"""
Framework agnostic tests for generate()-related methods.
"""
import numpy as np
from transformers import AutoTokenizer
from transformers.testing_utils import slow, torch_device
class GenerationIntegrationTestsMixin:
# To be populated by the child classes
framework_dependent_parameters = {
"AutoModelForCausalLM": None,
"AutoModelForSpeechSeq2Seq": None,
"AutoModelForSeq2SeqLM": None,
"AutoModelForVision2Seq": None,
"LogitsProcessorList": None,
"MinLengthLogitsProcessor": None,
"create_tensor_fn": None,
"floats_tensor": None,
"return_tensors": None,
"set_seed": None,
}
def test_validate_generation_inputs(self):
model_cls = self.framework_dependent_parameters["AutoModelForSeq2SeqLM"]
return_tensors = self.framework_dependent_parameters["return_tensors"]
create_tensor_fn = self.framework_dependent_parameters["create_tensor_fn"]
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
model = model_cls.from_pretrained("hf-internal-testing/tiny-random-t5")
encoder_input_str = "Hello world"
input_ids = tokenizer(encoder_input_str, return_tensors=return_tensors).input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(ValueError, "do_samples"):
model.generate(input_ids, do_samples=True)
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(ValueError, "foo"):
fake_model_kwargs = {"foo": "bar"}
model.generate(input_ids, **fake_model_kwargs)
# however, valid model_kwargs are accepted
valid_model_kwargs = {"attention_mask": create_tensor_fn(np.zeros_like(input_ids))}
model.generate(input_ids, **valid_model_kwargs)
def test_custom_logits_processor(self):
model_cls = self.framework_dependent_parameters["AutoModelForSeq2SeqLM"]
logits_processor_list_cls = self.framework_dependent_parameters["LogitsProcessorList"]
min_length_logits_processor_cls = self.framework_dependent_parameters["MinLengthLogitsProcessor"]
return_tensors = self.framework_dependent_parameters["return_tensors"]
bart_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_model = model_cls.from_pretrained("hf-internal-testing/tiny-random-bart", min_length=1)
input_ids = bart_tokenizer(article, return_tensors=return_tensors).input_ids
logits_processor = logits_processor_list_cls()
logits_processor.append(min_length_logits_processor_cls(min_length=10, eos_token_id=0))
# it should not be allowed to both define `min_length` via config and `logits_processor` list
with self.assertRaises(ValueError):
bart_model.generate(input_ids, logits_processor=logits_processor)
bart_model.config.min_length = None
bart_model.generate(input_ids, logits_processor=logits_processor)
def test_max_new_tokens_encoder_decoder(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."""
bart_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
bart_model = model_cls.from_pretrained("hf-internal-testing/tiny-random-bart")
input_ids = bart_tokenizer(article, return_tensors=return_tensors).input_ids
if is_pt:
bart_model = bart_model.to(torch_device)
input_ids = input_ids.to(torch_device)
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)
# 1 BOS + 3 new tokens
self.assertEqual(list(outputs.shape), [1, 4])
# Decoder only call
outputs = bart_model.generate(decoder_input_ids=input_ids, max_new_tokens=max_new_tokens)
# 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)
# 1 BOS + 20 + 3 new tokens
self.assertEqual(list(outputs.shape), [1, 24])
def test_max_new_tokens_decoder_only(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 = """Justin Timberlake."""
