[v0.8.x][BUGFIX] Update BERT embedding script (#1045)

* update embedding

* display tokens used in the batch, and remove hard coded values

* fix typo

* update embedding

display tokens used in the batch, and remove hard coded values

fix typo

* Update embedding.py

* fix get_model call

* Update embedding.py

* Update embedding.py

* fix download error in test

* Update test_scripts.py

scripts/bert/embedding.py

@@ -1,5 +1,3 @@
-# coding: utf-8
-
 # Licensed to the Apache Software Foundation (ASF) under one
 # or more contributor license agreements.  See the NOTICE file
 # distributed with this work for additional information
@@ -29,25 +27,14 @@
 from mxnet.gluon.data import DataLoader
 
 import gluonnlp
-from gluonnlp.data import BERTTokenizer, BERTSentenceTransform
+from gluonnlp.data import BERTTokenizer, BERTSentenceTransform, BERTSPTokenizer
 from gluonnlp.base import get_home_dir
 
 try:
     from data.embedding import BertEmbeddingDataset
 except ImportError:
     from .data.embedding import BertEmbeddingDataset
 
-try:
-    unicode
-except NameError:
-    # Define `unicode` for Python3
-    def unicode(s, *_):
-        return s
-
-
-def to_unicode(s):
-    return unicode(s, 'utf-8')
-
 
 __all__ = ['BertEmbedding']
 
@@ -75,36 +62,50 @@ class BertEmbedding:
         max length of each sequence
     batch_size : int, default 256
         batch size
+    sentencepiece : str, default None
+        Path to the sentencepiece .model file for both tokenization and vocab
     root : str, default '$MXNET_HOME/models' with MXNET_HOME defaults to '~/.mxnet'
         Location for keeping the model parameters.
     """
     def __init__(self, ctx=mx.cpu(), dtype='float32', model='bert_12_768_12',
                  dataset_name='book_corpus_wiki_en_uncased', params_path=None,
-                 max_seq_length=25, batch_size=256,
+                 max_seq_length=25, batch_size=256, sentencepiece=None,
                  root=os.path.join(get_home_dir(), 'models')):
         self.ctx = ctx
         self.dtype = dtype
         self.max_seq_length = max_seq_length
         self.batch_size = batch_size
         self.dataset_name = dataset_name
 
-        # Don't download the pretrained models if we have a parameter path
+        # use sentencepiece vocab and a checkpoint
+        # we need to set dataset_name to None, otherwise it uses the downloaded vocab
+        if params_path and sentencepiece:
+            dataset_name = None
+        else:
+            dataset_name = self.dataset_name
+        if sentencepiece:
+            vocab = gluonnlp.vocab.BERTVocab.from_sentencepiece(sentencepiece)
+        else:
+            vocab = None
         self.bert, self.vocab = gluonnlp.model.get_model(model,
-                                                         dataset_name=self.dataset_name,
+                                                         dataset_name=dataset_name,
                                                          pretrained=params_path is None,
                                                          ctx=self.ctx,
                                                          use_pooler=False,
                                                          use_decoder=False,
                                                          use_classifier=False,
-                                                         root=root)
-        self.bert.cast(self.dtype)
+                                                         root=root, vocab=vocab)
 
+        self.bert.cast(self.dtype)
         if params_path:
             logger.info('Loading params from %s', params_path)
-            self.bert.load_parameters(params_path, ctx=ctx, ignore_extra=True)
+            self.bert.load_parameters(params_path, ctx=ctx, ignore_extra=True, cast_dtype=True)
 
         lower = 'uncased' in self.dataset_name
-        self.tokenizer = BERTTokenizer(self.vocab, lower=lower)
+        if sentencepiece:
+            self.tokenizer = BERTSPTokenizer(sentencepiece, self.vocab, lower=lower)
+        else:
+            self.tokenizer = BERTTokenizer(self.vocab, lower=lower)
         self.transform = BERTSentenceTransform(tokenizer=self.tokenizer,
                                                max_seq_length=self.max_seq_length,
                                                pair=False)
@@ -153,12 +154,9 @@ def oov(self, batches, oov_way='avg'):
 
