manage correct placement of the tensors
@@ -2382,6 +2384,8 @@ def __call__(
             updated generated responses for those containing a new user input.
         """
 
+        if isinstance(conversations, Conversation):
+            conversations = [conversations]
         # Input validation
         if isinstance(conversations, list):
             for conversation in conversations:
@@ -2398,8 +2402,6 @@ def __call__(
             assert (
                 self.tokenizer.pad_token_id is not None or self.tokenizer.eos_token_id is not None
             ), "Please make sure that the tokenizer has a pad_token_id or eos_token_id when using a batch input"
-        elif isinstance(conversations, Conversation):
-            conversations = [conversations]
         else:
             raise ValueError("DialoguePipeline expects a Conversation or list of Conversations as an input")
 

tests/test_pipelines_conversational.py

@@ -9,26 +9,30 @@
 DEFAULT_DEVICE_NUM = -1 if torch_device == "cpu" else 0
 
 
-class TextGenerationPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
+class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
     pipeline_task = "conversational"
     small_models = []  # Models tested without the @slow decorator
     large_models = ["microsoft/DialoGPT-medium"]  # Models tested with the @slow decorator
-    valid_inputs = [Conversation("Hi there!"), [Conversation("Hi there!"), Conversation("How are you?")]]
     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):
+        # e overide the default test method to check that the output is a `Conversation` object
         self.assertIsNotNone(nlp)
 
-        mono_result = nlp(self.valid_inputs[0])
+        # We need to recreate conversation for successive tests to pass as
+        # Conversation objects get *consumed* by the pipeline
+        conversation = Conversation("Hi there!")
+        mono_result = nlp(conversation)
         self.assertIsInstance(mono_result, Conversation)
 
-        multi_result = nlp(self.valid_inputs[1])
+        conversations = [Conversation("Hi there!"), Conversation("How are you?")]
+        multi_result = nlp(conversations)
         self.assertIsInstance(multi_result, list)
         self.assertIsInstance(multi_result[0], Conversation)
+        # Conversation have been consumed and are not valid anymore
         # Inactive conversations passed to the pipeline raise a ValueError
-        self.assertRaises(ValueError, nlp, self.valid_inputs[1])
+        self.assertRaises(ValueError, nlp, conversation)
+        self.assertRaises(ValueError, nlp, conversations)
 
         for bad_input in self.invalid_inputs:
             self.assertRaises(Exception, nlp, bad_input)

tests/test_pipelines_text_generation.py

@@ -1,5 +1,7 @@
 import unittest
 
+from transformers import pipeline
+
 from .test_pipelines_common import MonoInputPipelineCommonMixin
 
 
@@ -8,3 +10,20 @@ class TextGenerationPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
     pipeline_running_kwargs = {"prefix": "This is "}
     small_models = ["sshleifer/tiny-ctrl"]  # Models tested without the @slow decorator
     large_models = []  # Models tested with the @slow decorator
+
+    def test_simple_generation(self):
+        nlp = pipeline(task="text-generation", model=self.small_models[0])
+        # text-generation is non-deterministic by nature, we can't fully test the output
+
+        outputs = nlp("This is a test")
+
+        self.assertEqual(len(outputs), 1)
+        self.assertEqual(list(outputs[0].keys()), ["generated_text"])
+        self.assertEqual(type(outputs[0]["generated_text"]), str)
+
+        outputs = nlp(["This is a test", "This is a second test"])
+        self.assertEqual(len(outputs[0]), 1)
+        self.assertEqual(list(outputs[0][0].keys()), ["generated_text"])
+        self.assertEqual(type(outputs[0][0]["generated_text"]), str)
+        self.assertEqual(list(outputs[1][0].keys()), ["generated_text"])
+        self.assertEqual(type(outputs[1][0]["generated_text"]), str)