update tokenizer - update squad example for xlnet

examples/run_glue.py

@@ -242,7 +242,7 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False):
     # Load data features from cache or dataset file
     cached_features_file = os.path.join(args.data_dir, 'cached_{}_{}_{}_{}'.format(
         'dev' if evaluate else 'train',
-        list(filter(None, args.model_name.split('/'))).pop(),
+        list(filter(None, args.model_name_or_path.split('/'))).pop(),
         str(args.max_seq_length),
         str(task)))
     if os.path.exists(cached_features_file):
@@ -282,8 +282,10 @@ def main():
     ## Required parameters
     parser.add_argument("--data_dir", default=None, type=str, required=True,
                         help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
-    parser.add_argument("--model_name", default=None, type=str, required=True,
-                        help="Bert/XLNet/XLM pre-trained model selected in the list: " + ", ".join(ALL_MODELS))
+    parser.add_argument("--model_type", default=None, type=str, required=True,
+                        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
+    parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
+                        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
     parser.add_argument("--task_name", default=None, type=str, required=True,
                         help="The name of the task to train selected in the list: " + ", ".join(processors.keys()))
     parser.add_argument("--output_dir", default=None, type=str, required=True,
@@ -400,15 +402,11 @@ def main():
     if args.local_rank not in [-1, 0]:
         torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
 
-    args.model_type = ""
-    for key in MODEL_CLASSES:
-        if key in args.model_name.lower():
-            args.model_type = key  # take the first match in model types
-            break
+    args.model_type = args.model_type.lower()
     config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name, num_labels=num_labels, finetuning_task=args.task_name)
-    tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name, do_lower_case=args.do_lower_case)
-    model = model_class.from_pretrained(args.model_name, from_tf=bool('.ckpt' in args.model_name), config=config)
+    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name)
+    tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case)
+    model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config)
 
     if args.local_rank == 0:
         torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab

examples/run_squad.py

@@ -213,7 +213,6 @@ def evaluate(args, model, tokenizer, prefix=""):
                 inputs.update({'cls_index': batch[4],
                                'p_mask':    batch[5]})
             outputs = model(**inputs)
-            batch_start_logits, batch_end_logits = outputs[:2]
 
         for i, example_index in enumerate(example_indices):
             eval_feature = features[example_index.item()]
@@ -242,7 +241,8 @@ def evaluate(args, model, tokenizer, prefix=""):
         write_predictions_extended(examples, features, all_results, args.n_best_size,
                         args.max_answer_length, output_prediction_file,
                         output_nbest_file, output_null_log_odds_file, args.predict_file,
-                        args.start_n_top, args.end_n_top, args.version_2_with_negative)
+                        model.config.start_n_top, model.config.end_n_top,
+                        args.version_2_with_negative, tokenizer, args.verbose_logging)
     else:
         write_predictions(examples, features, all_results, args.n_best_size,
                         args.max_answer_length, args.do_lower_case, output_prediction_file,
@@ -262,7 +262,7 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal
     input_file = args.predict_file if evaluate else args.train_file
     cached_features_file = os.path.join(os.path.dirname(input_file), 'cached_{}_{}_{}'.format(
         'dev' if evaluate else 'train',
-        list(filter(None, args.model_name.split('/'))).pop(),
+        list(filter(None, args.model_name_or_path.split('/'))).pop(),
         str(args.max_seq_length)))
     if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples:
         logger.info("Loading features from cached file %s", cached_features_file)
@@ -312,8 +312,10 @@ def main():
                         help="SQuAD json for training. E.g., train-v1.1.json")
     parser.add_argument("--predict_file", default=None, type=str, required=True,
                         help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
-    parser.add_argument("--model_name", default=None, type=str, required=True,
-                        help="Bert/XLNet/XLM pre-trained model selected in the list: " + ", ".join(ALL_MODELS))
+    parser.add_argument("--model_type", default=None, type=str, required=True,
+                        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
+    parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
+                        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
     parser.add_argument("--output_dir", default=None, type=str, required=True,
                         help="The output directory where the model checkpoints and predictions will be written.")
 
