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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>
70 lines
3.0 KiB
Python
70 lines
3.0 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_torch_available
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from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
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if is_torch_available():
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import torch
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from transformers import XLMRobertaModel
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@require_sentencepiece
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@require_tokenizers
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@require_torch
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class XLMRobertaModelIntegrationTest(unittest.TestCase):
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@slow
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def test_xlm_roberta_base(self):
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model = XLMRobertaModel.from_pretrained("xlm-roberta-base")
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input_ids = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]])
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# The dog is cute and lives in the garden house
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expected_output_shape = torch.Size((1, 12, 768)) # batch_size, sequence_length, embedding_vector_dim
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expected_output_values_last_dim = torch.tensor(
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[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]
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)
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# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
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# xlmr.eval()
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# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
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output = model(input_ids)["last_hidden_state"].detach()
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self.assertEqual(output.shape, expected_output_shape)
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# compare the actual values for a slice of last dim
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self.assertTrue(torch.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))
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@slow
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def test_xlm_roberta_large(self):
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model = XLMRobertaModel.from_pretrained("xlm-roberta-large")
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input_ids = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]])
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# The dog is cute and lives in the garden house
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expected_output_shape = torch.Size((1, 12, 1024)) # batch_size, sequence_length, embedding_vector_dim
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expected_output_values_last_dim = torch.tensor(
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[[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]
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)
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# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
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# xlmr.eval()
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# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
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output = model(input_ids)["last_hidden_state"].detach()
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self.assertEqual(output.shape, expected_output_shape)
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# compare the actual values for a slice of last dim
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self.assertTrue(torch.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))
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