WIP XLNet

pytorch_transformers/__init__.py

@@ -95,7 +95,7 @@ except (ImportError, AssertionError):
 if _tf_available:
     logger.info("TensorFlow version {} available.".format(tf.__version__))
 
-    from .modeling_tf_utils import TFPreTrainedModel
+    from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, TFSequenceSummary
     from .modeling_tf_auto import (TFAutoModel, TFAutoModelForSequenceClassification, TFAutoModelForQuestionAnswering,
                                    TFAutoModelWithLMHead)
 
@@ -107,7 +107,7 @@ if _tf_available:
                                    load_bert_pt_weights_in_tf2,
                                    TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
 
-    from .modeling_tf_gpt2 import (TFGPT2PreTrainedModel, TFGPT2MainLayer, TFGPT2Embeddings,
+    from .modeling_tf_gpt2 import (TFGPT2PreTrainedModel, TFGPT2MainLayer,
                                    TFGPT2Model, TFGPT2LMHeadModel, TFGPT2DoubleHeadsModel,
                                    load_gpt2_pt_weights_in_tf2,
                                    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)

pytorch_transformers/configuration_utils.py

@@ -54,6 +54,7 @@ class PretrainedConfig(object):
         self.output_attentions = kwargs.pop('output_attentions', False)
         self.output_hidden_states = kwargs.pop('output_hidden_states', False)
         self.torchscript = kwargs.pop('torchscript', False)
+        self.use_bfloat16 = kwargs.pop('use_bfloat16', False)
         self.pruned_heads = kwargs.pop('pruned_heads', {})
 
     def save_pretrained(self, save_directory):

pytorch_transformers/modeling_tf_gpt2.py

@@ -28,7 +28,8 @@ from io import open
 import numpy as np
 import tensorflow as tf
 
-from .modeling_tf_utils import TFPreTrainedModel, TFConv1D, TFSequenceSummary, shape_list
+from .modeling_tf_utils import (TFPreTrainedModel, TFConv1D, TFSharedEmbeddings,
+                                TFSequenceSummary, shape_list)
 from .configuration_gpt2 import GPT2Config
 from .file_utils import add_start_docstrings
 
@@ -65,6 +66,7 @@ def load_gpt2_pt_weights_in_tf2(tf_model, config, pytorch_checkpoint_path):
 
     symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights
     weight_value_tuples = []
+    all_pytorch_weights = set(list(state_dict.keys()))
     for symbolic_weight in symbolic_weights:
         name = symbolic_weight.name
         name = name.replace(':0', '')
@@ -100,13 +102,13 @@ def load_gpt2_pt_weights_in_tf2(tf_model, config, pytorch_checkpoint_path):
 
         weight_value_tuples.append((symbolic_weight, array))
 
-        state_dict.pop(name)
+        all_pytorch_weights.discard(name)
 
     K.batch_set_value(weight_value_tuples)
 
     tfo = tf_model(tf_inputs, training=False)  # Make sure restore ops are run
 
-    assert not state_dict, "Weights not loaded: {}".format(list(state_dict.keys()))
+    logger.info("Weights or buffers not loaded from PyTorch model: {}".format(all_pytorch_weights))
 
     return tf_model
 
@@ -267,65 +269,6 @@ class TFBlock(tf.keras.layers.Layer):
         outputs = [x] + output_attn[1:]
         return outputs  # x, present, (attentions)
 
