增加bert模型转换代码

PyTorch/NLP/BERT/convert_tf_checkpoint.py

+# Copyright (c) 2019 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.
+
+import torch
+import argparse
+
+#from modeling import BertForPreTraining, BertConfig
+
+from model import BertForPreTraining, BertConfig
+
+def parse_arguments():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--bert_model", default="bert-large-uncased", type=str,
+                        help="Bert pre-trained model selected in the list: bert-base-uncased, "
+                             "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
+    parser.add_argument('--tf_checkpoint',
+                        type=str,
+                        default="/google_bert_data",
+                        help="Path to directory containing TF checkpoint")
+    parser.add_argument('--bert_config_path',
+                        type=str,
+                        default="/workspace/phase1",
+                        help="Path bert_config.json is located in")
+    parser.add_argument('--output_checkpoint', type=str,
+                        default='./checkpoint.pt',
+                        help="Path to output PyT checkpoint")
+
+    return parser.parse_args()
+
+def prepare_model(args, device):
+
+    # Prepare model
+    config = BertConfig.from_json_file(args.bert_config_path)
+
+    # Padding for divisibility by 8
+    if config.vocab_size % 8 != 0:
+        config.vocab_size += 8 - (config.vocab_size % 8)
+        print('padded vocab size to: {}'.format(config.vocab_size))
+
+    # Set some options that the config file is expected to have (but don't need to be set properly
+    # at this point)
+    config.pad = False
+    config.unpad = False
+    config.dense_seq_output = False
+    config.fused_mha = False
+    config.fused_gelu_bias = False
+    config.fuse_qkv = False
+    config.fuse_scale = False
+    config.fuse_mask = False
+    config.fuse_dropout = False
+    config.apex_softmax = False
+    config.enable_stream = False
+    if config.fuse_mask == True: config.apex_softmax = True
+    if config.pad == False: config.enable_stream = True
+    if config.unpad == True: config.fused_mha = False
+
+    #Load from TF checkpoint
+    model = BertForPreTraining.from_pretrained(args.tf_checkpoint, from_tf=True, config=config)
+
+    return model
+
+def main():
+    args = parse_arguments()
+    device = torch.device("cuda")
+
+    model = prepare_model(args, device)
+
+    torch.save({'model' : model.state_dict() }, args.output_checkpoint)
+
+
+if __name__ == "__main__":
+    main()
+