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https://github.com/huggingface/transformers.git
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19420fd99e
* move test model folders (TODO: fix imports and others) * fix (potentially partially) imports (in model test modules) * fix (potentially partially) imports (in tokenization test modules) * fix (potentially partially) imports (in feature extraction test modules) * fix import utils.test_modeling_tf_core * fix path ../fixtures/ * fix imports about generation.test_generation_flax_utils * fix more imports * fix fixture path * fix get_test_dir * update module_to_test_file * fix get_tests_dir from wrong transformers.utils * update config.yml (CircleCI) * fix style * remove missing imports * update new model script * update check_repo * update SPECIAL_MODULE_TO_TEST_MAP * fix style * add __init__ * update self-scheduled * fix add_new_model scripts * check one way to get location back * python setup.py build install * fix import in test auto * update self-scheduled.yml * update slack notification script * Add comments about artifact names * fix for yolos Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
54 lines
2.1 KiB
Python
54 lines
2.1 KiB
Python
# 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_torch_available
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from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
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if is_torch_available():
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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@require_torch
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@require_sentencepiece
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@require_tokenizers
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class MT5IntegrationTest(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)
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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 = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-small", return_dict=True).to(torch_device)
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tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
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input_ids = tokenizer("Hello there", return_tensors="pt").input_ids
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labels = tokenizer("Hi I am", return_tensors="pt").input_ids
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loss = model(input_ids.to(torch_device), labels=labels.to(torch_device)).loss
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mtf_score = -(labels.shape[-1] * loss.item())
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EXPECTED_SCORE = -84.9127
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self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)
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