test_modeling_mt5.py

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
    from transformers import AutoModelForSeq2SeqLM, AutoTokenizer


@require_torch
@require_sentencepiece
@require_tokenizers
class MT5IntegrationTest(unittest.TestCase):
    @slow
    def test_small_integration_test(self):
        """
        For comparision run:
        >>> import t5  # pip install t5==0.7.1
        >>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary

        >>> path_to_mtf_small_mt5_checkpoint = '<fill_in>'
        >>> path_to_mtf_small_mt5_spm_model_path = '<fill_in>'
        >>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_mt5_checkpoint, batch_size=1, tpu=None)
        >>> vocab = SentencePieceVocabulary(path_to_mtf_small_mt5_spm_model_path)
        >>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
        """

        model = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-small", return_dict=True).to(torch_device)
        tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")

        input_ids = tokenizer("Hello there", return_tensors="pt").input_ids
        labels = tokenizer("Hi I am", return_tensors="pt").input_ids

        loss = model(input_ids.to(torch_device), labels=labels.to(torch_device)).loss
        mtf_score = -(labels.shape[-1] * loss.item())

        EXPECTED_SCORE = -84.9127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)