def test_integration_torch_conversation_truncated_history(self):
# When
nlp = pipeline(task="conversational",
min_length_for_response=24,
device=DEFAULT_DEVICE_NUM)
conversation_1 = Conversation(
"Going to the movies tonight - any suggestions?")
# Then
self.assertEqual(len(conversation_1.past_user_inputs), 0)
# When
result = nlp(conversation_1, do_sample=False, max_length=36)
# Then
self.assertEqual(result, conversation_1)
self.assertEqual(len(result.past_user_inputs), 1)
self.assertEqual(len(result.generated_responses), 1)
self.assertEqual(result.past_user_inputs[0],
"Going to the movies tonight - any suggestions?")
self.assertEqual(result.generated_responses[0], "The Big Lebowski")
# When
conversation_1.add_user_input("Is it an action movie?")
result = nlp(conversation_1, do_sample=False, max_length=36)
# Then
self.assertEqual(result, conversation_1)
self.assertEqual(len(result.past_user_inputs), 2)
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.")
def test_integration_torch_conversation(self):
# When
nlp = pipeline(task="conversational", device=DEFAULT_DEVICE_NUM)
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), 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], "Going to the movies tonight - any suggestions?")
self.assertEqual(result[0].generated_responses[0], "The Big Lebowski")
self.assertEqual(result[1].past_user_inputs[0], "What's the last book you have read?")
self.assertEqual(result[1].generated_responses[0], "The Last Question")
# When
conversation_2.add_user_input("Why do you recommend it?")
result = nlp(conversation_2, do_sample=False, max_length=1000)
# Then
self.assertEqual(result, conversation_2)
self.assertEqual(len(result.past_user_inputs), 2)
self.assertEqual(len(result.generated_responses), 2)
self.assertEqual(result.past_user_inputs[1], "Why do you recommend it?")
self.assertEqual(result.generated_responses[1], "It's a good book.")
def _test_conversation_pipeline(self, nlp):
valid_inputs = [Conversation("Hi there!"), [Conversation("Hi there!"), Conversation("How are you?")]]
invalid_inputs = ["Hi there!", Conversation()]
self.assertIsNotNone(nlp)
mono_result = nlp(valid_inputs[0])
self.assertIsInstance(mono_result, Conversation)
multi_result = nlp(valid_inputs[1])
self.assertIsInstance(multi_result, list)
self.assertIsInstance(multi_result[0], Conversation)
# Inactive conversations passed to the pipeline raise a ValueError
self.assertRaises(ValueError, nlp, valid_inputs[1])
for bad_input in invalid_inputs:
self.assertRaises(Exception, nlp, bad_input)
self.assertRaises(Exception, nlp, invalid_inputs)
cache_dir=cache_dir,
)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
from_tf=False,
config=config,
cache_dir=cache_dir,
)
config.min_length = 2
config.max_length = 1000
print(f"min_length: {config.min_length}")
print(f"max_length: {config.max_length}")
conversation = Conversation()
conversation_manager = ConversationalPipeline(model=model, tokenizer=tokenizer)
conversation.add_user_input("Is it an action movie?")
conversation_manager([conversation])
print(f"Response: {conversation.generated_responses[-1]}")
conversation.add_user_input("Is it a love movie?")
conversation_manager([conversation])
print(f"Response: {conversation.generated_responses[-1]}")
conversation.add_user_input("What is it about?")
conversation_manager([conversation])
print(f"Response: {conversation.generated_responses[-1]}")
conversation.add_user_input("Would you recommend it?")