gpt2_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
gpt2_model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2")
input_ids = gpt2_tokenizer(article, return_tensors=return_tensors).input_ids
if is_pt:
gpt2_model = gpt2_model.to(torch_device)
input_ids = input_ids.to(torch_device)
self.assertEqual(list(input_ids.shape), [1, 9])
max_new_tokens = 3
gpt2_model.config.max_length = 20
# call < 20
outputs = gpt2_model.generate(input_ids, max_new_tokens=max_new_tokens)
# 9 input_ids + 3 new tokens
self.assertEqual(list(outputs.shape), [1, 12])
# call > 20
outputs = gpt2_model.generate(max_new_tokens=max_new_tokens + 20)
# 1 BOS token + 23 new tokens
self.assertEqual(list(outputs.shape), [1, 24])
def test_encoder_decoder_generate_with_inputs_embeds(self):
model_cls = self.framework_dependent_parameters["AutoModelForSeq2SeqLM"]
return_tensors = self.framework_dependent_parameters["return_tensors"]
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
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_transition_scores_greedy_search(self):
model_cls = self.framework_dependent_parameters["AutoModelForCausalLM"]
return_tensors = self.framework_dependent_parameters["return_tensors"]
is_pt = not model_cls.__name__.startswith("TF")
articles = ["Justin Timberlake", "Michael Phelps"]
tokenizer = AutoTokenizer.from_pretrained("distilgpt2", padding_side="left")
tokenizer.pad_token = tokenizer.eos_token
model = model_cls.from_pretrained("distilgpt2")
input_ids = tokenizer(articles, return_tensors=return_tensors, padding=True).input_ids
if is_pt:
model = model.to(torch_device)
input_ids = input_ids.to(torch_device)
outputs = model.generate(
input_ids=input_ids,
max_new_tokens=5,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=None,
return_dict_in_generate=True,
output_scores=True,
)
transition_scores = model.compute_transition_scores(outputs.sequences, outputs.scores)
if is_pt:
transition_scores = transition_scores.cpu().numpy()
expected_scores = np.array(
[
[-57.8844, -60.45698, -70.16364, -65.50791, -66.35648],
[-54.417572, -60.216614, -62.661243, -58.621933, -58.298683],
]
)
self.assertTrue(np.allclose(transition_scores, expected_scores, atol=1e-3))
def test_transition_scores_greedy_search_normalized(self):
model_cls = self.framework_dependent_parameters["AutoModelForCausalLM"]
return_tensors = self.framework_dependent_parameters["return_tensors"]
is_pt = not model_cls.__name__.startswith("TF")
articles = ["Justin Timberlake", "Michael Phelps"]
tokenizer = AutoTokenizer.from_pretrained("distilgpt2", padding_side="left")
tokenizer.pad_token = tokenizer.eos_token
model = model_cls.from_pretrained("distilgpt2")
input_ids = tokenizer(articles, return_tensors=return_tensors, padding=True).input_ids
if is_pt:
model = model.to(torch_device)
input_ids = input_ids.to(torch_device)
outputs = model.generate(
input_ids=input_ids,
max_new_tokens=5,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=None,
return_dict_in_generate=True,
output_scores=True,
)
transition_scores = model.compute_transition_scores(outputs.sequences, outputs.scores, normalize_logits=True)
if is_pt:
transition_scores = transition_scores.cpu().numpy()
expected_scores = np.array(
[
[-2.538938, -2.2694316, -2.1580915, -1.572299, -2.6719835],
[-1.8826028, -2.2461371, -1.7556462, -2.9644494, -1.7996008],
]
)
self.assertTrue(np.allclose(transition_scores, expected_scores, atol=1e-3))
def test_transition_scores_beam_search_encoder_decoder(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 = [
"Justin Timberlake and Jessica Biel, welcome to parenthood.",
"Michael Phelps is arguably the most decorated Olympian of all time.",
]
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
model = model_cls.from_pretrained(
"hf-internal-testing/tiny-random-bart",
max_length=10,
num_beams=4,
num_return_sequences=2,
eos_token_id=None,
return_dict_in_generate=True,
output_scores=True,
length_penalty=0.0,
)
input_ids = tokenizer(articles, return_tensors=return_tensors, padding=True).input_ids
if is_pt:
model = model.to(torch_device)
input_ids = input_ids.to(torch_device)
outputs = model.generate(input_ids=input_ids)
transition_scores = model.compute_transition_scores(outputs.sequences, outputs.scores, outputs.beam_indices)
if is_pt:
transition_scores = transition_scores.cpu().numpy()
outputs.sequences_scores = outputs.sequences_scores.cpu().numpy()
self.assertTrue(np.allclose(np.sum(transition_scores, axis=-1), outputs.sequences_scores, atol=1e-3))
def test_transition_scores_beam_search_encoder_decoder_with_eos(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 = [
"Justin Timberlake and Jessica Biel, welcome to parenthood.",
"Michael Phelps is arguably the most decorated Olympian of all time.",
]
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
model = model_cls.from_pretrained(
"hf-internal-testing/tiny-random-bart",
max_length=10,
num_beams=4,
num_return_sequences=2,
return_dict_in_generate=True,
output_scores=True,
length_penalty=0.0,
)
input_ids = tokenizer(articles, return_tensors=return_tensors, padding=True).input_ids
if is_pt:
model = model.to(torch_device)
