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TF: GPT-2 generation supports left-padding (#17426)

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* TF GPT-2 now properly works with left padding

* throw a warning when eos token == pad token and there is no attention mask
This commit is contained in:
Joao Gante
2022-05-31 14:06:44 +01:00
committed by GitHub
parent c1a138613d
commit 975dd2bbbc
4 changed files with 136 additions and 96 deletions
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81
tests/models/gpt2/test_modeling_tf_gpt2.py
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@@ -456,7 +456,7 @@ class TFGPT2ModelLanguageGenerationTest(unittest.TestCase):
tokenizer.padding_side = "left"
sentences = ["Today is a beautiful day and", "Yesterday was"]
input_ids = tokenizer(sentences, return_tensors="tf", padding=True).input_ids
input_ids = tokenizer(sentences, return_tensors="tf", padding=True)
generation_kwargs = {
"bad_words_ids": [tokenizer("is").input_ids, tokenizer("angry about").input_ids],
@@ -465,12 +465,12 @@ class TFGPT2ModelLanguageGenerationTest(unittest.TestCase):
"repetition_penalty": 1.3,
}
output_ids = model.generate(input_ids, **generation_kwargs)
output_ids = model.generate(**input_ids, **generation_kwargs)
output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
expected_output_string = [
"Today is a beautiful day and I am so happy to be able take part in this amazing event.",
"Yesterday was a very busy day for the first time since I started writing this post",
"Yesterday was a very interesting time for the world to see how much of this is",
]
self.assertListEqual(output_strings, expected_output_string)
@@ -483,7 +483,7 @@ class TFGPT2ModelLanguageGenerationTest(unittest.TestCase):
tokenizer.padding_side = "left"
sentences = ["Today is a beautiful day and", "Yesterday was"]
input_ids = tokenizer(sentences, return_tensors="tf", padding=True).input_ids
input_ids = tokenizer(sentences, return_tensors="tf", padding=True)
generation_kwargs = {
"do_sample": True,
@@ -498,13 +498,13 @@ class TFGPT2ModelLanguageGenerationTest(unittest.TestCase):
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(":/CPU:0"):
output_ids = model.generate(input_ids, **generation_kwargs)
output_ids = model.generate(**input_ids, **generation_kwargs)
output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
expected_output_string = [
"Today is a beautiful day and we will make you feel very hot/terrific in all",
"Yesterday was another solid success as news coverage became standard American domestic television hit.",
"Today is a beautiful day and we will make you feel very hot/terrific in all your",
"Yesterday was known by national television networks as Le Big Show or Wild Dog Jeopard",
]
self.assertListEqual(output_strings, expected_output_string)
@@ -517,7 +517,7 @@ class TFGPT2ModelLanguageGenerationTest(unittest.TestCase):
tokenizer.padding_side = "left"
sentences = ["Today is a beautiful day and", "Yesterday was"]
input_ids = tokenizer(sentences, return_tensors="tf", padding=True).input_ids
input_ids = tokenizer(sentences, return_tensors="tf", padding=True)
generation_kwargs = {
"bad_words_ids": [tokenizer("is").input_ids, tokenizer("angry about").input_ids],
@@ -526,37 +526,69 @@ class TFGPT2ModelLanguageGenerationTest(unittest.TestCase):
"num_beams": 2,
}
output_ids = model.generate(input_ids, **generation_kwargs)
output_ids = model.generate(**input_ids, **generation_kwargs)
output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
expected_output_string = [
"Today is a beautiful day and a great day for all of us.\n\nI’m",
"Yesterday was the first day of the year for the second time in a row,",
"Yesterday was the first time that a person has been arrested in the United States for",
]
self.assertListEqual(output_strings, expected_output_string)
@slow
def test_lm_generate_distilgpt2_left_padding(self):
"""Tests that the generated text is the same, regarless of left padding"""
model = TFGPT2LMHeadModel.from_pretrained("distilgpt2")
tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2")
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"
generation_kwargs = {
"bad_words_ids": [tokenizer("is").input_ids, tokenizer("angry about").input_ids],
"no_repeat_ngram_size": 2,
"do_sample": False,
"repetition_penalty": 1.3,
}
expected_output_string = (
"Today is a beautiful day and I am so happy to be able take part in this amazing event."
)
sentences = ["Today is a beautiful day and"]
input_ids = tokenizer(sentences, return_tensors="tf", padding=True)
# using default length
output_ids = model.generate(**input_ids, **generation_kwargs)
output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
self.assertEqual(output_strings[0], expected_output_string)
sentences = ["Today is a beautiful day and", "This is a very long input that we absolutely don't care about"]
input_ids = tokenizer(sentences, return_tensors="tf", padding=True)
# longer max length to capture the full length (remember: it is left padded)
output_ids = model.generate(**input_ids, **generation_kwargs, max_length=27)
output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
self.assertEqual(output_strings[0], expected_output_string)
@slow
def test_lm_generate_gpt2_greedy_xla(self):
# TODO (Joao): convert this to an example with a batch size>1 with different input lengths that works (and fix
# the underlying problem)
model = TFGPT2LMHeadModel.from_pretrained("gpt2")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"
sentences = ["The dog"]
sentences = ["The dog", "The flying machine"]
expected_output_strings = [
"The dog was found in a field near the intersection of West and West Streets.\n\nThe dog",
"The dog was found in a field near the intersection of West and West Streets.\n\nThe",
"The flying machine is a small, lightweight, and lightweight aircraft that can be used for any type of",
]
input_ids = tokenizer(sentences, return_tensors="tf", padding=True).input_ids
input_ids = tokenizer(sentences, return_tensors="tf", padding=True)
output_ids = model.generate(input_ids, do_sample=False)
output_ids = model.generate(**input_ids, do_sample=False)
output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
self.assertListEqual(output_strings, expected_output_strings)
xla_generate = tf.function(model.generate, jit_compile=True)
output_ids = xla_generate(input_ids, do_sample=False)
output_ids = xla_generate(**input_ids, do_sample=False)
output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
self.assertListEqual(output_strings, expected_output_strings)
@@ -574,21 +606,24 @@ class TFGPT2ModelLanguageGenerationTest(unittest.TestCase):
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"
sentence = ["The dog"]
sentence = ["The dog", "The flying machine"]
expected_output_string = [
"The dog owner asked why did our vet decide there needed to be extra ventilation inside because most"
" puppies"
" puppies",
"The flying machine was made by an artist who found it difficult to control it as it did not use",
]
expected_output_string_xla = [
"The dog has been named in connection with the murder of a 20-year-old man in!"
"The dog has been named in connection with the murder of a 20-year-old man in",
"The flying machine is a new and improved system to operate and operate a new system and system "
"system system",
]
input_ids = tokenizer(sentence, return_tensors="tf", padding=True).input_ids
input_ids = tokenizer(sentence, return_tensors="tf", padding=True)
output_ids = model.generate(input_ids, do_sample=True, seed=[7, 0])
output_ids = model.generate(**input_ids, do_sample=True, seed=[7, 0])
output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
self.assertListEqual(output_strings, expected_output_string)
xla_generate = tf.function(model.generate, jit_compile=True)
output_ids = xla_generate(input_ids, do_sample=True, seed=[7, 0])
output_ids = xla_generate(**input_ids, do_sample=True, seed=[7, 0])
output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
self.assertListEqual(output_strings, expected_output_string_xla)