mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-17 00:23:14 +00:00
Brandon Thomas
87 lines
3.4 KiB
Python
87 lines
3.4 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. 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 os
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import tempfile
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import pytest
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import torch
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import nemo.collections.nlp as nemo_nlp
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from nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer
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from nemo.collections.nlp.modules.common.tokenizer_utils import get_tokenizer
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def do_export(model, name: str):
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with tempfile.TemporaryDirectory() as tmpdir:
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# Generate filename in the temporary directory.
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tmp_file_name = os.path.join(tmpdir, name + '.onnx')
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# Test export.
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if torch.cuda.is_available():
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model = model.cuda()
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model.export(tmp_file_name)
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class TestHuggingFace:
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@pytest.mark.unit
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def test_list_pretrained_models(self):
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pretrained_lm_models = nemo_nlp.modules.get_pretrained_lm_models_list()
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assert len(pretrained_lm_models) > 0
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@pytest.mark.with_downloads()
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@pytest.mark.unit
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def test_get_pretrained_bert_model(self):
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model = nemo_nlp.modules.get_lm_model(pretrained_model_name='bert-base-uncased')
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assert isinstance(model, nemo_nlp.modules.BertEncoder)
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do_export(model, "bert-base-uncased")
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@pytest.mark.with_downloads()
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@pytest.mark.unit
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def test_get_pretrained_distilbert_model(self):
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model = nemo_nlp.modules.get_lm_model(pretrained_model_name='distilbert-base-uncased')
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assert isinstance(model, nemo_nlp.modules.DistilBertEncoder)
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do_export(model, "distilbert-base-uncased")
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@pytest.mark.with_downloads()
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@pytest.mark.unit
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def test_get_pretrained_roberta_model(self):
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model = nemo_nlp.modules.get_lm_model(pretrained_model_name='roberta-base')
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assert isinstance(model, nemo_nlp.modules.RobertaEncoder)
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do_export(model, "roberta-base-uncased")
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@pytest.mark.with_downloads()
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@pytest.mark.unit
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def test_get_pretrained_albert_model(self):
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model = nemo_nlp.modules.get_lm_model(pretrained_model_name='albert-base-v1')
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assert isinstance(model, nemo_nlp.modules.AlbertEncoder)
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do_export(model, "albert-base-v1")
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@pytest.mark.with_downloads()
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@pytest.mark.unit
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def test_get_pretrained_chinese_bert_wwm_model(self):
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model_name = 'hfl/chinese-bert-wwm'
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model = nemo_nlp.modules.get_lm_model(pretrained_model_name=model_name)
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assert isinstance(model, nemo_nlp.modules.BertModule)
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tokenizer = get_tokenizer(tokenizer_name=model_name)
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assert isinstance(tokenizer, AutoTokenizer)
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@pytest.mark.with_downloads()
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@pytest.mark.unit
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def test_get_pretrained_arabic_model(self):
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model_name = 'asafaya/bert-base-arabic'
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model = nemo_nlp.modules.get_lm_model(pretrained_model_name=model_name)
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assert isinstance(model, nemo_nlp.modules.BertModule)
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tokenizer = get_tokenizer(tokenizer_name=model_name)
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assert isinstance(tokenizer, AutoTokenizer)
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