input_ids = input_ids.to(torch_device)
outputs = model.generate(input_ids=input_ids)
transition_scores = model.compute_transition_scores(outputs.sequences, outputs.scores, outputs.beam_indices)
if is_pt:
transition_scores = transition_scores.cpu().numpy()
outputs.sequences_scores = outputs.sequences_scores.cpu().numpy()
self.assertTrue(np.allclose(np.sum(transition_scores, axis=-1), outputs.sequences_scores, atol=1e-3))
def test_transition_scores_beam_search_decoder_only(self):
model_cls = self.framework_dependent_parameters["AutoModelForCausalLM"]
return_tensors = self.framework_dependent_parameters["return_tensors"]
is_pt = not model_cls.__name__.startswith("TF")
articles = [
"Justin Timberlake",
"Michael Phelps",
]
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
tokenizer.pad_token = tokenizer.eos_token
model = model_cls.from_pretrained(
"hf-internal-testing/tiny-random-gpt2",
max_length=10,
num_beams=4,
num_return_sequences=2,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=None,
return_dict_in_generate=True,
output_scores=True,
length_penalty=0.0,
)
input_ids = tokenizer(articles, return_tensors=return_tensors, padding=True).input_ids
if is_pt:
model = model.to(torch_device)
input_ids = input_ids.to(torch_device)
outputs = model.generate(input_ids=input_ids)
transition_scores = model.compute_transition_scores(outputs.sequences, outputs.scores, outputs.beam_indices)
if is_pt:
transition_scores = transition_scores.cpu().numpy()
outputs.sequences_scores = outputs.sequences_scores.cpu().numpy()
self.assertTrue(np.allclose(np.sum(transition_scores, axis=-1), outputs.sequences_scores, atol=1e-3))
def test_transition_scores_beam_sample_encoder_decoder(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 = [
"Justin Timberlake and Jessica Biel, welcome to parenthood.",
"Michael Phelps is arguably the most decorated Olympian of all time.",
]
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
model = model_cls.from_pretrained(
"hf-internal-testing/tiny-random-bart",
do_sample=True,
max_length=10,
num_beams=4,
num_return_sequences=2,
eos_token_id=None,
return_dict_in_generate=True,
output_scores=True,
length_penalty=0.0,
)
input_ids = tokenizer(articles, return_tensors=return_tensors, padding=True).input_ids
if is_pt:
model = model.to(torch_device)
input_ids = input_ids.to(torch_device)
outputs = model.generate(input_ids=input_ids)
transition_scores = model.compute_transition_scores(outputs.sequences, outputs.scores, outputs.beam_indices)
if is_pt:
transition_scores = transition_scores.cpu().numpy()
outputs.sequences_scores = outputs.sequences_scores.cpu().numpy()
self.assertTrue(np.allclose(np.sum(transition_scores, axis=-1), outputs.sequences_scores, atol=1e-3))
@slow
def test_transition_scores_early_stopping(self):
# This is an aggressive test that makes sure that `beam_search's`
# transition scores are computed correctly for varying `num_return_sequences`, `num_beams` and `batch_size > 1`
# 2 x input_ids for "question: How are you? \n context: I had a long day, "
model_cls = self.framework_dependent_parameters["AutoModelForSeq2SeqLM"]
create_tensor_fn = self.framework_dependent_parameters["create_tensor_fn"]
is_pt = not model_cls.__name__.startswith("TF")
input_ids = create_tensor_fn(2 * [[822, 10, 571, 33, 25, 58, 2625, 10, 27, 141, 3, 9, 307, 239, 6, 1]])
model = model_cls.from_pretrained("t5-small")
if is_pt:
model = model.to(torch_device)
input_ids = input_ids.to(torch_device)
outputs = model.generate(
input_ids,
max_length=10,
return_dict_in_generate=True,
output_scores=True,
forced_eos_token_id=model.config.eos_token_id,
num_beams=4,
do_sample=False,
num_return_sequences=3,
length_penalty=0.0,
)
transition_scores = model.compute_transition_scores(
sequences=outputs.sequences, scores=outputs.scores, beam_indices=outputs.beam_indices
)
if is_pt:
transition_scores = transition_scores.cpu().numpy()
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)
def test_generate_too_many_encoder_kwargs(self):
model_cls = self.framework_dependent_parameters["AutoModelForSeq2SeqLM"]
return_tensors = self.framework_dependent_parameters["return_tensors"]
article = """I need input_ids to generate"""
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
model = model_cls.from_pretrained("hf-internal-testing/tiny-random-bart", max_length=10)
input_ids = tokenizer(article, return_tensors=return_tensors).input_ids
with self.assertRaises(ValueError):
model.generate(input_ids=input_ids, inputs_embeds=input_ids)
def test_generate_input_features_as_encoder_kwarg(self):
model_cls = self.framework_dependent_parameters["AutoModelForSpeechSeq2Seq"]
floats_tensor = self.framework_dependent_parameters["floats_tensor"]
is_pt = not model_cls.__name__.startswith("TF")
input_features = floats_tensor((3, 80, 60))
model = model_cls.from_pretrained("hf-internal-testing/tiny-random-WhisperForConditionalGeneration")
if is_pt:
input_features.to(torch_device)
model = model.to(torch_device)
output_sequences_kwargs = model.generate(input_features=input_features, max_length=5)
output_sequences = model.generate(input_features, max_length=5)