-class TFGPT2Embeddings(tf.keras.layers.Layer):
-    """Construct the embeddings from word, position and token_type embeddings.
-    """
-    def __init__(self, config, **kwargs):
-        super(TFGPT2Embeddings, self).__init__(**kwargs)
-        self.vocab_size = config.vocab_size
-        self.hidden_size = config.hidden_size
-
-    def build(self, input_shape):
-        """Build shared word embedding layer
-        Shared weights logic adapted from
-            https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
-        """
-        self.weight = self.add_weight(
-            "weight",
-            shape=[self.vocab_size, self.hidden_size],
-            initializer=tf.random_normal_initializer(
-                mean=0., stddev=self.hidden_size**-0.5))
-        super(TFGPT2Embeddings, self).build(input_shape)
-
-    def call(self, inputs, mode="embedding"):
-        """Get token embeddings of inputs.
-        Args:
-            inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
-            mode: string, a valid value is one of "embedding" and "linear".
-        Returns:
-            outputs: (1) If mode == "embedding", output embedding tensor, float32 with
-                shape [batch_size, length, embedding_size]; (2) mode == "linear", output
-                linear tensor, float32 with shape [batch_size, length, vocab_size].
-        Raises:
-            ValueError: if mode is not valid.
-        
-        Shared weights logic adapted from
-            https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
-        """
-        if mode == "embedding":
-            return self._embedding(inputs)
-        elif mode == "linear":
-            return self._linear(inputs)
-        else:
-            raise ValueError("mode {} is not valid.".format(mode))
-
-    def _embedding(self, input_ids):
-        """Applies embedding based on inputs tensor."""
-        return tf.gather(self.weight, input_ids)
-
-    def _linear(self, inputs):
-        """Computes logits by running inputs through a linear layer.
-            Args:
-                inputs: A float32 tensor with shape [..., hidden_size]
-            Returns:
-                float32 tensor with shape [..., vocab_size].
-        """
-        first_dims = shape_list(inputs)[:-1]
-
-        x = tf.reshape(inputs, [-1, self.hidden_size])
-        logits = tf.matmul(x, self.weight, transpose_b=True)
-
-        return tf.reshape(logits, first_dims + [self.vocab_size])
 
 class TFGPT2MainLayer(tf.keras.layers.Layer):
     def __init__(self, config, *inputs, **kwargs):
@@ -336,10 +279,13 @@ class TFGPT2MainLayer(tf.keras.layers.Layer):
         self.vocab_size = config.vocab_size
         self.n_embd = config.n_embd
 
-        self.wte = TFGPT2Embeddings(config, name='wte')
+        self.wte = TFSharedEmbeddings(config.vocab_size, config.hidden_size, name='wte')
         self.wpe = tf.keras.layers.Embedding(config.n_positions, config.n_embd, name='wpe')
         self.drop = tf.keras.layers.Dropout(config.embd_pdrop)
-        self.h = [TFBlock(config.n_ctx, config, scale=True, name='h_{}'.format(i)) for i in range(config.n_layer)]
+        self.h = [TFBlock(config.n_ctx,
+                          config,
+                          scale=True,
+                          name='h_{}'.format(i)) for i in range(config.n_layer)]
         self.ln_f = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name='ln_f')
 
     def _resize_token_embeddings(self, new_num_tokens):

pytorch_transformers/modeling_tf_utils.py

@@ -288,6 +288,69 @@ class TFConv1D(tf.keras.layers.Layer):
         return x
 
 
+class TFSharedEmbeddings(tf.keras.layers.Layer):
+    """Construct shared token embeddings.
+    """
+    def __init__(self, vocab_size, hidden_size, initializer_range=None, **kwargs):
+        super(TFSharedEmbeddings, self).__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.initializer_range = initializer_range
+
+    def build(self, input_shape):
+        """Build shared word embedding layer
+        Shared weights logic adapted from
+            https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
+        """
+        initializer_range = self.hidden_size**-0.5 if self.initializer_range is None else self.initializer_range
+        self.weight = self.add_weight(
+            "weight",
+            shape=[self.vocab_size, self.hidden_size],
+            initializer=tf.random_normal_initializer(
+                mean=0., stddev=initializer_range))
+        super(TFSharedEmbeddings, self).build(input_shape)
+
+    def call(self, inputs, mode="embedding"):
+        """Get token embeddings of inputs.
+        Args:
+            inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
+            mode: string, a valid value is one of "embedding" and "linear".
+        Returns:
+            outputs: (1) If mode == "embedding", output embedding tensor, float32 with
+                shape [batch_size, length, embedding_size]; (2) mode == "linear", output
+                linear tensor, float32 with shape [batch_size, length, vocab_size].
+        Raises:
+            ValueError: if mode is not valid.
+        
+        Shared weights logic adapted from
+            https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
+        """
+        if mode == "embedding":
+            return self._embedding(inputs)
+        elif mode == "linear":
+            return self._linear(inputs)
+        else:
+            raise ValueError("mode {} is not valid.".format(mode))
+
+    def _embedding(self, input_ids):
+        """Applies embedding based on inputs tensor."""
+        return tf.gather(self.weight, input_ids)
+
+    def _linear(self, inputs):
+        """Computes logits by running inputs through a linear layer.
+            Args:
+                inputs: A float32 tensor with shape [..., hidden_size]
+            Returns:
+                float32 tensor with shape [..., vocab_size].
+        """
+        first_dims = shape_list(inputs)[:-1]
+
+        x = tf.reshape(inputs, [-1, self.hidden_size])
+        logits = tf.matmul(x, self.weight, transpose_b=True)
+
+        return tf.reshape(logits, first_dims + [self.vocab_size])
+
+
 class TFSequenceSummary(tf.keras.layers.Layer):
     r""" Compute a single vector summary of a sequence hidden states according to various possibilities:
         Args of the config class:

pytorch_transformers/tests/modeling_tf_common_test.py

@@ -262,7 +262,7 @@ class TFCommonTestCases:
             #     self.assertEqual(len(params_tied_2), len(params_tied))
 
 
-def ids_tensor(shape, vocab_size, rng=None, name=None):
+def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=tf.int32):
     """Creates a random int32 tensor of the shape within the vocab size."""
     if rng is None:
         rng = random.Random()
@@ -275,7 +275,7 @@ def ids_tensor(shape, vocab_size, rng=None, name=None):
     for _ in range(total_dims):
         values.append(rng.randint(0, vocab_size - 1))
 
-    return tf.constant(values, shape=shape)
+    return tf.constant(values, shape=shape, dtype=dtype)
 
 
 class TFModelUtilsTest(unittest.TestCase):

pytorch_transformers/tests/modeling_tf_xlnet_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
+from __future__ import division
+from __future__ import print_function
+
+import os
+import unittest
+import json
+import random
+import shutil
+import pytest
+
+from pytorch_transformers import XLNetConfig, is_tf_available
+
+if is_tf_available():
+    import tensorflow as tf
+
+    from pytorch_transformers.modeling_tf_xlnet import (TFXLNetModel, TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
+                                    #   XLNetLMHeadModel,
+                                    #   XLNetForSequenceClassification, XLNetForQuestionAnswering)
+else:
+    pytestmark = pytest.mark.skip("Require TensorFlow")
+
+from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
+from .configuration_common_test import ConfigTester
+
+class TFXLNetModelTest(TFCommonTestCases.TFCommonModelTester):
+
+    all_model_classes=(TFXLNetModel, ) if is_tf_available() else ()
+    # all_model_classes=(TFXLNetModel, TFXLNetLMHeadModel,
+    #                    TFXLNetForSequenceClassification, TFXLNetForQuestionAnswering) if is_tf_available() else ()
+    test_pruning = False
+
+    class TFXLNetModelTester(object):
+
+        def __init__(self,
+                     parent,
+                     batch_size=13,
+                     seq_length=7,
+                     mem_len=10,
+                     clamp_len=-1,
+                     reuse_len=15,
+                     is_training=True,
+                     use_labels=True,
+                     vocab_size=99,
+                     cutoffs=[10, 50, 80],
+                     hidden_size=32,
+                     num_attention_heads=4,
+                     d_inner=128,
+                     num_hidden_layers=5,
+                     max_position_embeddings=10,
+                     type_sequence_label_size=2,
+                     untie_r=True,
+                     bi_data=False,
+                     same_length=False,
+                     initializer_range=0.05,
+                     seed=1,
+                     type_vocab_size=2,
+            ):
+            self.parent = parent
+            self.batch_size = batch_size
+            self.seq_length = seq_length
+            self.mem_len = mem_len
+            # self.key_len = seq_length + mem_len
+            self.clamp_len = clamp_len
+            self.reuse_len = reuse_len
+            self.is_training = is_training
+            self.use_labels = use_labels
+            self.vocab_size = vocab_size
+            self.cutoffs = cutoffs
+            self.hidden_size = hidden_size
+            self.num_attention_heads = num_attention_heads
+            self.d_inner = d_inner
+            self.num_hidden_layers = num_hidden_layers
+            self.max_position_embeddings = max_position_embeddings
+            self.bi_data = bi_data
+            self.untie_r = untie_r
+            self.same_length = same_length
+            self.initializer_range = initializer_range
+            self.seed = seed
+            self.type_vocab_size = type_vocab_size
+            self.type_sequence_label_size = type_sequence_label_size
+
+        def prepare_config_and_inputs(self):
+            input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+            input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+            segment_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
+            input_mask = ids_tensor([self.batch_size, self.seq_length], 2, dtype=tf.float32)
+
+            input_ids_q = ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size)
+            perm_mask = tf.zeros((self.batch_size, self.seq_length + 1, self.seq_length), dtype=tf.float32)
+            perm_mask_last = tf.ones((self.batch_size, self.seq_length + 1, 1), dtype=tf.float32)
+            perm_mask = tf.concat([perm_mask, perm_mask_last], axis=-1)
