good quality generation example for GPT, GPT-2, Transfo-XL, XLNet

examples/run_generation.py

@@ -0,0 +1,198 @@
+#!/usr/bin/env python3
+# coding=utf-8
+# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+""" Generation with GPT/GPT-2/Transformer-XL/XLNet models
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import argparse
+import logging
+from tqdm import trange
+
+import torch
+import torch.nn.functional as F
+import numpy as np
+
+from pytorch_transformers import GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig
+
+from pytorch_transformers import GPT2LMHeadModel, GPT2Tokenizer
+from pytorch_transformers import OpenAIGPTLMHeadModel, OpenAIGPTTokenizer
+from pytorch_transformers import XLNetLMHeadModel, XLNetTokenizer
+from pytorch_transformers import TransfoXLLMHeadModel, TransfoXLTokenizer
+
+
+logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
+                    datefmt = '%m/%d/%Y %H:%M:%S',
+                    level = logging.INFO)
+logger = logging.getLogger(__name__)
+
+MAX_LENGTH = int(10000)  # Hardcoded max length to avoid infinite loop
+
+ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig)), ())
+
+MODEL_CLASSES = {
+    'gpt2': (GPT2LMHeadModel, GPT2Tokenizer),
+    'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
+    'xlnet': (XLNetLMHeadModel, XLNetTokenizer),
+    'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer),
+}
+
+# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
+# in https://github.com/rusiaaman/XLNet-gen#methodology
+# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
+PADDING_TEXT = """ In 1991, the remains of Russian Tsar Nicholas II and his family
+(except for Alexei and Maria) are discovered.
+The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
+remainder of the story. 1883 Western Siberia,
+a young Grigori Rasputin is asked by his father and a group of men to perform magic.
+Rasputin has a vision and denounces one of the men as a horse thief. Although his
+father initially slaps him for making such an accusation, Rasputin watches as the
+man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
+the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
+with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""
+
+
+def set_seed(args):
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    if args.n_gpu > 0:
+        torch.cuda.manual_seed_all(args.seed)
+
+
+def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
+    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
+        Args:
+            logits: logits distribution shape (vocabulary size)
+            top_k > 0: keep only top k tokens with highest probability (top-k filtering).
+            top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
+                Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
+        From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
+    """
+    assert logits.dim() == 1  # batch size 1 for now - could be updated for more but the code would be less clear
+    top_k = min(top_k, logits.size(-1))  # Safety check
+    if top_k > 0:
+        # Remove all tokens with a probability less than the last token of the top-k
+        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+        logits[indices_to_remove] = filter_value
+
+    if top_p > 0.0:
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+        cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+
+        # Remove tokens with cumulative probability above the threshold
+        sorted_indices_to_remove = cumulative_probs > top_p
+        # Shift the indices to the right to keep also the first token above the threshold
+        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+        sorted_indices_to_remove[..., 0] = 0
+
+        indices_to_remove = sorted_indices[sorted_indices_to_remove]
+        logits[indices_to_remove] = filter_value
+    return logits
+
+
+def sample_sequence(model, length, context, num_samples=1, temperature=1, top_k=0, top_p=0.0, is_xlnet=False, device='cpu'):
+    context = torch.tensor(context, dtype=torch.long, device=device)
+    context = context.unsqueeze(0).repeat(num_samples, 1)
+    generated = context
+    with torch.no_grad():
+        for _ in trange(length):
+
+            inputs = {'input_ids': generated}
+            if is_xlnet: 
+                # XLNet is a direct (predict same token, not next token) and bi-directional model by default
+                # => need one additional dummy token in the input (will be masked), attention mask and target mapping (see model docstring)
+                input_ids = torch.cat((generated, torch.zeros((1, 1), dtype=torch.long, device=device)), dim=1)
+                perm_mask = torch.zeros((1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float, device=device)
+                perm_mask[:, :, -1] = 1.0  # Previous tokens don't see last token
+                target_mapping = torch.zeros((1, 1, input_ids.shape[1]), dtype=torch.float, device=device)
+                target_mapping[0, 0, -1] = 1.0  # predict last token
+                inputs = {'input_ids': input_ids, 'perm_mask': perm_mask, 'target_mapping': target_mapping}
+
+            outputs = model(**inputs)  # Note: we could also use 'past' with GPT-2/Transfo-XL/XLNet (cached hidden-states)
+            next_token_logits = outputs[0][0, -1, :] / temperature
+            filtered_logits = top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p)
+            next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1)
+            generated = torch.cat((generated, next_token.unsqueeze(0)), dim=1)
+    return generated
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model_name', type=str, default=None, required=True,
+                        help="GPT, GPT-2, Transformer-XL or XLNet pre-trained model selected in the list: " + ", ".join(ALL_MODELS))
+    parser.add_argument("--prompt", type=str, default="")
+    parser.add_argument("--padding_text", type=str, default="")
+    parser.add_argument("--length", type=int, default=20)
+    parser.add_argument("--temperature", type=float, default=1.0)
+    parser.add_argument("--top_k", type=int, default=0)
+    parser.add_argument("--top_p", type=float, default=0.9)
+    parser.add_argument("--no_cuda", action='store_true',
+                        help="Avoid using CUDA when available")
+    parser.add_argument('--seed', type=int, default=42,
+                        help="random seed for initialization")
+    args = parser.parse_args()
+
+    args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
+    args.n_gpu = torch.cuda.device_count()
+
+    set_seed(args)
+
+    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
+
+    model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
+    tokenizer = tokenizer_class.from_pretrained(args.model_name)
+    model = model_class.from_pretrained(args.model_name)
+    model.to(args.device)
+    model.eval()
+
+    if args.length < 0 and model.config.max_position_embeddings > 0:
+        args.length = model.config.max_position_embeddings
+    elif 0 < model.config.max_position_embeddings < args.length:
+        args.length = model.config.max_position_embeddings  # No generation bigger than model size 
+    elif args.length < 0:
+        args.length = MAX_LENGTH  # avoid infinite loop
+
+    print(args)
+    while True:
+        raw_text = args.prompt if args.prompt else input("Model prompt >>> ")
+        if args.model_type in ["transfo-xl", "xlnet"]:
+            # Models with memory likes to have a long prompt for short inputs.
+            raw_text = (args.padding_text if args.padding_text else PADDING_TEXT) + raw_text
+        context_tokens = tokenizer.encode(raw_text)
+        out = sample_sequence(
+            model=model,
+            context=context_tokens,
+            length=args.length,
+            temperature=args.temperature,
+            top_k=args.top_k,
+            top_p=args.top_p,
+            device=args.device,
+            is_xlnet=bool(args.model_type == "xlnet"),
+        )
+        out = out[0, len(context_tokens):].tolist()
+        text = tokenizer.decode(out, clean_up_tokenization_spaces=True)
+        print(text)
+        if args.prompt:
+            break
+    return text
+
+
+if __name__ == '__main__':
+    main()