         Parameters
         ----------
-        batches : List[(tokens_id,
-                        sequence_outputs,
-                        pooled_output].
-            batch   token_ids (max_seq_length, ),
-                    sequence_outputs (max_seq_length, dim, ),
-                    pooled_output (dim, )
+        batches : List[(tokens_id, sequence_outputs)].
+            batch   token_ids shape is (max_seq_length,),
+                    sequence_outputs shape is (max_seq_length, dim)
         oov_way : str
             use **avg**, **sum** or **last** to get token embedding for those out of
             vocabulary words
@@ -169,21 +167,29 @@ def oov(self, batches, oov_way='avg'):
             List of tokens, and tokens embedding
         """
         sentences = []
+        padding_idx, cls_idx, sep_idx = None, None, None
+        if self.vocab.padding_token:
+            padding_idx = self.vocab[self.vocab.padding_token]
+        if self.vocab.cls_token:
+            cls_idx = self.vocab[self.vocab.cls_token]
+        if self.vocab.sep_token:
+            sep_idx = self.vocab[self.vocab.sep_token]
         for token_ids, sequence_outputs in batches:
             tokens = []
             tensors = []
             oov_len = 1
             for token_id, sequence_output in zip(token_ids, sequence_outputs):
-                if token_id == 1:
-                    # [PAD] token, sequence is finished.
+                # [PAD] token, sequence is finished.
+                if padding_idx and token_id == padding_idx:
                     break
-                if token_id in (2, 3):
-                    # [CLS], [SEP]
+                # [CLS], [SEP]
+                if cls_idx and token_id == cls_idx:
+                    continue
+                if sep_idx and token_id == sep_idx:
                     continue
                 token = self.vocab.idx_to_token[token_id]
-                if token.startswith('##'):
-                    token = token[2:]
-                    tokens[-1] += token
+                if not self.tokenizer.is_first_subword(token):
+                    tokens.append(token)
                     if oov_way == 'last':
                         tensors[-1] = sequence_output
                     else:
@@ -212,19 +218,21 @@ def oov(self, batches, oov_way='avg'):
     parser.add_argument('--model', type=str, default='bert_12_768_12',
                         help='pre-trained model')
     parser.add_argument('--dataset_name', type=str, default='book_corpus_wiki_en_uncased',
-                        help='dataset')
+                        help='name of the dataset used for pre-training')
     parser.add_argument('--params_path', type=str, default=None,
                         help='path to a params file to load instead of the pretrained model.')
-    parser.add_argument('--max_seq_length', type=int, default=25,
+    parser.add_argument('--sentencepiece', type=str, default=None,
+                        help='Path to the sentencepiece .model file for tokenization and vocab.')
+    parser.add_argument('--max_seq_length', type=int, default=128,
                         help='max length of each sequence')
     parser.add_argument('--batch_size', type=int, default=256,
                         help='batch size')
     parser.add_argument('--oov_way', type=str, default='avg',
-                        help='how to handle oov\n'
-                             'avg: average all oov embeddings to represent the original token\n'
-                             'sum: sum all oov embeddings to represent the original token\n'
-                             'last: use last oov embeddings to represent the original token\n')
-    parser.add_argument('--sentences', type=to_unicode, nargs='+', default=None,
+                        help='how to handle subword embeddings\n'
+                             'avg: average all subword embeddings to represent the original token\n'
+                             'sum: sum all subword embeddings to represent the original token\n'
+                             'last: use last subword embeddings to represent the original token\n')
+    parser.add_argument('--sentences', type=str, nargs='+', default=None,
                         help='sentence for encoding')
     parser.add_argument('--file', type=str, default=None,
                         help='file for encoding')
@@ -240,7 +248,8 @@ def oov(self, batches, oov_way='avg'):
     else:
         context = mx.cpu()
     bert_embedding = BertEmbedding(ctx=context, model=args.model, dataset_name=args.dataset_name,
-                                   max_seq_length=args.max_seq_length, batch_size=args.batch_size)
+                                   max_seq_length=args.max_seq_length, batch_size=args.batch_size,
+                                   params_path=args.params_path, sentencepiece=args.sentencepiece)
     result = []
     sents = []
     if args.sentences:
@@ -255,7 +264,7 @@ def oov(self, batches, oov_way='avg'):
         logger.error('Please specify --sentence or --file')
 
     if result:
-        for sent, embeddings in zip(sents, result):
-            print('Text: {}'.format(sent))
-            _, tokens_embedding = embeddings
+        for _, embeddings in zip(sents, result):
+            sent, tokens_embedding = embeddings
+            print('Text: {}'.format(' '.join(sent)))
             print('Tokens embedding: {}'.format(tokens_embedding))

scripts/bert/finetune_classifier.py

@@ -219,7 +219,7 @@
     except ImportError:
         # amp is not available
         logging.info('Mixed precision training with float16 requires MXNet >= '
-                     '1.5.0b20190627. Please consider upgrading your MXNet version.')
+                     '1.5.1. Please consider upgrading your MXNet version.')
         exit()
 
 # model and loss

scripts/bert/index.rst

@@ -278,8 +278,8 @@ The goal of this BERT Embedding is to obtain the token embedding from BERT's pre
 
 .. code-block:: shell
 
-    python bert/embedding.py --sentences "GluonNLP is a toolkit that enables easy text preprocessing, datasets loading and neural models building to help you speed up your Natural Language Processing (NLP) research."
-    Text: GluonNLP is a toolkit that enables easy text preprocessing, datasets loading and neural models building to help you speed up your Natural Language Processing (NLP) research.
+    python embedding.py --sentences "GluonNLP is a toolkit that enables easy text preprocessing, datasets loading and neural models building to help you speed up your Natural Language Processing (NLP) research."
+    Text: g ##lu ##on ##nl ##p is a tool ##kit that enables easy text prep ##ro ##ces ##sing , data ##set ##s loading and neural models building to help you speed up your natural language processing ( nl ##p ) research .
     Tokens embedding: [array([-0.11881411, -0.59530115,  0.627092  , ...,  0.00648153,
        -0.03886228,  0.03406909], dtype=float32), array([-0.7995638 , -0.6540758 , -0.00521846, ..., -0.42272145,
        -0.5787281 ,  0.7021201 ], dtype=float32), array([-0.7406778 , -0.80276626,  0.3931962 , ..., -0.49068323,

scripts/tests/test_scripts.py

@@ -25,6 +25,7 @@
 
 import pytest
 import mxnet as mx
+import gluonnlp as nlp
 
 @pytest.mark.serial
 @pytest.mark.remote_required
@@ -200,8 +201,10 @@ def test_bert_embedding(use_pretrained):
     if use_pretrained:
         args.extend(['--dtype', 'float32'])
     else:
+        _, _ = nlp.model.get_model('bert_12_768_12', dataset_name='book_corpus_wiki_en_uncased',
+                                   pretrained=True, root='test_bert_embedding')
         args.extend(['--params_path',
-                     '~/.mxnet/models/bert_12_768_12_book_corpus_wiki_en_uncased-75cc780f.params'])
+                     'test_bert_embedding/bert_12_768_12_book_corpus_wiki_en_uncased-75cc780f.params'])
     process = subprocess.check_call([sys.executable, './scripts/bert/embedding.py'] + args)
     time.sleep(5)