add test for initialization of Bert2Rnd

examples/run_summarization.py

@@ -0,0 +1,49 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team 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.
+""" Finetuning seq2seq models for abstractive summarization.
+
+The finetuning method for abstractive summarization is inspired by [1]. We
+concatenate the document and summary, mask words of the summary at random and
+maximizing the likelihood of masked words.
+
+[1] Dong Li, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng
+Gao, Ming Zhou, and Hsiao-Wuen Hon.  “Unified Language Model Pre-Training for
+Natural Language Understanding and Generation.” (May 2019) ArXiv:1905.03197
+"""
+
+import logging
+import random
+
+import numpy as np
+import torch
+
+logger = logging.getLogger(__name__)
+
+
+def set_seed(args):
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    if args.n_gpu > 0:
+        torch.cuda.manual_seed_all(args.seed)
+
+
+def train(args, train_dataset, model, tokenizer):
+    raise NotImplementedError
+
+
+def evaluate(args, model, tokenizer, prefix=""):
+    raise NotImplementedError

transformers/tests/modeling_bert_test.py

@@ -259,12 +259,12 @@ class BertModelTest(CommonTestCases.CommonModelTester):
             config.num_choices = self.num_choices
             model = Bert2Rnd(config=config)
             model.eval()
-            bert2bert_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
-            bert2bert_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
-            bert2bert_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
-            _ = model(bert2bert_inputs_ids,
-                      attention_mask=bert2bert_input_mask,
-                      token_type_ids=bert2bert_token_type_ids)
+            bert2rnd_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+            bert2rnd_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+            bert2rnd_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+            _ = model(bert2rnd_inputs_ids,
+                      attention_mask=bert2rnd_input_mask,
+                      token_type_ids=bert2rnd_token_type_ids)
 
         def prepare_config_and_inputs_for_common(self):
             config_and_inputs = self.prepare_config_and_inputs()