@@ -438,15 +440,11 @@ def main():
     if args.local_rank not in [-1, 0]:
         torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
 
-    args.model_type = ""
-    for key in MODEL_CLASSES:
-        if key in args.model_name.lower():
-            args.model_type = key  # take the first match in model types
-            break
+    args.model_type = args.model_type.lower()
     config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name)
-    tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name, do_lower_case=args.do_lower_case)
-    model = model_class.from_pretrained(args.model_name, from_tf=bool('.ckpt' in args.model_name), config=config)
+    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)
+    tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case)
+    model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config)
 
     if args.local_rank == 0:
         torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab

examples/test_examples.py

@@ -60,8 +60,9 @@ class ExamplesTests(unittest.TestCase):
                     "--warmup_steps=2",
                     "--overwrite_output_dir",
                     "--seed=42"]
-        model_name = "--model_name=bert-base-uncased"
-        with patch.object(sys, 'argv', testargs + [model_name]):
+        model_type, model_name = ("--model_type=bert",
+                                  "--model_name_or_path=bert-base-uncased")
+        with patch.object(sys, 'argv', testargs + [model_type, model_name]):
             result = run_glue.main()
             for value in result.values():
                 self.assertGreaterEqual(value, 0.75)
@@ -85,8 +86,9 @@ class ExamplesTests(unittest.TestCase):
                     "--per_gpu_eval_batch_size=1",
                     "--overwrite_output_dir",
                     "--seed=42"]
-        model_name = "--model_name=bert-base-uncased"
-        with patch.object(sys, 'argv', testargs + [model_name]):
+        model_type, model_name = ("--model_type=bert",
+                                  "--model_name_or_path=bert-base-uncased")
+        with patch.object(sys, 'argv', testargs + [model_type, model_name]):
             result = run_squad.main()
             self.assertGreaterEqual(result['f1'], 30)
             self.assertGreaterEqual(result['exact'], 30)

examples/utils_squad.py

@@ -87,6 +87,7 @@ class InputFeatures(object):
                  segment_ids,
                  cls_index,
                  p_mask,
+                 paragraph_len,
                  start_position=None,
                  end_position=None,
                  is_impossible=None):
@@ -101,6 +102,7 @@ class InputFeatures(object):
         self.segment_ids = segment_ids
         self.cls_index = cls_index
         self.p_mask = p_mask
+        self.paragraph_len = paragraph_len
         self.start_position = start_position
         self.end_position = end_position
         self.is_impossible = is_impossible
@@ -292,6 +294,7 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
                 tokens.append(all_doc_tokens[split_token_index])
                 segment_ids.append(sequence_b_segment_id)
                 p_mask.append(0)
+            paragraph_len = doc_span.length
 
             # SEP token
             tokens.append(sep_token)
@@ -385,6 +388,7 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
                     segment_ids=segment_ids,
                     cls_index=cls_index,
                     p_mask=p_mask,
+                    paragraph_len=paragraph_len,
                     start_position=start_position,
                     end_position=end_position,
                     is_impossible=span_is_impossible))
@@ -673,8 +677,9 @@ RawResultExtended = collections.namedtuple("RawResultExtended",
 def write_predictions_extended(all_examples, all_features, all_results, n_best_size,
                                 max_answer_length, output_prediction_file,
                                 output_nbest_file,
-                                output_null_log_odds_file, orig_data,
-                                start_n_top, end_n_top, version_2_with_negative):
+                                output_null_log_odds_file, orig_data_file,
+                                start_n_top, end_n_top, version_2_with_negative,
+                                tokenizer, verbose_logging):
     """ XLNet write prediction logic (more complex than Bert's).
         Write final predictions to the json file and log-odds of null if needed.
 
