[RoBERTa] model conversion, inference, tests 🔥

pytorch_transformers/tests/modeling_roberta_test.py

@@ -0,0 +1,69 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import (absolute_import, division, print_function,
+                        unicode_literals)
+
+import os
+import unittest
+import pytest
+import torch
+
+from pytorch_transformers.modeling_roberta import (RobertaForMaskedLM,
+                                                   RobertaModel)
+
+
+class RobertaModelTest(unittest.TestCase):
+
+    # @pytest.mark.slow
+    def test_inference_masked_lm(self):
+        model = RobertaForMaskedLM.from_pretrained('roberta-base')
+        
+        input_ids = torch.tensor([[    0, 31414,   232,   328,   740,  1140, 12695,    69, 46078,  1588,   2]])
+        output = model(input_ids)[0]
+        expected_shape = torch.Size((1, 11, 50265))
+        self.assertEqual(
+            output.shape,
+            expected_shape
+        )
+        # compare the actual values for a slice.
+        expected_slice = torch.Tensor(
+            [[[33.8843, -4.3107, 22.7779],
+              [ 4.6533, -2.8099, 13.6252],
+              [ 1.8222, -3.6898,  8.8600]]]
+        )
+        self.assertTrue(
+            torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3)
+        )
+
+    # @pytest.mark.slow
+    def test_inference_no_head(self):
+        model = RobertaModel.from_pretrained('roberta-base')
+        
+        input_ids = torch.tensor([[    0, 31414,   232,   328,   740,  1140, 12695,    69, 46078,  1588,   2]])
+        output = model(input_ids)[0]
+        # compare the actual values for a slice.
+        expected_slice = torch.Tensor(
+            [[[-0.0231,  0.0782,  0.0074],
+              [-0.1854,  0.0539, -0.0174],
+              [ 0.0548,  0.0799,  0.1687]]]
+        )
+        self.assertTrue(
+            torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3)
+        )
+
+
+
+if __name__ == '__main__':
+    unittest.main()

pytorch_transformers/tests/tokenization_roberta_test.py

@@ -0,0 +1,42 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import (absolute_import, division, print_function,
+                        unicode_literals)
+
+import os
+import unittest
+import pytest
+
+from pytorch_transformers.tokenization_roberta import RobertaTokenizer
+
+
+class RobertaTokenizationTest(unittest.TestCase):
+
+    # @pytest.mark.slow
+    def test_full_tokenizer(self):
+        tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
+        self.assertListEqual(
+            tokenizer.encode('Hello world!'),
+            [0, 31414, 232, 328, 2]
+        )
+        self.assertListEqual(
+            tokenizer.encode('Hello world! cécé herlolip'),
+            [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]
+        )
+
+
+
+if __name__ == '__main__':
+    unittest.main()