Updated test architecture

pytorch_transformers/tests/modeling_roberta_test.py

@@ -19,8 +19,9 @@ from __future__ import print_function
 import unittest
 import shutil
 import pytest
+import torch
 
-from pytorch_transformers import (RobertaConfig, RobertaModel, RobertaForMaskedLM)
+from pytorch_transformers import (RobertaConfig, RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification)
 from pytorch_transformers.modeling_roberta import ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
 
 from .modeling_common_test import (CommonTestCases, ConfigTester, ids_tensor)
@@ -156,6 +157,42 @@ class RobertaModelTest(CommonTestCases.CommonModelTester):
             inputs_dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
             return config, inputs_dict
 
+        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)
+            )
+
     def setUp(self):
         self.model_tester = RobertaModelTest.RobertaModelTester(self)
         self.config_tester = ConfigTester(self, config_class=RobertaConfig, hidden_size=37)
@@ -183,7 +220,7 @@ class RobertaModelTest(CommonTestCases.CommonModelTester):
 
 class RobertaModelIntegrationTest(unittest.TestCase):
 
-    @pytest.mark.slow
+    # @pytest.mark.slow
     def test_inference_masked_lm(self):
         model = RobertaForMaskedLM.from_pretrained('roberta-base')
         
@@ -204,7 +241,7 @@ class RobertaModelIntegrationTest(unittest.TestCase):
             torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3)
         )
 
-    @pytest.mark.slow
+    # @pytest.mark.slow
     def test_inference_no_head(self):
         model = RobertaModel.from_pretrained('roberta-base')
         

pytorch_transformers/tests/tokenization_roberta_test.py

@@ -15,42 +15,68 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
 
 import os
+import json
 import unittest
 
-from pytorch_transformers.tokenization_roberta import RobertaTokenizer, VOCAB_FILES_NAMES
+from pytorch_transformers.tokenization_roberta import RobertaTokenizer, DICT_FILES_NAMES
-from .tokenization_tests_commons import create_and_check_tokenizer_commons, TemporaryDirectory
+from pytorch_transformers.tokenization_gpt2 import GPT2Tokenizer, VOCAB_FILES_NAMES
+from .tokenization_tests_commons import CommonTestCases
 
 
-class RobertaTokenizationTest(unittest.TestCase):
+class RobertaTokenizationTest(CommonTestCases.CommonTokenizerTester):
+    tokenizer_class = RobertaTokenizer
 
-    def test_full_tokenizer(self):
+    def setUp(self):
-        """ Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt """
+        super(RobertaTokenizationTest, self).setUp()
+
+        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n",
                  "lo", "low", "er",
                  "low", "lowest", "newer", "wider", "<unk>"]
         vocab_tokens = dict(zip(vocab, range(len(vocab))))
-        special_tokens_map = {"unk_token": "<unk>"}
+        merges = ["#version: 0.2", "l o", "lo w", "e r", ""]
+        self.special_tokens_map = {"unk_token": "<unk>"}
 
-        with TemporaryDirectory() as tmpdirname:
+        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
-            vocab_file = os.path.join(tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
+        self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['merges_file'])
-            with open(vocab_file, "w") as fp:
+        with open(self.vocab_file, "w") as fp:
-                [fp.write(f"{vocab} {index}\n") for index, vocab in enumerate(vocab_tokens)]
+            fp.write(json.dumps(vocab_tokens))
+        with open(self.merges_file, "w") as fp:
+            fp.write("\n".join(merges))
 
-            input_text = u"lower newer"
+    def get_tokenizer(self):
-            output_text = u"lower<unk>newer"
+        bpe_tokenizer = GPT2Tokenizer.from_pretrained(self.tmpdirname, **self.special_tokens_map)
+        return RobertaTokenizer.from_pretrained("roberta-base", bpe_tokenizer=bpe_tokenizer)
 
-            create_and_check_tokenizer_commons(self, input_text, output_text, RobertaTokenizer, tmpdirname, **special_tokens_map)
+    def get_input_output_texts(self):
+        input_text = u"lower newer"
+        output_text = u"lower<unk>newer"
+        return input_text, output_text
 
-            tokenizer = RobertaTokenizer(vocab_file, **special_tokens_map)
+    def test_full_tokenizer(self):
-            text = "lower"
+        tokenizer = self.get_tokenizer()
-            bpe_tokens = ["low", "er"]
+        text = "lower"
-            tokens = tokenizer.tokenize(text)
+        bpe_tokens = ["low", "er"]
-            self.assertListEqual(tokens, bpe_tokens)
+        tokens = tokenizer.tokenize(text)
+        self.assertListEqual(tokens, bpe_tokens)
 
-            input_tokens = tokens + [tokenizer.unk_token]
+        input_tokens = tokens + [tokenizer.unk_token]
-            input_bpe_tokens = [13, 12, 17]
+        input_bpe_tokens = [0, 4, 12, 176, 2]
-            self.assertListEqual(
+        tokenizer.convert_tokens_to_ids(input_tokens)
-                tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
+        self.assertListEqual(
+            tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
+
+    def roberta_dict_integration_testing(self):
+        tokenizer = self.get_tokenizer()
+
+        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__':

pytorch_transformers/tests/tokenization_tests_commons.py

@@ -105,7 +105,7 @@ class CommonTestCases:
             self.assertEqual(added_toks, len(new_toks))
             self.assertEqual(all_size_2, all_size + len(new_toks))
 
-            tokens = tokenizer.encode("aaaaabbbbbb low cccccccccdddddddd l")
+            tokens = tokenizer.encode("aaaaabbbbbb low cccccccccdddddddd l", no_sep_cls_tokens=True)
             self.assertGreaterEqual(len(tokens), 4)
             self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
             self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
@@ -121,7 +121,8 @@ class CommonTestCases:
             self.assertEqual(added_toks_2, len(new_toks_2))
             self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
 
-            tokens = tokenizer.encode(">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l")
+            tokens = tokenizer.encode(">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l",
+                                      no_sep_cls_tokens=True)
 
             self.assertGreaterEqual(len(tokens), 6)
             self.assertGreater(tokens[0], tokenizer.vocab_size - 1)