added tests for OpenAI GPT and Transformer-XL tokenizers

README.md

@@ -529,10 +529,10 @@ This model *outputs*:
 
 `OpenAIGPTDoubleHeadsModel` includes the `OpenAIGPTModel` Transformer followed by two heads:
 - a language modeling head with weights tied to the input embeddings (no additional parameters) and:
-- a multiple choice classifier (linear layer).
+- a multiple choice classifier (linear layer that take as input a hidden state in a sequence to compute a score, see details in paper).
 
 *Inputs* are the same as the inputs of the [`OpenAIGPTModel`](#-9.-`OpenAIGPTModel`) class plus a classification mask and two optional labels:
-- `multiple_choice_token_mask`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length] with a value of 1 were the last hidden state is (usually the [CLS] token) and 0 otherwise.
+- `multiple_choice_token_ids`: a torch.LongTensor of shape [batch_size, num_choices] with the index of the token whose hidden state should be used as input for the multiple choice classifier (usually the [CLS] token for each choice).
 - `lm_labels`: optional language modeling labels: torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss is only computed for the labels set in [0, ..., vocab_size].
 - `multiple_choice_labels`: optional multiple choice labels: torch.LongTensor of shape [batch_size] with indices selected in [0, ..., num_choices].
 
@@ -613,9 +613,9 @@ Please refer to the doc strings and code in [`tokenization_openai.py`](./pytorch
 
 #### `TransfoXLTokenizer`
 
-`TransfoXLTokenizer` perform word tokenization.
+`TransfoXLTokenizer` perform word tokenization. This tokenizer can be used for adaptive softmax and has utilities for counting tokens in a corpus to create a vocabulary ordered by toekn frequency (for adaptive softmax). See the adaptive softmax paper ([Efficient softmax approximation for GPUs](http://arxiv.org/abs/1609.04309)) for more details.
 
-Please refer to the doc strings and code in [`tokenization_transfo_xl.py`](./pytorch_pretrained_bert/tokenization_transfo_xl.py) for the details of the `TransfoXLTokenizer`.
+Please refer to the doc strings and code in [`tokenization_transfo_xl.py`](./pytorch_pretrained_bert/tokenization_transfo_xl.py) for the details of these additional methods in `TransfoXLTokenizer`.
 
 ### Optimizers:
 

pytorch_pretrained_bert/tokenization_openai.py

@@ -70,7 +70,10 @@ def text_standardize(text):
 
 class OpenAIGPTTokenizer(object):
     """
-    mostly a wrapper for a public python bpe tokenizer
+    BPE tokenizer. Peculiarities:
+        - lower case all inputs
+        - uses SpaCy tokenizer
+        - special tokens: additional symbols (ex: "__classify__") to add to a vocabulary.
     """
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
@@ -150,7 +153,7 @@ class OpenAIGPTTokenizer(object):
         logger.info("Special tokens {}".format(self.special_tokens))
 
     def bpe(self, token):
-        word = tuple(token[:-1]) + ( token[-1] + '</w>',)
+        word = tuple(token[:-1]) + (token[-1] + '</w>',)
         if token in self.cache:
             return self.cache[token]
         pairs = get_pairs(word)
@@ -159,7 +162,7 @@ class OpenAIGPTTokenizer(object):
             return token+'</w>'
 
         while True:
-            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
+            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
             if bigram not in self.bpe_ranks:
                 break
             first, second = bigram

pytorch_pretrained_bert/tokenization_transfo_xl.py

@@ -25,6 +25,7 @@ import os
 import sys
 from collections import Counter, OrderedDict
 from io import open
+import unicodedata
 
 import torch
 import numpy as np
@@ -89,8 +90,8 @@ class TransfoXLTokenizer(object):
             tokenizer.__dict__[key] = value
         return tokenizer
 
-    def __init__(self, special=[], min_freq=0, max_size=None, lower_case=True,
-                 delimiter=None, vocab_file=None):
+    def __init__(self, special=[], min_freq=0, max_size=None, lower_case=False,
+                 delimiter=None, vocab_file=None, never_split=("<unk>", "<eos>", "<formula>")):
         self.counter = Counter()
         self.special = special
         self.min_freq = min_freq
@@ -98,6 +99,7 @@ class TransfoXLTokenizer(object):
         self.lower_case = lower_case
         self.delimiter = delimiter
         self.vocab_file = vocab_file
+        self.never_split = never_split
 
     def count_file(self, path, verbose=False, add_eos=False):
         if verbose: print('counting file {} ...'.format(path))
@@ -132,7 +134,12 @@ class TransfoXLTokenizer(object):
             for line in f:
                 symb = line.strip().split()[0]
                 self.add_symbol(symb)
+        if '<UNK>' in self.sym2idx:
             self.unk_idx = self.sym2idx['<UNK>']
+        elif '<unk>' in self.sym2idx:
+            self.unk_idx = self.sym2idx['<unk>']
+        else:
+            raise ValueError('No <unkown> token in vocabulary')
 