examples/test_examples.py

@@ -29,6 +29,7 @@ except ImportError:
 
 import run_glue
 import run_squad
+import run_generation
 
 logging.basicConfig(level=logging.DEBUG)
 
@@ -91,5 +92,18 @@ class ExamplesTests(unittest.TestCase):
             self.assertGreaterEqual(result['exact'], 30)
 
 
+    def test_generation(self):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+
+        testargs = ["run_generation.py",
+                    "--prompt=Hello",
+                    "--seed=42"]
+        model_name = "--model_name=openai-gpt"
+        with patch.object(sys, 'argv', testargs + [model_name]):
+            result = run_generation.main()
+            self.assertGreaterEqual(result['f1'], 30)
+            self.assertGreaterEqual(result['exact'], 30)
+
 if __name__ == "__main__":
     unittest.main()

pytorch_transformers/modeling_gpt2.py

@@ -37,9 +37,9 @@ from .modeling_bert import BertLayerNorm as LayerNorm
 logger = logging.getLogger(__name__)
 
 GPT2_PRETRAINED_MODEL_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-pytorch_model.bin",
-                                "gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-pytorch_model.bin"}
+                                     "gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-pytorch_model.bin"}
 GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json",
-                                 "gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-config.json"}
+                                      "gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-config.json"}
 
 def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path):
     """ Load tf checkpoints in a pytorch model
@@ -195,6 +195,10 @@ class GPT2Config(PretrainedConfig):
                 "or the path to a pretrained model config file (str)"
             )
 
