adding TF 2.0 model

transformers/configuration_ctrl.py

@@ -53,8 +53,8 @@ class CTRLConfig(PretrainedConfig):
     def __init__(
         self,
         vocab_size_or_config_json_file=246534,
-        n_positions=50000,
-        n_ctx=512,
+        n_positions=256,
+        n_ctx=256,
         n_embd=1280,
         dff=8192,
         n_layer=48,

transformers/modeling_ctrl.py

@@ -351,7 +351,7 @@ class CTRLModel(CTRLPreTrainedModel):
 
         x = self.w(input_ids)
         # x = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded
-        seq_len = input_ids.shape[1]
+        seq_len = input_ids.shape[-1]
         mask = torch.triu(torch.ones(seq_len, seq_len), 1).to(x.device)
 
         x *= np.sqrt(self.d_model_size)

transformers/modeling_roberta.py

@@ -172,7 +172,8 @@ class RobertaModel(BertModel):
         if input_ids[:, 0].sum().item() != 0:
             logger.warning("A sequence with no special tokens has been passed to the RoBERTa model. "
                            "This model requires special tokens in order to work. "
-                           "Please specify add_special_tokens=True in your encoding.")
+                           "Please specify add_special_tokens=True in your tokenize.encode()"
+                           "or tokenizer.convert_tokens_to_ids().")
         return super(RobertaModel, self).forward(input_ids,
                                                  attention_mask=attention_mask,
                                                  token_type_ids=token_type_ids,

