def test_integration_torch_conversation_dialogpt_input_ids(self):
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
model = AutoModelForCausalLM.from_pretrained(
"microsoft/DialoGPT-small")
nlp = ConversationalPipeline(model=model, tokenizer=tokenizer)
conversation_1 = Conversation("hello")
inputs = nlp._parse_and_tokenize([conversation_1])
self.assertEqual(inputs["input_ids"].tolist(), [[31373, 50256]])
conversation_2 = Conversation("how are you ?",
past_user_inputs=["hello"],
generated_responses=["Hi there!"])
inputs = nlp._parse_and_tokenize([conversation_2])
self.assertEqual(
inputs["input_ids"].tolist(),
[[31373, 50256, 17250, 612, 0, 50256, 4919, 389, 345, 5633, 50256]
])
inputs = nlp._parse_and_tokenize([conversation_1, conversation_2])
self.assertEqual(
inputs["input_ids"].tolist(),
[
[
31373, 50256, 50256, 50256, 50256, 50256, 50256, 50256,
50256, 50256, 50256
],
[
31373, 50256, 17250, 612, 0, 50256, 4919, 389, 345, 5633,
50256
],
],
)
def test_integration_torch_conversation_encoder_decoder(self):
# When
tokenizer = AutoTokenizer.from_pretrained(
"facebook/blenderbot_small-90M")
model = AutoModelForSeq2SeqLM.from_pretrained(
"facebook/blenderbot_small-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.")
def test_integration_torch_conversation_blenderbot_400M(self):
tokenizer = AutoTokenizer.from_pretrained(
"facebook/blenderbot-400M-distill")
model = AutoModelForSeq2SeqLM.from_pretrained(
"facebook/blenderbot-400M-distill")
nlp = ConversationalPipeline(model=model, tokenizer=tokenizer)
conversation_1 = Conversation("hello")
result = nlp(conversation_1, )
self.assertEqual(
result.generated_responses[0],
# ParlAI implementation output, we have a different one, but it's our
# second best, you can check by using num_return_sequences=10
# " Hello! How are you? I'm just getting ready to go to work, how about you?",
" Hello! How are you doing today? I just got back from a walk with my dog.",
)
conversation_1 = Conversation("Lasagne hello")
result = nlp(conversation_1, encoder_no_repeat_ngram_size=3)
self.assertEqual(
result.generated_responses[0],
" Do you like lasagne? It is a traditional Italian dish consisting of a shepherd's pie.",
)
conversation_1 = Conversation(
"Lasagne hello Lasagne is my favorite Italian dish. Do you like lasagne? I like lasagne."
)
result = nlp(
conversation_1,
encoder_no_repeat_ngram_size=3,
)
self.assertEqual(
result.generated_responses[0],
" Me too. I like how it can be topped with vegetables, meats, and condiments.",
)
def test_small_model_tf(self):
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
model = TFAutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
conversation = Conversation("hello")
output = conversation_agent(conversation)
self.assertEqual(output, Conversation(past_user_inputs=["hello"], generated_responses=["Hi"]))
def run_pipeline_test(self, model, tokenizer, feature_extractor):
conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
# Simple
outputs = conversation_agent(Conversation("Hi there!"))
self.assertEqual(outputs, Conversation(past_user_inputs=["Hi there!"], generated_responses=[ANY(str)]))
# Single list
outputs = conversation_agent([Conversation("Hi there!")])
self.assertEqual(outputs, Conversation(past_user_inputs=["Hi there!"], generated_responses=[ANY(str)]))
# Batch
conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
conversation_2 = Conversation("What's the last book you have read?")
self.assertEqual(len(conversation_1.past_user_inputs), 0)
self.assertEqual(len(conversation_2.past_user_inputs), 0)
outputs = conversation_agent([conversation_1, conversation_2])
self.assertEqual(outputs, [conversation_1, conversation_2])
self.assertEqual(
outputs,
[
Conversation(
past_user_inputs=["Going to the movies tonight - any suggestions?"],
generated_responses=[ANY(str)],
),
Conversation(past_user_inputs=["What's the last book you have read?"], generated_responses=[ANY(str)]),
],
)
# One conversation with history
conversation_2.add_user_input("Why do you recommend it?")
outputs = conversation_agent(conversation_2)
self.assertEqual(outputs, conversation_2)
self.assertEqual(
outputs,
Conversation(
past_user_inputs=["What's the last book you have read?", "Why do you recommend it?"],
generated_responses=[ANY(str), ANY(str)],
),
)
with self.assertRaises(ValueError):
conversation_agent("Hi there!")
with self.assertRaises(ValueError):
conversation_agent(Conversation())
# Conversation have been consumed and are not valid anymore
# Inactive conversations passed to the pipeline raise a ValueError
with self.assertRaises(ValueError):
conversation_agent(conversation_2)
def test_integration_torch_conversation_blenderbot_400M_input_ids(self):
tokenizer = AutoTokenizer.from_pretrained(
"facebook/blenderbot-400M-distill")
model = AutoModelForSeq2SeqLM.from_pretrained(
"facebook/blenderbot-400M-distill")
nlp = ConversationalPipeline(model=model, tokenizer=tokenizer)
# test1
conversation_1 = Conversation("hello")
inputs = nlp._parse_and_tokenize([conversation_1])
self.assertEqual(inputs["input_ids"].tolist(), [[1710, 86, 2]])
# test2
conversation_1 = Conversation(
"I like lasagne.",
past_user_inputs=["hello"],
generated_responses=[
" Do you like lasagne? It is a traditional Italian dish consisting of a shepherd's pie."
],
)
inputs = nlp._parse_and_tokenize([conversation_1])
self.assertEqual(
inputs["input_ids"].tolist(),
[
# This should be compared with the same conversation on ParlAI `safe_interactive` demo.
[
1710, # hello
86,
228, # Double space
228,
946,
304,
398,
6881,
558,
964,
38,
452,
315,
265,
6252,
452,
322,
968,
6884,
3146,
278,
306,
265,
617,
87,
388,
75,
341,
286,
521,
21,
228, # Double space
228,
281, # I like lasagne.
398,
6881,
558,
964,
21,
2, # EOS
]
],
)
def get_test_pipeline(self, model, tokenizer, feature_extractor):
conversation_agent = ConversationalPipeline(model=model,
tokenizer=tokenizer)
return conversation_agent, [Conversation("Hi there!")]
BlenderbotForConditionalGeneration,
BlenderbotTokenizer,
)
if __name__ == "__main__":
import logging
logger = logging.getLogger("transformers").setLevel(logging.CRITICAL)
model = BlenderbotForConditionalGeneration.from_pretrained(
"facebook/blenderbot-400M-distill")
tokenizer = BlenderbotTokenizer.from_pretrained(
"facebook/blenderbot-400M-distill")
convo = ConversationalPipeline(model=model,
tokenizer=tokenizer,
min_length_for_response=0,
framework="pt")
ipt = input(">>> ")
dialogue = Conversation(ipt)
while True:
try:
convo(dialogue, num_beams=3, min_length=0, temperature=1.5)
print(dialogue.generated_responses[-1][1:])
dialogue.add_user_input(input(">>> "))
truncate_convo_to_token_limit(dialogue)
except KeyboardInterrupt:
break
print("\n------Dialogue Summary------")
print(dialogue)