mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-19 04:28:26 +06:00
290b6e18ac
* Don't pass sampler for iterable dataset * Added check for test and eval dataloaders. * Formatting * Don't pass sampler for iterable dataset * Added check for test and eval dataloaders. * Formatting * Cleaner if nesting. * Added test for trainer and iterable dataset * Formatting for test * Fixed import when torch is available only. * Added require torch decorator to helper class * Moved dataset class inside unittest * Removed nested if and changed model in test * Checking torch availability for IterableDataset
204 lines
9.1 KiB
Python
Executable File
204 lines
9.1 KiB
Python
Executable File
import unittest
|
|
|
|
from transformers import AutoTokenizer, TrainingArguments, is_torch_available
|
|
from transformers.testing_utils import require_torch
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
from torch.utils.data import IterableDataset
|
|
|
|
from transformers import (
|
|
AutoModelForSequenceClassification,
|
|
DataCollatorForLanguageModeling,
|
|
DataCollatorForPermutationLanguageModeling,
|
|
GlueDataset,
|
|
GlueDataTrainingArguments,
|
|
LineByLineTextDataset,
|
|
TextDataset,
|
|
Trainer,
|
|
default_data_collator,
|
|
)
|
|
|
|
|
|
PATH_SAMPLE_TEXT = "./tests/fixtures/sample_text.txt"
|
|
|
|
|
|
@require_torch
|
|
class DataCollatorIntegrationTest(unittest.TestCase):
|
|
def test_default_with_dict(self):
|
|
features = [{"label": i, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
|
|
batch = default_data_collator(features)
|
|
self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8)))))
|
|
self.assertEqual(batch["labels"].dtype, torch.long)
|
|
self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
|
|
|
|
# With label_ids
|
|
features = [{"label_ids": [0, 1, 2], "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
|
|
batch = default_data_collator(features)
|
|
self.assertTrue(batch["labels"].equal(torch.tensor([[0, 1, 2]] * 8)))
|
|
self.assertEqual(batch["labels"].dtype, torch.long)
|
|
self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
|
|
|
|
# Features can already be tensors
|
|
features = [{"label": i, "inputs": torch.randint(10, [10])} for i in range(8)]
|
|
batch = default_data_collator(features)
|
|
self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8)))))
|
|
self.assertEqual(batch["labels"].dtype, torch.long)
|
|
self.assertEqual(batch["inputs"].shape, torch.Size([8, 10]))
|
|
|
|
# Labels can already be tensors
|
|
features = [{"label": torch.tensor(i), "inputs": torch.randint(10, [10])} for i in range(8)]
|
|
batch = default_data_collator(features)
|
|
self.assertEqual(batch["labels"].dtype, torch.long)
|
|
self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8)))))
|
|
self.assertEqual(batch["labels"].dtype, torch.long)
|
|
self.assertEqual(batch["inputs"].shape, torch.Size([8, 10]))
|
|
|
|
def test_default_with_no_labels(self):
|
|
features = [{"label": None, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
|
|
batch = default_data_collator(features)
|
|
self.assertTrue("labels" not in batch)
|
|
self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
|
|
|
|
# With label_ids
|
|
features = [{"label_ids": None, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
|
|
batch = default_data_collator(features)
|
|
self.assertTrue("labels" not in batch)
|
|
self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
|
|
|
|
def test_default_classification(self):
|
|
MODEL_ID = "bert-base-cased-finetuned-mrpc"
|
|
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
|
|
data_args = GlueDataTrainingArguments(
|
|
task_name="mrpc", data_dir="./tests/fixtures/tests_samples/MRPC", overwrite_cache=True
|
|
)
|
|
dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="dev")
|
|
data_collator = default_data_collator
|
|
batch = data_collator(dataset.features)
|
|
self.assertEqual(batch["labels"].dtype, torch.long)
|
|
|
|
def test_default_regression(self):
|
|
MODEL_ID = "distilroberta-base"
|
|
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
|
|
data_args = GlueDataTrainingArguments(
|
|
task_name="sts-b", data_dir="./tests/fixtures/tests_samples/STS-B", overwrite_cache=True
|
|
)
|
|
dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="dev")
|
|
data_collator = default_data_collator
|
|
batch = data_collator(dataset.features)
|
|
self.assertEqual(batch["labels"].dtype, torch.float)
|
|
|
|
def test_lm_tokenizer_without_padding(self):
|
|
tokenizer = AutoTokenizer.from_pretrained("gpt2")
|
|
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
|
|
# ^ causal lm
|
|
|
|
dataset = LineByLineTextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512)
