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dd9d483d03
* doc
* [tests] Add sample files for a regression task
* [HUGE] Trainer
* Feedback from @sshleifer
* Feedback from @thomwolf + logging tweak
* [file_utils] when downloading concurrently, get_from_cache will use the cached file for subsequent processes
* [glue] Use default max_seq_length of 128 like before
* [glue] move DataTrainingArguments around
* [ner] Change interface of InputExample, and align run_{tf,pl}
* Re-align the pl scripts a little bit
* ner
* [ner] Add integration test
* Fix language_modeling with API tweak
* [ci] Tweak loss target
* Don't break console output
* amp.initialize: model must be on right device before
* [multiple-choice] update for Trainer
* Re-align to 827d6d6ef0
110 lines
4.6 KiB
Python
110 lines
4.6 KiB
Python
import unittest
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from transformers import AutoTokenizer, TrainingArguments, is_torch_available
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from .utils import require_torch
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if is_torch_available():
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import torch
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from transformers import (
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Trainer,
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LineByLineTextDataset,
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AutoModelForSequenceClassification,
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DefaultDataCollator,
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DataCollatorForLanguageModeling,
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GlueDataset,
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GlueDataTrainingArguments,
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TextDataset,
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)
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PATH_SAMPLE_TEXT = "./tests/fixtures/sample_text.txt"
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@require_torch
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class DataCollatorIntegrationTest(unittest.TestCase):
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def test_default_classification(self):
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MODEL_ID = "bert-base-cased-finetuned-mrpc"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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data_args = GlueDataTrainingArguments(
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task_name="mrpc", data_dir="./examples/tests_samples/MRPC", overwrite_cache=True
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)
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dataset = GlueDataset(data_args, tokenizer=tokenizer, evaluate=True)
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data_collator = DefaultDataCollator()
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batch = data_collator.collate_batch(dataset.features)
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self.assertEqual(batch["labels"].dtype, torch.long)
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def test_default_regression(self):
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MODEL_ID = "distilroberta-base"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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data_args = GlueDataTrainingArguments(
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task_name="sts-b", data_dir="./examples/tests_samples/STS-B", overwrite_cache=True
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)
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dataset = GlueDataset(data_args, tokenizer=tokenizer, evaluate=True)
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data_collator = DefaultDataCollator()
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batch = data_collator.collate_batch(dataset.features)
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self.assertEqual(batch["labels"].dtype, torch.float)
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def test_lm_tokenizer_without_padding(self):
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tokenizer = AutoTokenizer.from_pretrained("gpt2")
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data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
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# ^ causal lm
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dataset = LineByLineTextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512)
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examples = [dataset[i] for i in range(len(dataset))]
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with self.assertRaises(ValueError):
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# Expect error due to padding token missing on gpt2:
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data_collator.collate_batch(examples)
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dataset = TextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512, overwrite_cache=True)
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examples = [dataset[i] for i in range(len(dataset))]
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batch = data_collator.collate_batch(examples)
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self.assertIsInstance(batch, dict)
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 512)))
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self.assertEqual(batch["labels"].shape, torch.Size((2, 512)))
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def test_lm_tokenizer_with_padding(self):
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tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
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data_collator = DataCollatorForLanguageModeling(tokenizer)
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# ^ masked lm
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dataset = LineByLineTextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512)
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examples = [dataset[i] for i in range(len(dataset))]
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batch = data_collator.collate_batch(examples)
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self.assertIsInstance(batch, dict)
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self.assertEqual(batch["input_ids"].shape, torch.Size((31, 107)))
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self.assertEqual(batch["masked_lm_labels"].shape, torch.Size((31, 107)))
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dataset = TextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512, overwrite_cache=True)
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examples = [dataset[i] for i in range(len(dataset))]
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batch = data_collator.collate_batch(examples)
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self.assertIsInstance(batch, dict)
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 512)))
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self.assertEqual(batch["masked_lm_labels"].shape, torch.Size((2, 512)))
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@require_torch
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class TrainerIntegrationTest(unittest.TestCase):
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def test_trainer_eval_mrpc(self):
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MODEL_ID = "bert-base-cased-finetuned-mrpc"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
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data_args = GlueDataTrainingArguments(
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task_name="mrpc", data_dir="./examples/tests_samples/MRPC", overwrite_cache=True
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)
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eval_dataset = GlueDataset(data_args, tokenizer=tokenizer, evaluate=True)
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training_args = TrainingArguments(output_dir="./examples", no_cuda=True)
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trainer = Trainer(model=model, args=training_args, eval_dataset=eval_dataset)
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result = trainer.evaluate()
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self.assertLess(result["loss"], 0.2)
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def test_trainer_eval_lm(self):
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MODEL_ID = "distilroberta-base"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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dataset = LineByLineTextDataset(
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tokenizer=tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=tokenizer.max_len_single_sentence,
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)
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self.assertEqual(len(dataset), 31)
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