@@ -764,13 +769,30 @@ def write_predictions_extended(all_examples, all_features, all_results, n_best_s
                 break
             feature = features[pred.feature_index]
 
-            tok_start_to_orig_index = feature.tok_start_to_orig_index
-            tok_end_to_orig_index = feature.tok_end_to_orig_index
-            start_orig_pos = tok_start_to_orig_index[pred.start_index]
-            end_orig_pos = tok_end_to_orig_index[pred.end_index]
-
-            paragraph_text = example.paragraph_text
-            final_text = paragraph_text[start_orig_pos: end_orig_pos + 1].strip()
+            # XLNet un-tokenizer
+            # Let's keep it simple for now and see if we need all this later.
+            # 
+            # tok_start_to_orig_index = feature.tok_start_to_orig_index
+            # tok_end_to_orig_index = feature.tok_end_to_orig_index
+            # start_orig_pos = tok_start_to_orig_index[pred.start_index]
+            # end_orig_pos = tok_end_to_orig_index[pred.end_index]
+            # paragraph_text = example.paragraph_text
+            # final_text = paragraph_text[start_orig_pos: end_orig_pos + 1].strip()
+
+            # Previously used Bert untokenizer
+            tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
+            orig_doc_start = feature.token_to_orig_map[pred.start_index]
+            orig_doc_end = feature.token_to_orig_map[pred.end_index]
+            orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
+            tok_text = tokenizer.convert_tokens_to_string(tok_tokens)
+
+            # Clean whitespace
+            tok_text = tok_text.strip()
+            tok_text = " ".join(tok_text.split())
+            orig_text = " ".join(orig_tokens)
+
+            final_text = get_final_text(tok_text, orig_text, tokenizer.do_lower_case,
+                                        verbose_logging)
 
             if final_text in seen_predictions:
                 continue
@@ -829,6 +851,9 @@ def write_predictions_extended(all_examples, all_features, all_results, n_best_s
         with open(output_null_log_odds_file, "w") as writer:
             writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
 
+    with open(orig_data_file, "r", encoding='utf-8') as reader:
+        orig_data = json.load(reader)["data"]
+
     qid_to_has_ans = make_qid_to_has_ans(orig_data)
     has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
     no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]

pytorch_transformers/modeling_utils.py

@@ -528,9 +528,9 @@ class PoolerEndLogits(nn.Module):
                 Mask of invalid position such as query and special symbols (PAD, SEP, CLS)
                 1.0 means token should be masked.
         """
-        slen, hsz = hidden_states.shape[-2:]
         assert start_states is not None or start_positions is not None, "One of start_states, start_positions should be not None"
         if start_positions is not None:
+            slen, hsz = hidden_states.shape[-2:]
             start_positions = start_positions[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz)
             start_states = hidden_states.gather(-2, start_positions) # shape (bsz, 1, hsz)
             start_states = start_states.expand(-1, slen, -1) # shape (bsz, slen, hsz)
@@ -571,7 +571,7 @@ class PoolerAnswerClass(nn.Module):
                 no dependency on end_feature so that we can obtain one single `cls_logits`
                 for each sample
         """
-        slen, hsz = hidden_states.shape[-2:]
+        hsz = hidden_states.shape[-1]
         assert start_states is not None or start_positions is not None, "One of start_states, start_positions should be not None"
         if start_positions is not None:
             start_positions = start_positions[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz)
@@ -614,12 +614,21 @@ class SQuADHead(nn.Module):
     Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
         **loss**: (`optional`, returned if both ``start_positions`` and ``end_positions`` are provided) ``torch.FloatTensor`` of shape ``(1,)``:
             Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses.
-        **last_hidden_state**: `(`optional`, returned if ``start_positions`` or ``end_positions`` is not provided) `torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
-            Sequence of hidden-states at the last layer of the model.
-        **mems**:
-            list of ``torch.FloatTensor`` (one for each layer):
-            that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
-            (see `mems` input above). Can be used to speed up sequential decoding and attend to longer context.
+        **start_top_log_probs**: `(`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
+            ``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top)``
+            Log probabilities for the top config.start_n_top start token possibilities (beam-search).
+        **start_top_index**: `(`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
+            ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top)``
+            Indices for the top config.start_n_top start token possibilities (beam-search).
+        **end_top_log_probs**: `(`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
+            ``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
+            Log probabilities for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
+        **end_top_index**: `(`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
+            ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
+            Indices for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
+        **cls_logits**: `(`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
+            ``torch.FloatTensor`` of shape ``(batch_size,)``
+            Log probabilities for the ``is_impossible`` label of the answers.
     """
     def __init__(self, config):
         super(SQuADHead, self).__init__()
@@ -667,8 +676,8 @@ class SQuADHead(nn.Module):
             start_log_probs = F.softmax(start_logits, dim=-1) # shape (bsz, slen)
 