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, (3, 5))
def test_generate_pixel_values_as_encoder_kwarg(self):
model_cls = self.framework_dependent_parameters["AutoModelForVision2Seq"]
floats_tensor = self.framework_dependent_parameters["floats_tensor"]
is_pt = not model_cls.__name__.startswith("TF")
pixel_values = floats_tensor((2, 3, 30, 30))
model = model_cls.from_pretrained("hf-internal-testing/tiny-random-VisionEncoderDecoderModel-vit-gpt2")
model.config.decoder.eos_token_id = None
if is_pt:
pixel_values = pixel_values.to(torch_device)
model = model.to(torch_device)
output_sequences_kwargs = model.generate(pixel_values=pixel_values, max_length=5)
output_sequences = model.generate(pixel_values, max_length=5)
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, (2, 5))
def test_generate_encoder_outputs_attention_mask(self):
model_cls = self.framework_dependent_parameters["AutoModelForSpeechSeq2Seq"]
floats_tensor = self.framework_dependent_parameters["floats_tensor"]
create_tensor_fn = self.framework_dependent_parameters["create_tensor_fn"]
is_pt = not model_cls.__name__.startswith("TF")
input_features = floats_tensor((3, 80, 60))
attention_mask = create_tensor_fn(np.ones(input_features.shape))
model = model_cls.from_pretrained("hf-internal-testing/tiny-random-WhisperForConditionalGeneration")
if is_pt:
input_features = input_features.to(torch_device)
attention_mask = attention_mask.to(torch_device)
model = model.to(torch_device)
encoder = model.get_encoder()
encoder_outputs = encoder(input_features)
output_sequences_no_mask = model.generate(encoder_outputs=encoder_outputs)
output_sequences_with_mask = model.generate(encoder_outputs=encoder_outputs, attention_mask=attention_mask)
if is_pt:
output_sequences_no_mask = output_sequences_no_mask.cpu().numpy()
output_sequences_with_mask = output_sequences_with_mask.cpu().numpy()
self.assertTrue(np.array_equal(output_sequences_no_mask, output_sequences_with_mask))
def test_eos_token_id_int_and_list_greedy_search(self):
model_cls = self.framework_dependent_parameters["AutoModelForCausalLM"]
return_tensors = self.framework_dependent_parameters["return_tensors"]
is_pt = not model_cls.__name__.startswith("TF")
generation_kwargs = {
"do_sample": False,
"num_beams": 1,
}
expectation = 13
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
text = """Hello, my dog is cute and"""
tokens = tokenizer(text, return_tensors=return_tensors)
model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2")
if is_pt:
model = model.to(torch_device)
tokens = tokens.to(torch_device)
eos_token_id = 873
generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs)
self.assertTrue(expectation == len(generated_tokens[0]))
eos_token_id = [873, 198]
generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs)
self.assertTrue(expectation == len(generated_tokens[0]))
def test_eos_token_id_int_and_list_contrastive_search(self):
model_cls = self.framework_dependent_parameters["AutoModelForCausalLM"]
return_tensors = self.framework_dependent_parameters["return_tensors"]
is_pt = not model_cls.__name__.startswith("TF")
generation_kwargs = {
"do_sample": False,
"num_beams": 1,
"penalty_alpha": 0.6,
"top_k": 4,
}
expectation = 17
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
text = """Hello, my dog is cute and"""
tokens = tokenizer(text, return_tensors=return_tensors)
model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2")
if is_pt:
model = model.to(torch_device)
tokens = tokens.to(torch_device)
eos_token_id = 225
generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs)
self.assertTrue(expectation == len(generated_tokens[0]))
eos_token_id = [225, 198]
generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs)
self.assertTrue(expectation == len(generated_tokens[0]))
def test_eos_token_id_int_and_list_beam_search(self):
model_cls = self.framework_dependent_parameters["AutoModelForCausalLM"]
return_tensors = self.framework_dependent_parameters["return_tensors"]
is_pt = not model_cls.__name__.startswith("TF")
generation_kwargs = {
"do_sample": False,
"num_beams": 3,
}
expectation = 13
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
text = """Hello, my dog is cute and"""
tokens = tokenizer(text, return_tensors=return_tensors)
model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2")
if is_pt:
model = model.to(torch_device)
tokens = tokens.to(torch_device)
eos_token_id = 873
generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs)
unpadded_correct_condition = expectation == len(generated_tokens[0])
padded_correct_condition = expectation < len(generated_tokens[0]) and all(
[token == model.config.pad_token_id for token in generated_tokens[0][expectation:]]
)
self.assertTrue(unpadded_correct_condition or padded_correct_condition)
eos_token_id = [873, 198]
generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs)
unpadded_correct_condition = expectation == len(generated_tokens[0])
padded_correct_condition = expectation < len(generated_tokens[0]) and all(
[token == model.config.pad_token_id for token in generated_tokens[0][expectation:]]
)
self.assertTrue(unpadded_correct_condition or padded_correct_condition)