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Cleaning up ConversationalPipeline to support more than DialoGPT. (#10002)

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* Cleaning up `ConversationalPipeline` to support more than DialoGPT.

Currently ConversationalPipeline was heavily biased towards DialoGPT
,which is the default model for this pipeline.

This PR proposes changes to put back the modifications specific to
DialoGPT into tokenizer-specific behavior wherever possible, by
creating `_build_conversation_input_ids` function that takes
conversation as input, and returns a list of ints corresponding
to the tokens. It feels natural to put here because all models
have probably different strategies to build input_ids from the
full conversation and it's the tokenizer's job to transform strings
into tokens (and vice-versa)

If `_build_conversation_input_ids` is missing, previous behavior is
used so we don't break anything so far (except for blenderbot where it's a fix).

This PR also contains a fix for too long inputs. There used
to be dead code for trying to limit the size of incoming input.
The introduced fixed is that we limit
within `_build_conversation_input_ids` to `tokenizer.model_max_length`.
It corresponds to the intent of the removed dead code and is actually
better because it corresponds to `model_max_length` which is different
from `max_length` (which is a default parameter for `generate`).

- Removed `history` logic from the Conversation as it's not relevant
anymore because tokenization logic has been moved to tokenizer.
And tokenizer cannot save any cache, and conversation cannot know
what is relevant or not.
Also it's not usable from `blenderbot` because the input_ids are
not append only (EOS tokens is always at the end).

- Added `iter_texts` method on `Conversation` because all
the code was literred with some form of this iteration of
past/generated_responses.

* Removing torch mention in types.

* Adding type checking to `_build_conversation_input_ids`.

* Fixing import in strings.
This commit is contained in:
Nicolas Patry
2021-02-08 12:29:07 +01:00
committed by GitHub
parent ae37ceacbd
commit b1aa4982cd
5 changed files with 189 additions and 163 deletions
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178
tests/test_pipelines_conversational.py
View File

@@ -15,6 +15,7 @@
import unittest
from transformers import (
AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoTokenizer,
Conversation,
@@ -87,11 +88,7 @@ class SimpleConversationPipelineTests(unittest.TestCase):
self.assertEqual(len(conversation_1.past_user_inputs), 0)
self.assertEqual(len(conversation_2.past_user_inputs), 0)
with self.assertLogs("transformers", level="WARNING") as log:
result = conversation_agent([conversation_1, conversation_2], max_length=48)
self.assertEqual(len(log.output), 2)
self.assertIn("You might consider trimming the early phase of the conversation", log.output[0])
self.assertIn("Setting `pad_token_id`", log.output[1])
result = conversation_agent([conversation_1, conversation_2], max_length=48)
# Two conversations in one pass
self.assertEqual(result, [conversation_1, conversation_2])
@@ -111,12 +108,7 @@ class SimpleConversationPipelineTests(unittest.TestCase):
# One conversation with history
conversation_2.add_user_input("Why do you recommend it?")
with self.assertLogs("transformers", level="WARNING") as log:
result = conversation_agent(conversation_2, max_length=64)
self.assertEqual(len(log.output), 3)
self.assertIn("Cutting history off because it's too long", log.output[0])
self.assertIn("You might consider trimming the early phase of the conversation", log.output[1])
self.assertIn("Setting `pad_token_id`", log.output[2])
result = conversation_agent(conversation_2, max_length=64)
self.assertEqual(result, conversation_2)
self.assertEqual(
@@ -128,65 +120,6 @@ class SimpleConversationPipelineTests(unittest.TestCase):
),
)
@require_torch
def test_history_cache(self):
conversation_agent = self.get_pipeline()
conversation = Conversation(
"Why do you recommend it?",
past_user_inputs=["What's the last book you have read?"],
generated_responses=["b"],
)
with self.assertLogs("transformers", level="WARNING") as log:
_ = conversation_agent(conversation, max_length=64)
self.assertEqual(len(log.output), 3)
self.assertIn("Cutting history off because it's too long (63 > 32) for underlying model", log.output[0])
self.assertIn("63 is bigger than 0.9 * max_length: 64", log.output[1])
self.assertIn("Setting `pad_token_id`", log.output[2])
self.assertEqual(conversation._index, 1)
self.assertEqual(
conversation._history,
[
87,
104,
97,
116,
39,
115,
32,
116,
104,
101,
32,
108,
97,
115,
116,
32,
98,
111,
111,
107,
32,
121,
111,
117,
32,
104,
97,
118,
101,
32,
114,
101,
97,
100,
63,
259, # EOS
98, # b
259, # EOS
],
)
class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
pipeline_task = "conversational"
@@ -276,6 +209,102 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
self.assertEqual(result.past_user_inputs[1], "Is it an action movie?")
self.assertEqual(result.generated_responses[1], "It's a comedy.")
@require_torch
@slow
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],
],
)
@require_torch
@slow
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
]
],
)
@require_torch
@slow
def test_integration_torch_conversation_blenderbot_400M(self):
@@ -295,11 +324,11 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
" Hello! How are you doing today? I just got back from a walk with my dog.",
)
conversation_1 = Conversation(" Lasagne hello")
conversation_1 = Conversation("Lasagne hello")
result = nlp(conversation_1, encoder_no_repeat_ngram_size=3)
self.assertEqual(
result.generated_responses[0],
" Lasagne is my favorite Italian dish. Do you like lasagne?",
" Do you like lasagne? It is a traditional Italian dish consisting of a shepherd's pie.",
)
conversation_1 = Conversation(
@@ -311,10 +340,7 @@ class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCas
)
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?",
" Lasagne is a traditional Italian dish consisting of a yeasted flatbread typically topped with tomato sauce and cheese.",
" Me too. I like how it can be topped with vegetables, meats, and condiments.",
)
@require_torch