             start_top_log_probs, start_top_index = torch.topk(start_log_probs, self.start_n_top, dim=-1) # shape (bsz, start_n_top)
-            start_top_index = start_top_index.unsqueeze(-1).expand(-1, -1, hsz) # shape (bsz, start_n_top, hsz)
-            start_states = torch.gather(hidden_states, -2, start_top_index) # shape (bsz, start_n_top, hsz)
+            start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz) # shape (bsz, start_n_top, hsz)
+            start_states = torch.gather(hidden_states, -2, start_top_index_exp) # shape (bsz, start_n_top, hsz)
             start_states = start_states.unsqueeze(1).expand(-1, slen, -1, -1) # shape (bsz, slen, start_n_top, hsz)
 
             hidden_states_expanded = hidden_states.unsqueeze(2).expand_as(start_states) # shape (bsz, slen, start_n_top, hsz)

pytorch_transformers/modeling_xlnet.py

@@ -1167,12 +1167,23 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
             1.0 means token should be masked. 0.0 mean token is not masked.
 
     Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
-        **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
-            Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
-        **start_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
-            Span-start scores (before SoftMax).
-        **end_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
-            Span-end scores (before SoftMax).
+        **loss**: (`optional`, returned if both ``start_positions`` and ``end_positions`` are provided) ``torch.FloatTensor`` of shape ``(1,)``:
+            Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses.
+        **start_top_log_probs**: `(`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
+            ``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top)``
+            Log probabilities for the top config.start_n_top start token possibilities (beam-search).
+        **start_top_index**: `(`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
+            ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top)``
+            Indices for the top config.start_n_top start token possibilities (beam-search).
+        **end_top_log_probs**: `(`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
+            ``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
+            Log probabilities for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
+        **end_top_index**: `(`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
+            ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
+            Indices for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
+        **cls_logits**: `(`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
+            ``torch.FloatTensor`` of shape ``(batch_size,)``
+            Log probabilities for the ``is_impossible`` label of the answers.
         **mems**:
             list of ``torch.FloatTensor`` (one for each layer):
             that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
@@ -1243,12 +1254,10 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
                 loss_fct_cls = nn.BCEWithLogitsLoss()
                 cls_loss = loss_fct_cls(cls_logits, is_impossible)
 
-                # note(zhiliny): by default multiply the loss by 0.5 so that the scale is
-                # comparable to start_loss and end_loss
+                # note(zhiliny): by default multiply the loss by 0.5 so that the scale is comparable to start_loss and end_loss
                 total_loss += cls_loss * 0.5
-                outputs = (total_loss, start_logits, end_logits, cls_logits) + outputs
-            else:
-                outputs = (total_loss, start_logits, end_logits) + outputs
+
+            outputs = (total_loss,) + outputs
 
         else:
             # during inference, compute the end logits based on beam search
@@ -1256,8 +1265,8 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
             start_log_probs = F.softmax(start_logits, dim=-1) # shape (bsz, slen)
 
