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Generate: make TF .generate() signature == PT .generate() signature (#21525)

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This commit is contained in:
Joao Gante
2023-02-09 11:10:13 +00:00
committed by GitHub
parent c35bb6de54
commit e69f9715eb
3 changed files with 102 additions and 67 deletions
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90
tests/generation/test_framework_agnostic.py
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@@ -396,3 +396,93 @@ class GenerationIntegrationTestsMixin:
outputs.sequences_scores = outputs.sequences_scores.cpu().numpy()
self.assertTrue(np.allclose(np.sum(transition_scores, axis=-1), outputs.sequences_scores))
def test_encoder_decoder_generate_attention_mask(self):
model_cls = self.framework_dependent_parameters["AutoModelForSeq2SeqLM"]
return_tensors = self.framework_dependent_parameters["return_tensors"]
is_pt = not model_cls.__name__.startswith("TF")
articles = ["Timberlake", "Jessica Biel, welcome to parenthood among other things"]
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
# need extreme generation values here to force this test
# to fail when `attention_mask` is not correctly treated in generate
model = model_cls.from_pretrained(
"hf-internal-testing/tiny-random-bart", max_length=50, num_beams=5, num_return_sequences=5
)
model.config.eos_token_id = None
input_ids = tokenizer(articles[0], return_tensors=return_tensors).input_ids
input_ids_batched = tokenizer(articles, padding=True, return_tensors=return_tensors).input_ids
if is_pt:
model = model.to(torch_device)
input_ids = input_ids.to(torch_device)
input_ids_batched = input_ids_batched.to(torch_device)
output_sequences_batched = model.generate(
input_ids=input_ids_batched, return_dict_in_generate=True, output_scores=True
)
output_sequences = model.generate(input_ids=input_ids, return_dict_in_generate=True, output_scores=True)
batched_out = output_sequences_batched.sequences_scores
out = output_sequences.sequences_scores
if is_pt:
batched_out = batched_out.cpu().numpy()
out = out.cpu().numpy()
diff = np.abs(np.sum(batched_out[:5]) - np.sum(out))
self.assertTrue(diff < 1e-4)
def test_generate_input_ids_as_kwarg(self):
model_cls = self.framework_dependent_parameters["AutoModelForCausalLM"]
return_tensors = self.framework_dependent_parameters["return_tensors"]
is_pt = not model_cls.__name__.startswith("TF")
article = """I need input_ids to generate"""
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2", max_length=15)
input_ids = tokenizer(article, return_tensors=return_tensors).input_ids
if is_pt:
model = model.to(torch_device)
input_ids = input_ids.to(torch_device)
output_sequences_kwargs = model.generate(input_ids=input_ids)
output_sequences = model.generate(input_ids)
if is_pt:
output_sequences_kwargs = output_sequences_kwargs.cpu().numpy()
output_sequences = output_sequences.cpu().numpy()
self.assertTrue(np.array_equal(output_sequences, output_sequences_kwargs))
self.assertEqual(output_sequences.shape, (1, 15))
def test_generate_input_ids_as_encoder_kwarg(self):
model_cls = self.framework_dependent_parameters["AutoModelForSeq2SeqLM"]
return_tensors = self.framework_dependent_parameters["return_tensors"]
is_pt = not model_cls.__name__.startswith("TF")
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
model = model_cls.from_pretrained("hf-internal-testing/tiny-random-bart", max_length=5)
model.config.eos_token_id = None
input_ids = tokenizer(article, return_tensors=return_tensors).input_ids
if is_pt:
model = model.to(torch_device)
input_ids = input_ids.to(torch_device)
output_sequences_kwargs = model.generate(input_ids=input_ids)
output_sequences = model.generate(input_ids)
if is_pt:
output_sequences_kwargs = output_sequences_kwargs.cpu().numpy()
output_sequences = output_sequences.cpu().numpy()
self.assertTrue(np.array_equal(output_sequences, output_sequences_kwargs))
self.assertEqual(output_sequences.shape, (1, 5))
def test_generate_inputs_and_encoder_kwargs(self):
model_cls = self.framework_dependent_parameters["AutoModelForCausalLM"]
return_tensors = self.framework_dependent_parameters["return_tensors"]
article = """I need input_ids to generate"""
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2", max_length=10)
input_ids = tokenizer(article, return_tensors=return_tensors).input_ids
with self.assertRaises(ValueError):
model.generate(input_ids, input_ids=input_ids)