wehub-resource-sync
79 lines
3.6 KiB
Python
79 lines
3.6 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2019 Hugging Face inc.
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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 copy
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import json
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import os
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import tempfile
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import unittest
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from paddlenlp.transformers import AutoModel, BertModel
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from paddlenlp.utils.env import CONFIG_NAME, PADDLE_WEIGHTS_NAME
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class AutoModelTest(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls.model = AutoModel.from_pretrained("__internal_testing__/tiny-random-bert")
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def test_from_pretrained_local(self):
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with tempfile.TemporaryDirectory() as tmp_dir:
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self.model.save_pretrained(tmp_dir)
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model = AutoModel.from_pretrained(tmp_dir)
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self.assertIsInstance(model, BertModel)
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def test_from_pretrained_no_init_class_with_model_name(self):
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with tempfile.TemporaryDirectory() as tmp_dir:
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model = copy.deepcopy(self.model)
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# when init_class is not found, we rely on the filename to get the import class
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model_save_path = os.path.join(tmp_dir, "tiny-random-bert")
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model.save_pretrained(model_save_path)
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config = model.config.to_dict()
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config.pop("architectures")
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with open(os.path.join(model_save_path, "config.json"), "w", encoding="utf-8") as writer:
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writer.write(json.dumps(config, indent=2, sort_keys=True) + "\n")
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reloaded_model = AutoModel.from_pretrained(model_save_path)
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self.assertIsInstance(reloaded_model, BertModel)
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def test_from_pretrained_no_init_class_no_model_name(self):
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with tempfile.TemporaryDirectory() as tmp_dir:
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model = copy.deepcopy(self.model)
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model.save_pretrained(tmp_dir)
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config = model.config.to_dict()
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config.pop("architectures")
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with open(os.path.join(tmp_dir, "config.json"), "w", encoding="utf-8") as writer:
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writer.write(json.dumps(config, indent=2, sort_keys=True) + "\n")
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with self.assertRaises(AttributeError):
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AutoModel.from_pretrained(tmp_dir)
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def test_model_from_pretrained_cache_dir(self):
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model_name = "__internal_testing__/tiny-random-bert"
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with tempfile.TemporaryDirectory() as tempdir:
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AutoModel.from_pretrained(model_name, cache_dir=tempdir)
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self.assertTrue(os.path.exists(os.path.join(tempdir, model_name, CONFIG_NAME)))
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self.assertTrue(os.path.exists(os.path.join(tempdir, model_name, PADDLE_WEIGHTS_NAME)))
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# check against double appending model_name in cache_dir
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self.assertFalse(os.path.exists(os.path.join(tempdir, model_name, model_name)))
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@unittest.skip("skipping due to connection error!")
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def test_from_hf_hub(self):
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model = AutoModel.from_pretrained("PaddleCI/tiny-random-bert", from_hf_hub=True, convert_from_torch=False)
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self.assertIsInstance(model, BertModel)
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@unittest.skip("skipping due to connection error!")
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def test_from_aistudio(self):
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model = AutoModel.from_pretrained("PaddleNLP/tiny-random-bert", from_aistudio=True)
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self.assertIsInstance(model, BertModel)
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