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[Generation] Allow inputs_embeds as an input (#14443)

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* up

* finalize

* finalize

* finish

* Update src/transformers/generation_utils.py

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This commit is contained in:
Patrick von Platen
2021-11-19 15:35:06 +01:00
committed by GitHub
parent 0490b98877
commit f25a9332e8
2 changed files with 92 additions and 32 deletions
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92
tests/test_generation_utils.py
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@@ -1388,15 +1388,17 @@ class GenerationIntegrationTests(unittest.TestCase):
def test_max_length_backward_compat_greedy(self):
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_tokenizer = BartTokenizer.from_pretrained("sshleifer/bart-tiny-random")
bart_model = BartForConditionalGeneration.from_pretrained("sshleifer/bart-tiny-random").to(torch_device)
bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
torch_device
)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
max_length = 20
input_ids = input_ids.expand(2, -1)
model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
input_ids = bart_model._prepare_decoder_input_ids_for_generation(
input_ids,
input_ids.shape[0],
decoder_start_token_id=bart_model.config.decoder_start_token_id,
bos_token_id=bart_model.config.bos_token_id,
)
@@ -1412,15 +1414,17 @@ class GenerationIntegrationTests(unittest.TestCase):
def test_max_length_backward_compat_sample(self):
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_tokenizer = BartTokenizer.from_pretrained("sshleifer/bart-tiny-random")
bart_model = BartForConditionalGeneration.from_pretrained("sshleifer/bart-tiny-random").to(torch_device)
bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
torch_device
)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
max_length = 20
input_ids = input_ids.expand(2, -1)
model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
input_ids = bart_model._prepare_decoder_input_ids_for_generation(
input_ids,
input_ids.shape[0],
decoder_start_token_id=bart_model.config.decoder_start_token_id,
bos_token_id=bart_model.config.bos_token_id,
)
@@ -1436,8 +1440,10 @@ class GenerationIntegrationTests(unittest.TestCase):
def test_max_length_backward_compat_beam_search(self):
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_tokenizer = BartTokenizer.from_pretrained("sshleifer/bart-tiny-random")
bart_model = BartForConditionalGeneration.from_pretrained("sshleifer/bart-tiny-random").to(torch_device)
bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
torch_device
)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
batch_size = 1
@@ -1447,7 +1453,7 @@ class GenerationIntegrationTests(unittest.TestCase):
input_ids = input_ids.expand(2, -1)
model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
input_ids = bart_model._prepare_decoder_input_ids_for_generation(
input_ids,
input_ids.shape[0],
decoder_start_token_id=bart_model.config.decoder_start_token_id,
bos_token_id=bart_model.config.bos_token_id,
)
@@ -1464,8 +1470,10 @@ class GenerationIntegrationTests(unittest.TestCase):
def test_max_length_backward_compat_group_beam_search(self):
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_tokenizer = BartTokenizer.from_pretrained("sshleifer/bart-tiny-random")
bart_model = BartForConditionalGeneration.from_pretrained("sshleifer/bart-tiny-random").to(torch_device)
bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
torch_device
)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
batch_size = 1
@@ -1477,7 +1485,7 @@ class GenerationIntegrationTests(unittest.TestCase):
input_ids = input_ids.expand(6, -1)
model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
input_ids = bart_model._prepare_decoder_input_ids_for_generation(
input_ids,
input_ids.shape[0],
decoder_start_token_id=bart_model.config.decoder_start_token_id,
bos_token_id=bart_model.config.bos_token_id,
)
@@ -1496,8 +1504,10 @@ class GenerationIntegrationTests(unittest.TestCase):
def test_max_length_warning_if_different(self):
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_tokenizer = BartTokenizer.from_pretrained("sshleifer/bart-tiny-random")
bart_model = BartForConditionalGeneration.from_pretrained("sshleifer/bart-tiny-random").to(torch_device)
bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
torch_device
)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
batch_size = 1
@@ -1513,7 +1523,7 @@ class GenerationIntegrationTests(unittest.TestCase):
input_ids = input_ids.expand(6, -1)
model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
input_ids = bart_model._prepare_decoder_input_ids_for_generation(
input_ids,
input_ids.shape[0],
decoder_start_token_id=bart_model.config.decoder_start_token_id,
bos_token_id=bart_model.config.bos_token_id,
)
@@ -1577,8 +1587,10 @@ class GenerationIntegrationTests(unittest.TestCase):
def test_beam_search_warning_if_max_length_is_passed(self):
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_tokenizer = BartTokenizer.from_pretrained("sshleifer/bart-tiny-random")
bart_model = BartForConditionalGeneration.from_pretrained("sshleifer/bart-tiny-random").to(torch_device)
bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
torch_device
)
batch_size = 1
num_beams = 3
@@ -1587,6 +1599,9 @@ class GenerationIntegrationTests(unittest.TestCase):
input_ids = input_ids.expand(num_beams, -1)
model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
# pretend decoder_input_ids correspond to first encoder input id
decoder_input_ids = input_ids[:, :1]
stopping_criteria_max_length = 18
stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=stopping_criteria_max_length)])
@@ -1599,7 +1614,7 @@ class GenerationIntegrationTests(unittest.TestCase):
)
generated_ids = bart_model.beam_search(
input_ids,
decoder_input_ids,
num_beams=num_beams,
stopping_criteria=stopping_criteria,
beam_scorer=beam_scorer,
@@ -1613,7 +1628,7 @@ class GenerationIntegrationTests(unittest.TestCase):
)
generated_ids_no_max_len = bart_model.beam_search(
input_ids,
decoder_input_ids,
num_beams=num_beams,
stopping_criteria=stopping_criteria,
beam_scorer=beam_scorer_no_max_len,
@@ -1625,14 +1640,17 @@ class GenerationIntegrationTests(unittest.TestCase):
def test_max_new_tokens_encoder_decoder(self):
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_tokenizer = BartTokenizer.from_pretrained("sshleifer/bart-tiny-random")
bart_model = BartForConditionalGeneration.from_pretrained("sshleifer/bart-tiny-random").to(torch_device)
bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
torch_device
)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
self.assertEqual(list(input_ids.shape), [1, 15])
self.assertEqual(list(input_ids.shape), [1, 29])
max_new_tokens = 3
bart_model.config.max_length = 20
bart_model.config.eos_token_id = None
# Encoder decoder call
outputs = bart_model.generate(input_ids, max_new_tokens=max_new_tokens)
@@ -1641,8 +1659,8 @@ class GenerationIntegrationTests(unittest.TestCase):
# Decoder only call
outputs = bart_model.generate(decoder_input_ids=input_ids, max_new_tokens=max_new_tokens)
# 15 + 3 new tokens
self.assertEqual(list(outputs.shape), [1, 18])
# 29 + 3 new tokens
self.assertEqual(list(outputs.shape), [1, 32])
# Encoder decoder call > 20
outputs = bart_model.generate(max_new_tokens=max_new_tokens + 20)
@@ -1680,3 +1698,29 @@ class GenerationIntegrationTests(unittest.TestCase):
# max_new_tokens and max_length serve the same purpose and should not be used together.
with self.assertWarns(UserWarning):
gpt2_model.generate(decoder_input_ids=input_ids, max_new_tokens=10, max_length=20)
def test_encoder_decoder_generate_with_inputs_embeds(self):
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart", max_length=5).to(
torch_device
)
model.config.eos_token_id = None
input_ids = tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
inputs_embeds = model.get_input_embeddings()(input_ids)
output_sequences = model.generate(inputs_embeds=inputs_embeds)
# make sure model generated correctly until `max_length`
self.assertEqual(output_sequences.shape, (1, 5))
def test_decoder_generate_with_inputs_embeds(self):
article = """I need input_ids to generate"""
tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
model = GPT2LMHeadModel.from_pretrained("hf-internal-testing/tiny-random-gpt2", max_length=5).to(torch_device)
input_ids = tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
inputs_embeds = model.get_input_embeddings()(input_ids)
# cannot generate from `inputs_embeds` for decoder only
with self.assertRaises(ValueError):
model.generate(inputs_embeds=inputs_embeds)