Add tests for Reformer tokenizer (huggingface#6485)

tests/test_tokenization_reformer.py

@@ -0,0 +1,253 @@
+# 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.
+
+
+import os
+import unittest
+
+from transformers.file_utils import cached_property
+from transformers.testing_utils import require_torch, slow
+from transformers.tokenization_reformer import SPIECE_UNDERLINE, ReformerTokenizer
+
+from .test_tokenization_common import TokenizerTesterMixin
+
+
+SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/test_sentencepiece.model")
+
+
+class ReformerTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
+
+    tokenizer_class = ReformerTokenizer
+
+    def setUp(self):
+        super().setUp()
+
+        tokenizer = ReformerTokenizer(SAMPLE_VOCAB, keep_accents=True)
+        tokenizer.save_pretrained(self.tmpdirname)
+
+    def test_full_tokenizer(self):
+        tokenizer = ReformerTokenizer(SAMPLE_VOCAB, keep_accents=True)
+
+        tokens = tokenizer.tokenize("This is a test")
+        self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
+
+        self.assertListEqual(
+            tokenizer.convert_tokens_to_ids(tokens), [285, 46, 10, 170, 382],
+        )
+
+        tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
+        self.assertListEqual(
+            tokens,
+            [
+                SPIECE_UNDERLINE + "I",
+                SPIECE_UNDERLINE + "was",
+                SPIECE_UNDERLINE + "b",
+                "or",
+                "n",
+                SPIECE_UNDERLINE + "in",
+                SPIECE_UNDERLINE + "",
+                "9",
+                "2",
+                "0",
+                "0",
+                "0",
+                ",",
+                SPIECE_UNDERLINE + "and",
+                SPIECE_UNDERLINE + "this",
+                SPIECE_UNDERLINE + "is",
+                SPIECE_UNDERLINE + "f",
+                "al",
+                "s",
+                "é",
+                ".",
+            ],
+        )
+        ids = tokenizer.convert_tokens_to_ids(tokens)
+        self.assertListEqual(
+            ids, [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],
+        )
+
+        back_tokens = tokenizer.convert_ids_to_tokens(ids)
+        self.assertListEqual(
+            back_tokens,
+            [
+                SPIECE_UNDERLINE + "I",
+                SPIECE_UNDERLINE + "was",
+                SPIECE_UNDERLINE + "b",
+                "or",
+                "n",
+                SPIECE_UNDERLINE + "in",
+                SPIECE_UNDERLINE + "",
+                "<unk>",
+                "2",
+                "0",
+                "0",
+                "0",
+                ",",
+                SPIECE_UNDERLINE + "and",
+                SPIECE_UNDERLINE + "this",
+                SPIECE_UNDERLINE + "is",
+                SPIECE_UNDERLINE + "f",
+                "al",
+                "s",
+                "<unk>",
+                ".",
+            ],
+        )
+
+    @cached_property
+    def big_tokenizer(self):
+        return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")
+
+    @slow
+    def test_tokenization_base_easy_symbols(self):
+        symbols = "Hello World!"
+        original_tokenizer_encodings = [126, 32, 262, 152, 38, 72, 287]
+
+        self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
+
+    @slow
+    def test_tokenization_base_hard_symbols(self):
+        symbols = 'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
+        original_tokenizer_encodings = [
+            108,
+            265,
+            24,
+            111,
+            4,
+            258,
+            156,
+            35,
+            28,
+            275,
+            3,
+            259,
+            297,
+            260,
+            84,
+            4,
+            35,
+            110,
+            44,
+            8,
+            259,
+            91,
+            268,
+            21,
+            11,
+            209,
+            274,
+            109,
+            266,
+            277,
+            117,
+            86,
+            93,
+            315,
+            258,
+            278,
+            258,
+            277,
+            258,
+            0,
+            258,
+            288,
+            258,
+            319,
+            258,
+            0,
+            258,
+            0,
+            258,
+            0,
+            258,
+            0,
+            258,
+            287,
+            258,
+            315,
+            258,
+            289,
+            258,
+            278,
+            99,
+            269,
+            266,
+            262,
+            8,
+            259,
+            241,
+            4,
+            217,
+            230,
+            268,
+            266,
+            55,
+            168,
+            106,
+            75,
+            193,
+            266,
+            223,
+            27,
+            49,
+            26,
+            282,
+            25,
+            264,
+            299,
+            19,
+            26,
+            0,
+            258,
+            277,
+            117,
+            86,
+            93,
+            176,
+            183,
+            270,
+            11,
+            262,
+            42,
+            61,
+            265,
+        ]
+
+        self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
+
+    @slow
+    @require_torch
+    def test_torch_encode_plus_sent_to_model(self):
+        import torch
+        from transformers import ReformerModel, ReformerConfig
+
+        # Build sequence
+        first_ten_tokens = list(self.big_tokenizer.get_vocab().keys())[:10]
+        sequence = " ".join(first_ten_tokens)
+        encoded_sequence = self.big_tokenizer.encode_plus(sequence, return_tensors="pt")
+        batch_encoded_sequence = self.big_tokenizer.batch_encode_plus([sequence, sequence], return_tensors="pt")
+
+        config = ReformerConfig()
+        # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
+        config.axial_pos_shape = encoded_sequence["input_ids"].shape
+        model = ReformerModel(config)
+
+        # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
+        assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
+
+        with torch.no_grad():
+            model(**encoded_sequence)
+            model(**batch_encoded_sequence)