     def build_vocab(self):
         if self.vocab_file:
@@ -198,7 +205,7 @@ class TransfoXLTokenizer(object):
             self.sym2idx[sym] = len(self.idx2sym) - 1
 
     def get_sym(self, idx):
-        assert 0 <= idx < len(self), 'Index {} out of range'.format(idx)
+        assert 0 <= idx < len(self), 'Index {} out of vocabulary range'.format(idx)
         return self.idx2sym[idx]
 
     def get_idx(self, sym):
@@ -206,9 +213,16 @@ class TransfoXLTokenizer(object):
             return self.sym2idx[sym]
         else:
             # print('encounter unk {}'.format(sym))
-            assert '<eos>' not in sym
-            assert hasattr(self, 'unk_idx')
+            # assert '<eos>' not in sym
+            if hasattr(self, 'unk_idx'):
                 return self.sym2idx.get(sym, self.unk_idx)
+            # Backward compatibility with pre-trained models
+            elif '<unk>' in self.sym2idx:
+                return self.sym2idx['<unk>']
+            elif '<UNK>' in self.sym2idx:
+                return self.sym2idx['<UNK>']
+            else:
+                raise ValueError('Token not in vocabulary and no <unk> token in vocabulary for replacement')
 
     def convert_ids_to_tokens(self, indices):
         """Converts a sequence of indices in symbols using the vocab."""
@@ -231,24 +245,82 @@ class TransfoXLTokenizer(object):
     def __len__(self):
         return len(self.idx2sym)
 
+    def _run_split_on_punc(self, text):
+        """Splits punctuation on a piece of text."""
+        if text in self.never_split:
+            return [text]
+        chars = list(text)
+        i = 0
+        start_new_word = True
+        output = []
+        while i < len(chars):
+            char = chars[i]
+            if _is_punctuation(char):
+                output.append([char])
+                start_new_word = True
+            else:
+                if start_new_word:
+                    output.append([])
+                start_new_word = False
+                output[-1].append(char)
+            i += 1
+
+        return ["".join(x) for x in output]
+
+    def _run_strip_accents(self, text):
+        """Strips accents from a piece of text."""
+        text = unicodedata.normalize("NFD", text)
+        output = []
+        for char in text:
+            cat = unicodedata.category(char)
+            if cat == "Mn":
+                continue
+            output.append(char)
+        return "".join(output)
+
+    def _clean_text(self, text):
+        """Performs invalid character removal and whitespace cleanup on text."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if cp == 0 or cp == 0xfffd or _is_control(char):
+                continue
+            if _is_whitespace(char):
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+    def whitespace_tokenize(self, text):
+        """Runs basic whitespace cleaning and splitting on a peice of text."""
+        text = text.strip()
+        if not text:
+            return []
+        if self.delimiter == '':
+            tokens = text
+        else:
+            tokens = text.split(self.delimiter)
+        return tokens
+
     def tokenize(self, line, add_eos=False, add_double_eos=False):
+        line = self._clean_text(line)
         line = line.strip()
-        # convert to lower case
-        if self.lower_case:
-            line = line.lower()
 
-        # empty delimiter '' will evaluate False
-        if self.delimiter == '':
-            symbols = line
-        else:
-            symbols = line.split(self.delimiter)
+        symbols = self.whitespace_tokenize(line)
+
+        split_symbols = []
+        for symbol in symbols:
+            if self.lower_case and symbol not in self.never_split:
+                symbol = symbol.lower()
+                symbol = self._run_strip_accents(symbol)
+            split_symbols.extend(self._run_split_on_punc(symbol))
 
         if add_double_eos: # lm1b
-            return ['<S>'] + symbols + ['<S>']
+            return ['<S>'] + split_symbols + ['<S>']
         elif add_eos:
-            return symbols + ['<eos>']
+            return split_symbols + ['<eos>']
         else:
-            return symbols
+            return split_symbols
 
 
 class LMOrderedIterator(object):
@@ -556,3 +628,42 @@ def get_lm_corpus(datadir, dataset):
         torch.save(corpus, fn)
 
     return corpus
+
+def _is_whitespace(char):
+    """Checks whether `chars` is a whitespace character."""
+    # \t, \n, and \r are technically contorl characters but we treat them
+    # as whitespace since they are generally considered as such.
+    if char == " " or char == "\t" or char == "\n" or char == "\r":
+        return True
+    cat = unicodedata.category(char)
+    if cat == "Zs":
+        return True
+    return False
+
+
+def _is_control(char):
+    """Checks whether `chars` is a control character."""
+    # These are technically control characters but we count them as whitespace
+    # characters.
+    if char == "\t" or char == "\n" or char == "\r":
+        return False
+    cat = unicodedata.category(char)
+    if cat.startswith("C"):
+        return True
+    return False
+
+
+def _is_punctuation(char):
+    """Checks whether `chars` is a punctuation character."""
+    cp = ord(char)
+    # We treat all non-letter/number ASCII as punctuation.
+    # Characters such as "^", "$", and "`" are not in the Unicode
+    # Punctuation class but we treat them as punctuation anyways, for
+    # consistency.
+    if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
+            (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
+        return True
+    cat = unicodedata.category(char)
+    if cat.startswith("P"):
+        return True
+    return False