|
|
examples = [dataset[i] for i in range(len(dataset))]
|
|
with self.assertRaises(ValueError):
|
|
# Expect error due to padding token missing on gpt2:
|
|
data_collator(examples)
|
|
|
|
dataset = TextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512, overwrite_cache=True)
|
|
examples = [dataset[i] for i in range(len(dataset))]
|
|
batch = data_collator(examples)
|
|
self.assertIsInstance(batch, dict)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 512)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((2, 512)))
|
|
|
|
def test_lm_tokenizer_with_padding(self):
|
|
tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
|
|
data_collator = DataCollatorForLanguageModeling(tokenizer)
|
|
# ^ masked lm
|
|
|
|
dataset = LineByLineTextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512)
|
|
examples = [dataset[i] for i in range(len(dataset))]
|
|
batch = data_collator(examples)
|
|
self.assertIsInstance(batch, dict)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((31, 107)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((31, 107)))
|
|
|
|
dataset = TextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512, overwrite_cache=True)
|
|
examples = [dataset[i] for i in range(len(dataset))]
|
|
batch = data_collator(examples)
|
|
self.assertIsInstance(batch, dict)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 512)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((2, 512)))
|
|
|
|
def test_plm(self):
|
|
tokenizer = AutoTokenizer.from_pretrained("xlnet-base-cased")
|
|
data_collator = DataCollatorForPermutationLanguageModeling(tokenizer)
|
|
# ^ permutation lm
|
|
|
|
dataset = LineByLineTextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512)
|
|
examples = [dataset[i] for i in range(len(dataset))]
|
|
batch = data_collator(examples)
|
|
self.assertIsInstance(batch, dict)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((31, 112)))
|
|
self.assertEqual(batch["perm_mask"].shape, torch.Size((31, 112, 112)))
|
|
self.assertEqual(batch["target_mapping"].shape, torch.Size((31, 112, 112)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((31, 112)))
|
|
|
|
dataset = TextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512, overwrite_cache=True)
|
|
examples = [dataset[i] for i in range(len(dataset))]
|
|
batch = data_collator(examples)
|
|
self.assertIsInstance(batch, dict)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 512)))
|
|
self.assertEqual(batch["perm_mask"].shape, torch.Size((2, 512, 512)))
|
|
self.assertEqual(batch["target_mapping"].shape, torch.Size((2, 512, 512)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((2, 512)))
|
|
|
|
example = [torch.randint(5, [5])]
|
|
with self.assertRaises(ValueError):
|
|
# Expect error due to odd sequence length
|
|
data_collator(example)
|
|
|
|
|
|
if is_torch_available():
|
|
|
|
class SampleIterableDataset(IterableDataset):
|
|
def __init__(self, file_path):
|
|
self.file_path = file_path
|
|
|
|
def parse_file(self):
|
|
f = open(self.file_path, "r")
|
|
return f.readlines()
|
|
|
|
def __iter__(self):
|
|
return iter(self.parse_file())
|
|
|
|
|
|
@require_torch
|
|
class TrainerIntegrationTest(unittest.TestCase):
|
|
def test_trainer_eval_mrpc(self):
|
|
MODEL_ID = "bert-base-cased-finetuned-mrpc"
|
|
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
|
|
model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
|
|
data_args = GlueDataTrainingArguments(
|
|
task_name="mrpc", data_dir="./tests/fixtures/tests_samples/MRPC", overwrite_cache=True
|
|
)
|
|
eval_dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="dev")
|
|
|
|
training_args = TrainingArguments(output_dir="./examples", no_cuda=True)
|
|
trainer = Trainer(model=model, args=training_args, eval_dataset=eval_dataset)
|
|
result = trainer.evaluate()
|
|
self.assertLess(result["eval_loss"], 0.2)
|
|
|
|
def test_trainer_eval_lm(self):
|
|
MODEL_ID = "distilroberta-base"
|
|
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
|
|
dataset = LineByLineTextDataset(
|
|
tokenizer=tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=tokenizer.max_len_single_sentence,
|
|
)
|
|
self.assertEqual(len(dataset), 31)
|
|
|
|
def test_trainer_iterable_dataset(self):
|
|
MODEL_ID = "sshleifer/tiny-distilbert-base-cased"
|
|
model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
|
|
train_dataset = SampleIterableDataset(PATH_SAMPLE_TEXT)
|
|
training_args = TrainingArguments(output_dir="./examples", no_cuda=True)
|
|
trainer = Trainer(model=model, args=training_args, train_dataset=train_dataset)
|
|
loader = trainer.get_train_dataloader()
|
|
self.assertIsInstance(loader, torch.utils.data.DataLoader)
|