+    @property
+    def max_position_embeddings(self):
+        return self.n_positions
+
     @property
     def hidden_size(self):
         return self.n_embd

pytorch_transformers/modeling_openai.py

@@ -214,6 +214,10 @@ class OpenAIGPTConfig(PretrainedConfig):
                 "or the path to a pretrained model config file (str)"
             )
 
+    @property
+    def max_position_embeddings(self):
+        return self.n_positions
+
     @property
     def hidden_size(self):
         return self.n_embd

pytorch_transformers/modeling_transfo_xl.py

@@ -287,6 +287,10 @@ class TransfoXLConfig(PretrainedConfig):
             raise ValueError("First argument must be either a vocabulary size (int)"
                              "or the path to a pretrained model config file (str)")
 
+    @property
+    def max_position_embeddings(self):
+        return self.tgt_len + self.ext_len + self.mem_len
+
     @property
     def vocab_size(self):
         return self.n_token

pytorch_transformers/modeling_xlnet.py

@@ -211,9 +211,6 @@ class XLNetConfig(PretrainedConfig):
             layers in the embeddings, encoder, and pooler.
         dropatt: The dropout ratio for the attention
             probabilities.
-        max_position_embeddings: The maximum sequence length that this model might
-            ever be used with. Typically set this to something large just in case
-            (e.g., 512 or 1024 or 2048).
         initializer_range: The sttdev of the truncated_normal_initializer for
             initializing all weight matrices.
         layer_norm_eps: The epsilon used by LayerNorm.
@@ -247,7 +244,6 @@ class XLNetConfig(PretrainedConfig):
                  untie_r=True,
                  attn_type="bi",
 
-                 max_position_embeddings=512,
                  initializer_range=0.02,
                  layer_norm_eps=1e-12,
 
@@ -289,7 +285,6 @@ class XLNetConfig(PretrainedConfig):
             self.untie_r = untie_r
             self.attn_type = attn_type
 
-            self.max_position_embeddings = max_position_embeddings
             self.initializer_range = initializer_range
             self.layer_norm_eps = layer_norm_eps
 
@@ -312,6 +307,10 @@ class XLNetConfig(PretrainedConfig):
             raise ValueError("First argument must be either a vocabulary size (int)"
                              "or the path to a pretrained model config file (str)")
 
+    @property
+    def max_position_embeddings(self):
+        return -1
+
     @property
     def vocab_size(self):
         return self.n_token
@@ -765,7 +764,7 @@ class XLNetModel(XLNetPreTrainedModel):
         return pos_emb
 
     def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
-                mems=None, perm_mask=None, target_mapping=None, inp_q=None, head_mask=None):
+                mems=None, perm_mask=None, target_mapping=None, head_mask=None):
         """
         Performs a model forward pass. **Can be called by calling the class directly, once it has been instantiated.**
 
@@ -790,10 +789,6 @@ class XLNetModel(XLNetPreTrainedModel):
                 on the j-th token.
                 Only used during pretraining for partial prediction.
                 Set to None during finetuning.
-            inp_q: [optional] float32 Tensor in shape [bsz, len].
-                1 for tokens with losses and 0 for tokens without losses.
-                Only used during pretraining for two-stream attention.
-                Set to None during finetuning.
             head_mask: TODO Lysandre didn't fill
 
 
@@ -823,7 +818,6 @@ class XLNetModel(XLNetPreTrainedModel):
         attention_mask = attention_mask.transpose(0, 1).contiguous() if attention_mask is not None else None
         perm_mask = perm_mask.permute(1, 2, 0).contiguous() if perm_mask is not None else None
         target_mapping = target_mapping.permute(1, 2, 0).contiguous() if target_mapping is not None else None
-        inp_q = inp_q.transpose(0, 1).contiguous() if inp_q is not None else None
 