tests/tokenization_openai_test.py

+# 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 json
+from io import open
+
+from pytorch_pretrained_bert.tokenization_openai import OpenAIGPTTokenizer
+
+
+class OpenAIGPTTokenizationTest(unittest.TestCase):
+
+    def test_full_tokenizer(self):
+        """ Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt """
+        vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n",
+                 "w</w>", "r</w>", "t</w>",
+                 "lo", "low", "er</w>",
+                 "low</w>", "lowest</w>", "newer</w>", "wider</w>"]
+        vocab_tokens = dict(zip(vocab, range(len(vocab))))
+        merges = ["#version: 0.2", "l o", "lo w", "e r</w>", ""]
+        with open("/tmp/openai_tokenizer_vocab_test.json", "w", encoding='utf-8') as fp:
+            json.dump(vocab_tokens, fp)
+            vocab_file = fp.name
+        with open("/tmp/openai_tokenizer_merges_test.txt", "w", encoding='utf-8') as fp:
+            fp.write("\n".join(merges))
+            merges_file = fp.name
+
+        tokenizer = OpenAIGPTTokenizer(vocab_file, merges_file, special_tokens=["<unk>"])
+        os.remove(vocab_file)
+        os.remove(merges_file)
+
+        text = "lower"
+        bpe_tokens = ["low", "er</w>"]
+        tokens = tokenizer.tokenize(text)
+        self.assertListEqual(tokens, bpe_tokens)
+
+        input_tokens = tokens + ["<unk>"]
+        input_bpe_tokens = [14, 15, 20]
+        self.assertListEqual(
+            tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
+
+if __name__ == '__main__':
+    unittest.main()

tests/tokenization_transfo_xl_test.py

+# 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
+from io import open
+
+from pytorch_pretrained_bert.tokenization_transfo_xl import (TransfoXLTokenizer,
+                                                  _is_control, _is_punctuation,
+                                                  _is_whitespace)
+
+
+class TransfoXLTokenizationTest(unittest.TestCase):
+
+    def test_full_tokenizer(self):
+        vocab_tokens = [
+            "<unk>", "[CLS]", "[SEP]", "want", "unwanted", "wa", "un", "running", ","
+        ]
+        with open("/tmp/transfo_xl_tokenizer_test.txt", "w", encoding='utf-8') as vocab_writer:
+            vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
+            vocab_file = vocab_writer.name
+
+        tokenizer = TransfoXLTokenizer(vocab_file=vocab_file, lower_case=True)
+        tokenizer.build_vocab()
+        os.remove(vocab_file)
+
+        tokens = tokenizer.tokenize(u"<unk> UNwant\u00E9d,running")
+        self.assertListEqual(tokens, ["<unk>", "unwanted", ",", "running"])
+
+        self.assertListEqual(
+            tokenizer.convert_tokens_to_ids(tokens), [0, 4, 8, 7])
+
+    def test_full_tokenizer_lower(self):
+        tokenizer = TransfoXLTokenizer(lower_case=True)
+
+        self.assertListEqual(
+            tokenizer.tokenize(u" \tHeLLo!how  \n Are yoU?  "),
+            ["hello", "!", "how", "are", "you", "?"])
+        self.assertListEqual(tokenizer.tokenize(u"H\u00E9llo"), ["hello"])
+
+    def test_full_tokenizer_no_lower(self):
+        tokenizer = TransfoXLTokenizer(lower_case=False)
+
+        self.assertListEqual(
+            tokenizer.tokenize(u" \tHeLLo!how  \n Are yoU?  "),
+            ["HeLLo", "!", "how", "Are", "yoU", "?"])
+
+    def test_is_whitespace(self):
+        self.assertTrue(_is_whitespace(u" "))
+        self.assertTrue(_is_whitespace(u"\t"))
+        self.assertTrue(_is_whitespace(u"\r"))
+        self.assertTrue(_is_whitespace(u"\n"))
+        self.assertTrue(_is_whitespace(u"\u00A0"))
+
+        self.assertFalse(_is_whitespace(u"A"))
+        self.assertFalse(_is_whitespace(u"-"))
+
+    def test_is_control(self):
+        self.assertTrue(_is_control(u"\u0005"))
+
+        self.assertFalse(_is_control(u"A"))
+        self.assertFalse(_is_control(u" "))
+        self.assertFalse(_is_control(u"\t"))
+        self.assertFalse(_is_control(u"\r"))
+
+    def test_is_punctuation(self):
+        self.assertTrue(_is_punctuation(u"-"))
+        self.assertTrue(_is_punctuation(u"$"))
+        self.assertTrue(_is_punctuation(u"`"))
+        self.assertTrue(_is_punctuation(u"."))
+
+        self.assertFalse(_is_punctuation(u"A"))
+        self.assertFalse(_is_punctuation(u" "))
+
+
+if __name__ == '__main__':
+    unittest.main()