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Fixed: Better names for nlp variables in pipelines' tests and docs. (#11752)

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* Fixed: Better names for nlp variables in pipelines' tests and docs.

* Fixed: Better variable names
This commit is contained in:
Vyom Pathak
2021-05-18 19:17:28 +05:30
committed by GitHub
parent cebb96f53a
commit fd3b12e8c3
12 changed files with 163 additions and 159 deletions
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56
tests/test_pipelines_conversational.py
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@@ -128,41 +128,41 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
invalid_inputs = ["Hi there!", Conversation()]
def _test_pipeline(
self, nlp
self, conversation_agent
): # override the default test method to check that the output is a `Conversation` object
self.assertIsNotNone(nlp)
self.assertIsNotNone(conversation_agent)
# 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)
mono_result = conversation_agent(conversation)
self.assertIsInstance(mono_result, Conversation)
conversations = [Conversation("Hi there!"), Conversation("How are you?")]
multi_result = nlp(conversations)
multi_result = conversation_agent(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, conversation)
self.assertRaises(ValueError, nlp, conversations)
self.assertRaises(ValueError, conversation_agent, conversation)
self.assertRaises(ValueError, conversation_agent, conversations)
for bad_input in self.invalid_inputs:
self.assertRaises(Exception, nlp, bad_input)
self.assertRaises(Exception, nlp, self.invalid_inputs)
self.assertRaises(Exception, conversation_agent, bad_input)
self.assertRaises(Exception, conversation_agent, self.invalid_inputs)
@require_torch
@slow
def test_integration_torch_conversation(self):
# When
nlp = pipeline(task="conversational", device=DEFAULT_DEVICE_NUM)
conversation_agent = 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)
result = conversation_agent([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)
@@ -175,7 +175,7 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
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)
result = conversation_agent(conversation_2, do_sample=False, max_length=1000)
# Then
self.assertEqual(result, conversation_2)
self.assertEqual(len(result.past_user_inputs), 2)
@@ -187,12 +187,12 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
@slow
def test_integration_torch_conversation_truncated_history(self):
# When
nlp = pipeline(task="conversational", min_length_for_response=24, device=DEFAULT_DEVICE_NUM)
conversation_agent = 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)
result = conversation_agent(conversation_1, do_sample=False, max_length=36)
# Then
self.assertEqual(result, conversation_1)
self.assertEqual(len(result.past_user_inputs), 1)
@@ -201,7 +201,7 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
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)
result = conversation_agent(conversation_1, do_sample=False, max_length=36)
# Then
self.assertEqual(result, conversation_1)
self.assertEqual(len(result.past_user_inputs), 2)
@@ -214,19 +214,19 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
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_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
conversation_1 = Conversation("hello")
inputs = nlp._parse_and_tokenize([conversation_1])
inputs = conversation_agent._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])
inputs = conversation_agent._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])
inputs = conversation_agent._parse_and_tokenize([conversation_1, conversation_2])
self.assertEqual(
inputs["input_ids"].tolist(),
[
@@ -240,11 +240,11 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
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)
conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
# test1
conversation_1 = Conversation("hello")
inputs = nlp._parse_and_tokenize([conversation_1])
inputs = conversation_agent._parse_and_tokenize([conversation_1])
self.assertEqual(inputs["input_ids"].tolist(), [[1710, 86, 2]])
# test2
@@ -255,7 +255,7 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
" Do you like lasagne? It is a traditional Italian dish consisting of a shepherd's pie."
],
)
inputs = nlp._parse_and_tokenize([conversation_1])
inputs = conversation_agent._parse_and_tokenize([conversation_1])
self.assertEqual(
inputs["input_ids"].tolist(),
[
@@ -310,10 +310,10 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
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_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
conversation_1 = Conversation("hello")
result = nlp(
result = conversation_agent(
conversation_1,
)
self.assertEqual(
@@ -325,7 +325,7 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
)
conversation_1 = Conversation("Lasagne hello")
result = nlp(conversation_1, encoder_no_repeat_ngram_size=3)
result = conversation_agent(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.",
@@ -334,7 +334,7 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
conversation_1 = Conversation(
"Lasagne hello Lasagne is my favorite Italian dish. Do you like lasagne? I like lasagne."
)
result = nlp(
result = conversation_agent(
conversation_1,
encoder_no_repeat_ngram_size=3,
)
@@ -349,7 +349,7 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
# 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_agent = 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? ")
@@ -357,7 +357,7 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
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)
result = conversation_agent([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)
@@ -378,7 +378,7 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
# 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)
result = conversation_agent([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)