         qlen, bsz = input_ids.shape[0], input_ids.shape[1]
         mlen = mems[0].shape[0] if mems is not None else 0
@@ -878,12 +872,11 @@ class XLNetModel(XLNetPreTrainedModel):
         ##### Word embeddings and prepare h & g hidden states
         word_emb_k = self.word_embedding(input_ids)
         output_h = self.dropout(word_emb_k)
-        if inp_q is not None:
-            if target_mapping is not None:
-                word_emb_q = self.mask_emb.expand(target_mapping.shape[0], bsz, -1)
-            else:
-                inp_q_ext = inp_q[:, :, None]
-                word_emb_q = inp_q_ext * self.mask_emb + (1 - inp_q_ext) * word_emb_k
+        if target_mapping is not None:
+            word_emb_q = self.mask_emb.expand(target_mapping.shape[0], bsz, -1)
+        # else:  # We removed the inp_q input which was same as target mapping
+        #     inp_q_ext = inp_q[:, :, None]
+        #     word_emb_q = inp_q_ext * self.mask_emb + (1 - inp_q_ext) * word_emb_k
             output_g = self.dropout(word_emb_q)
         else:
             output_g = None
@@ -994,7 +987,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
         self._tie_or_clone_weights(self.lm_loss, self.transformer.word_embedding)
 
     def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
-                mems=None, perm_mask=None, target_mapping=None, inp_q=None,
+                mems=None, perm_mask=None, target_mapping=None,
                 labels=None, head_mask=None):
         """
          all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
@@ -1020,11 +1013,6 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
                 on the j-th token.
                 Only used during pretraining for partial prediction.
                 Set to None during finetuning.
-            inp_q: [optional] float32 Tensor in shape [bsz, len].
-                1 for tokens with losses and 0 for tokens without losses.
-                Only used during pretraining for two-stream attention.
-                Set to None during finetuning.
-
 
         Returns:
             A ``tuple(encoded_layers, pooled_output)``, with
@@ -1054,7 +1042,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
             all_encoder_layers, pooled_output = model.forward(input_ids, token_type_ids, input_mask)
         """
         transformer_outputs = self.transformer(input_ids, token_type_ids, input_mask, attention_mask,
-                                               mems, perm_mask, target_mapping, inp_q, head_mask)
+                                               mems, perm_mask, target_mapping, head_mask)
 
         logits = self.lm_loss(transformer_outputs[0])
 
@@ -1103,7 +1091,7 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
         self.apply(self.init_weights)
 
     def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
-                mems=None, perm_mask=None, target_mapping=None, inp_q=None,
+                mems=None, perm_mask=None, target_mapping=None,
                 labels=None, head_mask=None):
         """
         Performs a model forward pass. **Can be called by calling the class directly, once it has been instantiated.**
@@ -1129,10 +1117,6 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
                 on the j-th token.
                 Only used during pre-training for partial prediction.
                 Set to None during fine-tuning.
-            inp_q: float32 Tensor in shape [bsz, len].
-                1 for tokens with losses and 0 for tokens without losses.
-                Only used during pre-training for two-stream attention.
-                Set to None during fine-tuning.
             labels: TODO Lysandre didn't fill
             head_mask: an optional ``torch.Tensor`` of shape [num_heads] or [num_layers, num_heads] with indices between 0 and 1.
                 It's a mask to be used to nullify some heads of the transformer. 1.0 => head is fully masked, 0.0 => head is not masked.
@@ -1161,7 +1145,7 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
             all_encoder_layers, pooled_output = model.forward(input_ids, token_type_ids, input_mask)
         """
         transformer_outputs = self.transformer(input_ids, token_type_ids, input_mask, attention_mask,
-                                               mems, perm_mask, target_mapping, inp_q, head_mask)
+                                               mems, perm_mask, target_mapping, head_mask)
         output = transformer_outputs[0]
 
         output = self.sequence_summary(output)
@@ -1215,7 +1199,7 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
         self.apply(self.init_weights)
 
     def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
-                mems=None, perm_mask=None, target_mapping=None, inp_q=None,
+                mems=None, perm_mask=None, target_mapping=None,
                 start_positions=None, end_positions=None, cls_index=None, is_impossible=None, p_mask=None,
                 head_mask=None):
 