             start_top_log_probs, start_top_index = torch.topk(start_log_probs, self.start_n_top, dim=-1) # shape (bsz, start_n_top)
-            start_top_index = start_top_index.unsqueeze(-1).expand(-1, -1, hsz) # shape (bsz, start_n_top, hsz)
-            start_states = torch.gather(hidden_states, -2, start_top_index) # shape (bsz, start_n_top, hsz)
+            start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz) # shape (bsz, start_n_top, hsz)
+            start_states = torch.gather(hidden_states, -2, start_top_index_exp) # shape (bsz, start_n_top, hsz)
             start_states = start_states.unsqueeze(1).expand(-1, slen, -1, -1) # shape (bsz, slen, start_n_top, hsz)
 
             hidden_states_expanded = hidden_states.unsqueeze(2).expand_as(start_states) # shape (bsz, slen, start_n_top, hsz)
@@ -1269,11 +1278,11 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
             end_top_log_probs = end_top_log_probs.view(-1, self.start_n_top * self.end_n_top)
             end_top_index = end_top_index.view(-1, self.start_n_top * self.end_n_top)
 
-            start_states = torch.einsum("blh,bl->bh", hidden_states, start_log_probs)
-            cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index)
+            start_states = torch.einsum("blh,bl->bh", hidden_states, start_log_probs)  # get the representation of START as weighted sum of hidden states
+            cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index)  # Shape (batch size,): one single `cls_logits` for each sample
 
             outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits) + outputs
 
-        # return start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits, mems, (hidden states), (attentions)
-        # or (if labels are provided) total_loss, start_logits, end_logits, (cls_logits), mems, (hidden states), (attentions)
+        # return start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits
+        # or (if labels are provided) (total_loss,)
         return outputs

pytorch_transformers/tests/tokenization_bert_test.py

@@ -38,7 +38,10 @@ class TokenizationTest(unittest.TestCase):
             with open(vocab_file, "w", encoding='utf-8') as vocab_writer:
                 vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
 
-            create_and_check_tokenizer_commons(self, BertTokenizer, tmpdirname)
+            input_text = u"UNwant\u00E9d,running"
+            output_text = u"unwanted, running"
+
+            create_and_check_tokenizer_commons(self, input_text, output_text, BertTokenizer, tmpdirname)
 
             tokenizer = BertTokenizer(vocab_file)
 

pytorch_transformers/tests/tokenization_gpt2_test.py

@@ -41,7 +41,10 @@ class GPT2TokenizationTest(unittest.TestCase):
             with open(merges_file, "w") as fp:
                 fp.write("\n".join(merges))
 
-            create_and_check_tokenizer_commons(self, GPT2Tokenizer, tmpdirname, **special_tokens_map)
+            input_text = u"lower newer"
+            output_text = u"lower<unk>newer"
+
+            create_and_check_tokenizer_commons(self, input_text, output_text, GPT2Tokenizer, tmpdirname, **special_tokens_map)
 
             tokenizer = GPT2Tokenizer(vocab_file, merges_file, **special_tokens_map)
             text = "lower"

pytorch_transformers/tests/tokenization_openai_test.py

@@ -42,7 +42,10 @@ class OpenAIGPTTokenizationTest(unittest.TestCase):
             with open(merges_file, "w") as fp:
                 fp.write("\n".join(merges))
 
-            create_and_check_tokenizer_commons(self, OpenAIGPTTokenizer, tmpdirname)
+            input_text = u"lower newer"
+            output_text = u"lower newer"
+
+            create_and_check_tokenizer_commons(self, input_text, output_text, OpenAIGPTTokenizer, tmpdirname)
 
             tokenizer = OpenAIGPTTokenizer(vocab_file, merges_file)
 

pytorch_transformers/tests/tokenization_tests_commons.py

@@ -113,23 +113,24 @@ def create_and_check_add_tokens_tokenizer(tester, tokenizer_class, *inputs, **kw
     tester.assertEqual(tokens[-2], tokenizer.convert_tokens_to_ids(tokenizer.pad_token))
 