+            # perm_mask[:, :, -1] = 1.0  # Previous tokens don't see last token
+            target_mapping = tf.zeros((self.batch_size, 1, self.seq_length), dtype=torch.float32)
+            target_mapping_last = tf.ones((self.batch_size, 1, 1), dtype=torch.float32)
+            target_mapping = tf.concat([target_mapping, target_mapping_last], axis=-1)
+            # target_mapping[:, 0, -1] = 1.0  # predict last token
+
+            sequence_labels = None
+            lm_labels = None
+            is_impossible_labels = None
+            if self.use_labels:
+                lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+                sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+                is_impossible_labels = ids_tensor([self.batch_size], 2, dtype=tf.float32)
+
+            config = XLNetConfig(
+                vocab_size_or_config_json_file=self.vocab_size,
+                d_model=self.hidden_size,
+                n_head=self.num_attention_heads,
+                d_inner=self.d_inner,
+                n_layer=self.num_hidden_layers,
+                untie_r=self.untie_r,
+                max_position_embeddings=self.max_position_embeddings,
+                mem_len=self.mem_len,
+                clamp_len=self.clamp_len,
+                same_length=self.same_length,
+                reuse_len=self.reuse_len,
+                bi_data=self.bi_data,
+                initializer_range=self.initializer_range,
+                num_labels=self.type_sequence_label_size)
+
+            return (config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
+                    target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels)
+
+        def set_seed(self):
+            random.seed(self.seed)
+            tf.random.set_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, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
+            model = TFXLNetModel(config)
+
+            inputs = {'input_ids': input_ids,
+                      'input_mask': input_mask,
+                      'token_type_ids': token_type_ids}
+
+            _, _ = model(inputs)
+
+            inputs = [input_ids, input_mask]
+
+            outputs, mems_1 = model(inputs)
+
+            result = {
+                "mems_1": [mem.numpy() for m in mems_1],
+                "outputs": outputs.numpy(),
+            }
+
+            self.parent.assertListEqual(
+                list(result["outputs"].shape),
+                [self.batch_size, self.seq_length, self.hidden_size])
+            self.parent.assertListEqual(
+                list(list(mem.shape) for mem in result["mems_1"]),
+                [[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, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
+            pass
+            # model = XLNetLMHeadModel(config)
+            # model.eval()
+
+            # loss_1, all_logits_1, mems_1 = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels)
+
+            # 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)
+
+            # result = {
+            #     "loss_1": loss_1,
+            #     "mems_1": mems_1,
+            #     "all_logits_1": all_logits_1,
+            #     "loss_2": loss_2,
+            #     "mems_2": mems_2,
+            #     "all_logits_2": all_logits_2,
+            # }
+
+            # self.parent.assertListEqual(
+            #     list(result["loss_1"].size()),
+            #     [])
+            # self.parent.assertListEqual(
+            #     list(result["all_logits_1"].size()),
+            #     [self.batch_size, self.seq_length, self.vocab_size])
+            # self.parent.assertListEqual(
+            #     list(list(mem.size()) for mem in result["mems_1"]),
+            #     [[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
+
+            # self.parent.assertListEqual(
+            #     list(result["loss_2"].size()),
+            #     [])
+            # self.parent.assertListEqual(
+            #     list(result["all_logits_2"].size()),
+            #     [self.batch_size, self.seq_length, self.vocab_size])
+            # self.parent.assertListEqual(
+            #     list(list(mem.size()) for mem in result["mems_2"]),
+            #     [[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, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
+            pass
+            # model = XLNetForQuestionAnswering(config)
+            # model.eval()
+
+            # outputs = model(input_ids_1)
+            # start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits, mems = outputs
+
+            # outputs = model(input_ids_1, start_positions=sequence_labels,
+            #                              end_positions=sequence_labels,