@@ -1266,7 +1250,7 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
             start_logits, end_logits = model.forward(input_ids, token_type_ids, input_mask)
         """
         transformer_outputs = self.transformer(input_ids, token_type_ids, input_mask, attention_mask,
-                                               mems, perm_mask, target_mapping, inp_q, head_mask)
+                                               mems, perm_mask, target_mapping, head_mask)
         hidden_states = transformer_outputs[0]
         start_logits = self.start_logits(hidden_states, p_mask)
 

pytorch_transformers/tests/modeling_xlnet_test.py

@@ -97,7 +97,6 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
             perm_mask[:, :, -1] = 1.0  # Previous tokens don't see last token
             target_mapping = torch.zeros(self.batch_size, 1, self.seq_length + 1, dtype=torch.float)
             target_mapping[:, 0, -1] = 1.0  # predict last token
-            inp_q = target_mapping[:, 0, :].clone()  # predict last token
 
             sequence_labels = None
             lm_labels = None
@@ -124,14 +123,14 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
                 num_labels=self.type_sequence_label_size)
 
             return (config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
-                    target_mapping, inp_q, segment_ids, lm_labels, sequence_labels, is_impossible_labels)
+                    target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels)
 
         def set_seed(self):
             random.seed(self.seed)
             torch.manual_seed(self.seed)
 
         def create_and_check_xlnet_base_model(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
-                target_mapping, inp_q, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
+                target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
             model = XLNetModel(config)
             model.eval()
 
@@ -153,7 +152,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
                 [[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
 
         def create_and_check_xlnet_lm_head(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
-                target_mapping, inp_q, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
+                target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
             model = XLNetLMHeadModel(config)
             model.eval()
 
@@ -161,7 +160,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
 
             loss_2, all_logits_2, mems_2 = model(input_ids_2, token_type_ids=segment_ids, labels=lm_labels, mems=mems_1)
 
-            logits, _ = model(input_ids_q, perm_mask=perm_mask, target_mapping=target_mapping, inp_q=inp_q)
+            logits, _ = model(input_ids_q, perm_mask=perm_mask, target_mapping=target_mapping)
 
             result = {
                 "loss_1": loss_1,
@@ -193,7 +192,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
                 [[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
 
         def create_and_check_xlnet_qa(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
-                target_mapping, inp_q, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
+                target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
             model = XLNetForQuestionAnswering(config)
             model.eval()
 
@@ -243,7 +242,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
                 [[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
 
         def create_and_check_xlnet_sequence_classif(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
-                target_mapping, inp_q, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
+                target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
             model = XLNetForSequenceClassification(config)
             model.eval()
 
@@ -269,7 +268,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
         def prepare_config_and_inputs_for_common(self):
             config_and_inputs = self.prepare_config_and_inputs()
             (config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
-                target_mapping, inp_q, segment_ids, lm_labels,
+                target_mapping, segment_ids, lm_labels,
                 sequence_labels, is_impossible_labels) = config_and_inputs
             inputs_dict = {'input_ids': input_ids_1}
             return config, inputs_dict

pytorch_transformers/tokenization_transfo_xl.py

@@ -25,7 +25,6 @@ import os
 import sys
 from collections import Counter, OrderedDict
 from io import open
-import unicodedata
 
 import torch
 import numpy as np

pytorch_transformers/tokenization_utils.py

@@ -343,7 +343,7 @@ class PreTrainedTokenizer(object):
             return sum((split_on_tokens(tok_list[1:], sub_text.strip()) + [tok] \
                         for sub_text in split_text), [])[:-1]
 
-        added_tokens = list(self.added_tokens_encoder.keys())
+        added_tokens = list(self.added_tokens_encoder.keys()) + self.all_special_tokens
         tokenized_text = split_on_tokens(added_tokens, text)
         return tokenized_text
 
@@ -466,7 +466,7 @@ class PreTrainedTokenizer(object):
 
 
 def clean_up_tokenization(out_string):
-    out_string.replace(' .', '.').replace(' ?', '?').replace(' !', '!').replace(' ,', ','
+    out_string = out_string.replace(' .', '.').replace(' ?', '?').replace(' !', '!').replace(' ,', ','
                     ).replace(" ' ", "'").replace(" n't", "n't").replace(" 'm", "'m").replace(" do not", " don't"
                     ).replace(" 's", "'s").replace(" 've", "'ve").replace(" 're", "'re")
     return out_string

pytorch_transformers/tokenization_xlnet.py

@@ -172,7 +172,7 @@ class XLNetTokenizer(PreTrainedTokenizer):
 
     def _convert_ids_to_string(self, tokens_ids):
         """Converts a sequence of ids in a string."""
-        out_string = ''.join(tokens_ids)
+        out_string = ''.join(tokens_ids).replace(SPIECE_UNDERLINE, ' ')
         return out_string
 
     def save_vocabulary(self, save_directory):