 
-def create_and_check_required_methods_tokenizer(tester, tokenizer_class, *inputs, **kwargs):
+def create_and_check_required_methods_tokenizer(tester, input_text, output_text, tokenizer_class, *inputs, **kwargs):
     tokenizer = tokenizer_class.from_pretrained(*inputs, **kwargs)
 
-    text = u"He is very happy, UNwant\u00E9d,running"
-    tokens = tokenizer.tokenize(text)
+    tokens = tokenizer.tokenize(input_text)
     ids = tokenizer.convert_tokens_to_ids(tokens)
-    ids_2 = tokenizer.encode(text)
+    ids_2 = tokenizer.encode(input_text)
     tester.assertListEqual(ids, ids_2)
 
     tokens_2 = tokenizer.convert_ids_to_tokens(ids)
     text_2 = tokenizer.decode(ids)
 
+    tester.assertEqual(text_2, output_text)
+
     tester.assertNotEqual(len(tokens_2), 0)
     tester.assertIsInstance(text_2, (str, unicode))
 
-def create_and_check_tokenizer_commons(tester, tokenizer_class, *inputs, **kwargs):
-    create_and_check_required_methods_tokenizer(tester, tokenizer_class, *inputs, **kwargs)
+def create_and_check_tokenizer_commons(tester, input_text, output_text, tokenizer_class, *inputs, **kwargs):
+    create_and_check_required_methods_tokenizer(tester, input_text, output_text, tokenizer_class, *inputs, **kwargs)
     create_and_check_add_tokens_tokenizer(tester, tokenizer_class, *inputs, **kwargs)
     create_and_check_save_and_load_tokenizer(tester, tokenizer_class, *inputs, **kwargs)
     create_and_check_pickle_tokenizer(tester, tokenizer_class, *inputs, **kwargs)

pytorch_transformers/tests/tokenization_transfo_xl_test.py

@@ -34,7 +34,10 @@ class TransfoXLTokenizationTest(unittest.TestCase):
             with open(vocab_file, "w", encoding='utf-8') as vocab_writer:
                 vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
 
-            create_and_check_tokenizer_commons(self, TransfoXLTokenizer, tmpdirname, lower_case=True)
+            input_text = u"<unk> UNwanted , running"
+            output_text = u"<unk> unwanted, running"
+
+            create_and_check_tokenizer_commons(self, input_text, output_text, TransfoXLTokenizer, tmpdirname, lower_case=True)
 
             tokenizer = TransfoXLTokenizer(vocab_file=vocab_file, lower_case=True)
 

pytorch_transformers/tests/tokenization_xlm_test.py

@@ -41,7 +41,10 @@ class XLMTokenizationTest(unittest.TestCase):
             with open(merges_file, "w") as fp:
                 fp.write("\n".join(merges))
 
-            create_and_check_tokenizer_commons(self, XLMTokenizer, tmpdirname)
+            input_text = u"lower newer"
+            output_text = u"lower newer"
+
+            create_and_check_tokenizer_commons(self, input_text, output_text, XLMTokenizer, tmpdirname)
 
             tokenizer = XLMTokenizer(vocab_file, merges_file)
 

pytorch_transformers/tests/tokenization_xlnet_test.py

@@ -32,7 +32,10 @@ class XLNetTokenizationTest(unittest.TestCase):
         with TemporaryDirectory() as tmpdirname:
             tokenizer.save_pretrained(tmpdirname)
 
-            create_and_check_tokenizer_commons(self, XLNetTokenizer, tmpdirname)
+            input_text = u"This is a test"
+            output_text = u"This is a test"
+
+            create_and_check_tokenizer_commons(self, input_text, output_text, XLNetTokenizer, tmpdirname)
 
             tokens = tokenizer.tokenize(u'This is a test')
             self.assertListEqual(tokens, [u'▁This', u'▁is', u'▁a', u'▁t', u'est'])

pytorch_transformers/tokenization_bert.py

@@ -161,10 +161,9 @@ class BertTokenizer(PreTrainedTokenizer):
         """Converts an index (integer) in a token (string/unicode) using the vocab."""
         return self.ids_to_tokens.get(index, self.unk_token)
 
