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74 lines
3.1 KiB
Python
74 lines
3.1 KiB
Python
# coding=utf-8
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from transformers import is_tf_available
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from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
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if is_tf_available():
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import tensorflow as tf
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from transformers import AutoTokenizer, T5Tokenizer, TFAutoModelForSeq2SeqLM, TFMT5ForConditionalGeneration
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@require_tf
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class TFMT5ModelTest(unittest.TestCase): # no mixin with common tests -> most cases are already covered in the TF T5
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@slow
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def test_resize_embeddings(self):
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model = TFMT5ForConditionalGeneration.from_pretrained("google/mt5-small")
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original_vocab_size = model.get_input_embeddings().weight.shape[0]
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# the vocab size is defined in the model config
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self.assertEqual(original_vocab_size, model.config.vocab_size)
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tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
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tokenizer.add_special_tokens({"bos_token": "", "eos_token": ""})
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model._resize_token_embeddings(len(tokenizer))
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# the vocab size is now resized to the length of the tokenizer, which is different from the original size
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self.assertEqual(model.get_input_embeddings().weight.shape[0], len(tokenizer))
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self.assertNotEqual(model.get_input_embeddings().weight.shape[0], original_vocab_size)
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@require_tf
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@require_sentencepiece
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@require_tokenizers
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class TFMT5ModelIntegrationTest(unittest.TestCase):
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@slow
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def test_small_integration_test(self):
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"""
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For comparision run:
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>>> import t5 # pip install t5==0.7.1
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>>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary
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>>> path_to_mtf_small_mt5_checkpoint = '<fill_in>'
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>>> path_to_mtf_small_mt5_spm_model_path = '<fill_in>'
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>>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_mt5_checkpoint, batch_size=1, tpu=None)
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>>> vocab = SentencePieceVocabulary(path_to_mtf_small_mt5_spm_model_path, extra_ids=100)
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>>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
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"""
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model = TFAutoModelForSeq2SeqLM.from_pretrained("google/mt5-small")
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tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
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input_ids = tokenizer("Hello there", return_tensors="tf").input_ids
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labels = tokenizer("Hi I am", return_tensors="tf").input_ids
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loss = model(input_ids, labels=labels).loss
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mtf_score = -tf.math.reduce_mean(loss).numpy()
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EXPECTED_SCORE = -21.210594
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self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 2e-4)
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