+            #                              cls_index=sequence_labels,
+            #                              is_impossible=is_impossible_labels,
+            #                              p_mask=input_mask)
+
+            # outputs = model(input_ids_1, start_positions=sequence_labels,
+            #                              end_positions=sequence_labels,
+            #                              cls_index=sequence_labels,
+            #                              is_impossible=is_impossible_labels)
+
+            # total_loss, mems = outputs
+
+            # outputs = model(input_ids_1, start_positions=sequence_labels,
+            #                              end_positions=sequence_labels)
+
+            # total_loss, mems = outputs
+
+            # result = {
+            #     "loss": total_loss,
+            #     "start_top_log_probs": start_top_log_probs,
+            #     "start_top_index": start_top_index,
+            #     "end_top_log_probs": end_top_log_probs,
+            #     "end_top_index": end_top_index,
+            #     "cls_logits": cls_logits,
+            #     "mems": mems,
+            # }
+
+            # self.parent.assertListEqual(
+            #     list(result["loss"].size()),
+            #     [])
+            # self.parent.assertListEqual(
+            #     list(result["start_top_log_probs"].size()),
+            #     [self.batch_size, model.config.start_n_top])
+            # self.parent.assertListEqual(
+            #     list(result["start_top_index"].size()),
+            #     [self.batch_size, model.config.start_n_top])
+            # self.parent.assertListEqual(
+            #     list(result["end_top_log_probs"].size()),
+            #     [self.batch_size, model.config.start_n_top * model.config.end_n_top])
+            # self.parent.assertListEqual(
+            #     list(result["end_top_index"].size()),
+            #     [self.batch_size, model.config.start_n_top * model.config.end_n_top])
+            # self.parent.assertListEqual(
+            #     list(result["cls_logits"].size()),
+            #     [self.batch_size])
+            # self.parent.assertListEqual(
+            #     list(list(mem.size()) for mem in result["mems"]),
+            #     [[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, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
+            pass
+            # model = XLNetForSequenceClassification(config)
+            # model.eval()
+
+            # logits, mems_1 = model(input_ids_1)
+            # loss, logits, mems_1 = model(input_ids_1, labels=sequence_labels)
+
+            # result = {
+            #     "loss": loss,
+            #     "mems_1": mems_1,
+            #     "logits": logits,
+            # }
+
+            # self.parent.assertListEqual(
+            #     list(result["loss"].size()),
+            #     [])
+            # self.parent.assertListEqual(
+            #     list(result["logits"].size()),
+            #     [self.batch_size, self.type_sequence_label_size])
+            # self.parent.assertListEqual(
+            #     list(list(mem.size()) for mem in result["mems_1"]),
+            #     [[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
+
+        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, segment_ids, lm_labels,
+                sequence_labels, is_impossible_labels) = config_and_inputs
+            inputs_dict = {'input_ids': input_ids_1}
+            return config, inputs_dict
+
+
+    def setUp(self):
+        self.model_tester = TFXLNetModelTest.TFXLNetModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=XLNetConfig, d_inner=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_xlnet_base_model(self):
+        self.model_tester.set_seed()
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs)
+
+    def test_xlnet_lm_head(self):
+        self.model_tester.set_seed()
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs) 
+
+    def test_xlnet_sequence_classif(self):
+        self.model_tester.set_seed()
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs)
+
+    def test_xlnet_qa(self):
+        self.model_tester.set_seed()
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_xlnet_qa(*config_and_inputs)
+
+    @pytest.mark.slow
+    def test_model_from_pretrained(self):
+        cache_dir = "/tmp/pytorch_transformers_test/"
+        for model_name in list(TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
+            model = TFXLNetModel.from_pretrained(model_name, cache_dir=cache_dir)
+            shutil.rmtree(cache_dir)
+            self.assertIsNotNone(model)
+
+
+if __name__ == "__main__":
+    unittest.main()