-    def _convert_ids_to_string(self, tokens_ids):
-        """Converts a sequence of ids in a string."""
-        tokens = self.convert_ids_to_tokens(tokens_ids)
-        out_string = ''.join(tokens).replace(' ##', '').strip()
+    def convert_tokens_to_string(self, tokens):
+        """ Converts a sequence of tokens (string) in a single string. """
+        out_string = ' '.join(tokens).replace(' ##', '').strip()
         return out_string
 
     def save_vocabulary(self, vocab_path):

pytorch_transformers/tokenization_gpt2.py

@@ -185,9 +185,9 @@ class GPT2Tokenizer(PreTrainedTokenizer):
         """Converts an index (integer) in a token (string/unicode) using the vocab."""
         return self.decoder.get(index)
 
-    def _convert_ids_to_string(self, tokens_ids):
-        """Converts a sequence of ids in a string."""
-        text = ''.join(tokens_ids)
+    def convert_tokens_to_string(self, tokens):
+        """ Converts a sequence of tokens (string) in a single string. """
+        text = ''.join(tokens)
         text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
         return text
 

pytorch_transformers/tokenization_openai.py

@@ -174,9 +174,9 @@ class OpenAIGPTTokenizer(PreTrainedTokenizer):
         """Converts an id in a token (BPE) using the vocab."""
         return self.decoder.get(index, self.unk_token)
 
-    def _convert_ids_to_string(self, tokens_ids):
-        """Converts a sequence of ids in a string."""
-        out_string = ''.join(tokens_ids).replace('</w>', ' ').strip()
+    def convert_tokens_to_string(self, tokens):
+        """ Converts a sequence of tokens (string) in a single string. """
+        out_string = ''.join(tokens).replace('</w>', ' ').strip()
         return out_string
 
     def save_vocabulary(self, save_directory):

pytorch_transformers/tokenization_transfo_xl.py

@@ -229,9 +229,9 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
             else:
                 raise ValueError('Token not in vocabulary and no <unk> token in vocabulary for replacement')
 
-    def _convert_ids_to_string(self, tokens_ids):
-        """Converts a sequence of ids in a string."""
-        out_string = ' '.join(tokens_ids).strip()
+    def convert_tokens_to_string(self, tokens):
+        """ Converts a sequence of tokens (string) in a single string. """
+        out_string = ' '.join(tokens).strip()
         return out_string
 
     def convert_to_tensor(self, symbols):

pytorch_transformers/tokenization_utils.py

@@ -361,28 +361,33 @@ class PreTrainedTokenizer(object):
             (resp.) a sequence of ids, using the vocabulary.
         """
         if isinstance(tokens, str) or (six.PY2 and isinstance(tokens, unicode)):
-            return self.convert_token_to_id_with_added_voc(tokens)
+            return self._convert_token_to_id_with_added_voc(tokens)
 
         ids = []
         for token in tokens:
-            ids.append(self.convert_token_to_id_with_added_voc(token))
+            ids.append(self._convert_token_to_id_with_added_voc(token))
         if len(ids) > self.max_len:
             logger.warning("Token indices sequence length is longer than the specified maximum sequence length "
                            "for this model ({} > {}). Running this sequence through the model will result in "
                            "indexing errors".format(len(ids), self.max_len))
         return ids
 
-
-    def convert_token_to_id_with_added_voc(self, token):
+    def _convert_token_to_id_with_added_voc(self, token):
         if token in self.added_tokens_encoder:
             return self.added_tokens_encoder[token]
         return self._convert_token_to_id(token)
 