transformers/tests/modeling_tf_ctrl_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 unittest
+import shutil
+import pytest
+import sys
+
+from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
+from .configuration_common_test import ConfigTester
+
+from transformers import CTRLConfig, is_tf_available
+
+if is_tf_available():
+    import tensorflow as tf
+    from transformers.modeling_tf_ctrl import (TFCTRLModel, TFCTRLLMHeadModel,
+                                                TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
+else:
+    pytestmark = pytest.mark.skip("Require TensorFlow")
+
+
+class TFCTRLModelTest(TFCommonTestCases.TFCommonModelTester):
+
+    all_model_classes = (TFCTRLModel, TFCTRLLMHeadModel) if is_tf_available() else ()
+
+    class TFCTRLModelTester(object):
+
+        def __init__(self,
+                     parent,
+                     batch_size=13,
+                     seq_length=7,
+                     is_training=True,
+                     use_token_type_ids=True,
+                     use_input_mask=True,
+                     use_labels=True,
+                     use_mc_token_ids=True,
+                     vocab_size=99,
+                     hidden_size=32,
+                     num_hidden_layers=5,
+                     num_attention_heads=4,
+                     intermediate_size=37,
+                     hidden_act="gelu",
+                     hidden_dropout_prob=0.1,
+                     attention_probs_dropout_prob=0.1,
+                     max_position_embeddings=512,
+                     type_vocab_size=16,
+                     type_sequence_label_size=2,
+                     initializer_range=0.02,
+                     num_labels=3,
+                     num_choices=4,
+                     scope=None,
+                     ):
+            self.parent = parent
+            self.batch_size = batch_size
+            self.seq_length = seq_length
+            self.is_training = is_training
+            self.use_token_type_ids = use_token_type_ids
+            self.use_input_mask = use_input_mask
+            self.use_labels = use_labels
+            self.use_mc_token_ids = use_mc_token_ids
+            self.vocab_size = vocab_size
+            self.hidden_size = hidden_size
+            self.num_hidden_layers = num_hidden_layers
+            self.num_attention_heads = num_attention_heads
+            self.intermediate_size = intermediate_size
+            self.hidden_act = hidden_act
+            self.hidden_dropout_prob = hidden_dropout_prob
+            self.attention_probs_dropout_prob = attention_probs_dropout_prob
+            self.max_position_embeddings = max_position_embeddings
+            self.type_vocab_size = type_vocab_size
+            self.type_sequence_label_size = type_sequence_label_size
+            self.initializer_range = initializer_range
+            self.num_labels = num_labels
+            self.num_choices = num_choices
+            self.scope = scope
+
+        def prepare_config_and_inputs(self):
+            input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+            input_mask = None
+            if self.use_input_mask:
+                input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
+
+            token_type_ids = None
+            if self.use_token_type_ids:
+                token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
+
+            mc_token_ids = None
+            if self.use_mc_token_ids:
+                mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
+
+            sequence_labels = None
+            token_labels = None
+            choice_labels = None
+            if self.use_labels:
+                sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+                token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+                choice_labels = ids_tensor([self.batch_size], self.num_choices)
+
+            config = CTRLConfig(
+                vocab_size_or_config_json_file=self.vocab_size,
+                n_embd=self.hidden_size,
+                n_layer=self.num_hidden_layers,
+                n_head=self.num_attention_heads,
+                # intermediate_size=self.intermediate_size,
+                # hidden_act=self.hidden_act,
+                # hidden_dropout_prob=self.hidden_dropout_prob,
+                # attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+                n_positions=self.max_position_embeddings,
+                n_ctx=self.max_position_embeddings
+                # type_vocab_size=self.type_vocab_size,
+                # initializer_range=self.initializer_range
+            )
+
+            head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
+
+            return config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels
+
+        def create_and_check_ctrl_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
+            model = TFCTRLModel(config=config)
+            inputs = {'input_ids': input_ids,
+                      'attention_mask': input_mask,
+                      'token_type_ids': token_type_ids}
+            sequence_output = model(inputs)[0]
+
+            inputs = [input_ids, None, input_mask]  # None is the input for 'past'
+            sequence_output = model(inputs)[0]
+
+            sequence_output = model(input_ids)[0]
+
+            result = {
+                "sequence_output": sequence_output.numpy(),
+            }
+            self.parent.assertListEqual(
+                list(result["sequence_output"].shape),
+                [self.batch_size, self.seq_length, self.hidden_size])
+
+
+        def create_and_check_ctrl_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
+            model = TFCTRLLMHeadModel(config=config)
+            inputs = {'input_ids': input_ids,
+                      'attention_mask': input_mask,
+                      'token_type_ids': token_type_ids}
+            prediction_scores = model(inputs)[0]
+            result = {
+                "prediction_scores": prediction_scores.numpy(),
+            }
+            self.parent.assertListEqual(
+                list(result["prediction_scores"].shape),
+                [self.batch_size, self.seq_length, self.vocab_size])
+
+        def prepare_config_and_inputs_for_common(self):
+            config_and_inputs = self.prepare_config_and_inputs()
+
+            (config, input_ids, input_mask, head_mask, token_type_ids,
+             mc_token_ids, sequence_labels, token_labels, choice_labels) = config_and_inputs
+
+            inputs_dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
+            return config, inputs_dict
+
+    def setUp(self):
+        self.model_tester = TFCTRLModelTest.TFCTRLModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=CTRLConfig, n_embd=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_ctrl_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_ctrl_model(*config_and_inputs)
+
+    def test_ctrl_lm_head(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_ctrl_lm_head(*config_and_inputs)
+
+    @pytest.mark.slow
+    def test_model_from_pretrained(self):
+        cache_dir = "/tmp/transformers_test/"
+        for model_name in list(TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
+            model = TFCTRLModel.from_pretrained(model_name, cache_dir=cache_dir)
+            shutil.rmtree(cache_dir)
+            self.assertIsNotNone(model)
+
+if __name__ == "__main__":
+    unittest.main()
+

transformers/tests/modeling_tf_gpt2_test.py

@@ -222,7 +222,7 @@ class TFGPT2ModelTest(TFCommonTestCases.TFCommonModelTester):
     @pytest.mark.slow
     def test_model_from_pretrained(self):
         cache_dir = "/tmp/transformers_test/"
-        for model_name in list(TF_gpt2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
+        for model_name in list(TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
             model = TFGPT2Model.from_pretrained(model_name, cache_dir=cache_dir)
             shutil.rmtree(cache_dir)
             self.assertIsNotNone(model)