-
     def _convert_token_to_id(self, token):
         raise NotImplementedError
 
 
+    def encode(self, text):
+        """ Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary.
+            same as self.convert_tokens_to_ids(self.tokenize(text)).
+        """
+        return self.convert_tokens_to_ids(self.tokenize(text))
+
+
     def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
         """ Converts a single index or a sequence of indices (integers) in a token "
             (resp.) a sequence of tokens (str/unicode), using the vocabulary and added tokens.
@@ -391,7 +396,10 @@ class PreTrainedTokenizer(object):
                 skip_special_tokens: Don't decode special tokens (self.all_special_tokens). Default: False
         """
         if isinstance(ids, int):
-            return self.convert_id_to_token(ids)
+            if ids in self.added_tokens_decoder:
+                return self.added_tokens_decoder[ids]
+            else:
+                return self._convert_id_to_token(ids)
         tokens = []
         for index in ids:
             if index in self.all_special_ids and skip_special_tokens:
@@ -402,34 +410,26 @@ class PreTrainedTokenizer(object):
                 tokens.append(self._convert_id_to_token(index))
         return tokens
 
-
     def _convert_id_to_token(self, index):
         raise NotImplementedError
 
-
-    def encode(self, text):
-        """ Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary.
-            same as self.convert_tokens_to_ids(self.tokenize(text)).
+    def convert_tokens_to_string(self, tokens):
+        """ Converts a sequence of tokens (string) in a single string.
+            The most simple way to do it is ' '.join(self.convert_ids_to_tokens(token_ids))
+            but we often want to remove sub-word tokenization artifacts at the same time.
         """
-        return self.convert_tokens_to_ids(self.tokenize(text))
-
+        return ' '.join(self.convert_ids_to_tokens(tokens))
 
     def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
         """ Converts a sequence of ids (integer) in a string, using the tokenizer and vocabulary
             with options to remove special tokens and clean up tokenization spaces.
         """
         filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)
-        text = self._convert_ids_to_string(filtered_tokens)
+        text = self.convert_tokens_to_string(filtered_tokens)
         if clean_up_tokenization_spaces:
             text = clean_up_tokenization(text)
         return text
 
-    def _convert_ids_to_string(self, tokens_ids):
-        """ Converts a sequence of ids (integer) in a string, using the tokenizer and vocabulary.
-            roughtly same as ' '.join(self.convert_ids_to_tokens(token_ids)).
-        """
-        return ' '.join(self.convert_ids_to_tokens(tokens_ids))
-
     @property
     def special_tokens_map(self):
         """ A dictionary mapping special token class attribute (cls_token, unk_token...) to their

pytorch_transformers/tokenization_xlm.py

@@ -202,9 +202,9 @@ class XLMTokenizer(PreTrainedTokenizer):
         """Converts an index (integer) in a token (string/unicode) using the vocab."""
         return self.decoder.get(index, self.unk_token)
 
-    def _convert_ids_to_string(self, tokens_ids):
-        """Converts a sequence of ids in a string."""
-        out_string = ''.join(tokens_ids).replace('</w>', ' ').strip()
+    def convert_tokens_to_string(self, tokens):
+        """ Converts a sequence of tokens (string) in a single string. """
+        out_string = ''.join(tokens).replace('</w>', ' ').strip()
         return out_string
 
     def save_vocabulary(self, save_directory):

pytorch_transformers/tokenization_xlnet.py

@@ -170,9 +170,9 @@ class XLNetTokenizer(PreTrainedTokenizer):
             token = token.decode('utf-8')
         return token
 
-    def _convert_ids_to_string(self, tokens_ids):
-        """Converts a sequence of ids in a string."""
-        out_string = ''.join(tokens_ids).replace(SPIECE_UNDERLINE, ' ')
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (strings for sub-words) in a single string."""
+        out_string = ''.join(tokens).replace(SPIECE_UNDERLINE, ' ').strip()
         return out_string
 
     def save_vocabulary(self, save_directory):
@@ -184,6 +184,7 @@ class XLNetTokenizer(PreTrainedTokenizer):
             return
         out_vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES['vocab_file'])
 
-        copyfile(self.vocab_file, out_vocab_file)
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
 
         return (out_vocab_file,)