fix tinybert bug and add export script file for mass

model_zoo/official/nlp/mass/export.py

@@ -0,0 +1,82 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""export checkpoint file into air models"""
+
+import argparse
+import numpy as np
+
+from mindspore import Tensor, context
+from mindspore.common import dtype as mstype
+from mindspore.train.serialization import export
+
+from src.utils import Dictionary
+from src.utils.load_weights import load_infer_weights
+from src.transformer.transformer_for_infer import TransformerInferModel
+from config import TransformerConfig
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+parser = argparse.ArgumentParser(description='mass')
+parser.add_argument('--gigaword_infer_config', type=str, required=True, help='gigaword config file')
+parser.add_argument('--vocab_file', type=str, required=True, help='vocabulary file')
+args = parser.parse_args()
+
+def get_config(config_file):
+    tfm_config = TransformerConfig.from_json_file(config_file)
+    tfm_config.compute_type = mstype.float16
+    tfm_config.dtype = mstype.float32
+
+    return tfm_config
+
+
+if __name__ == '__main__':
+    vocab = Dictionary.load_from_persisted_dict(args.vocab_file)
+    config = get_config(args.gigaword_infer_config)
+    dec_len = config.max_decode_length
+    output_file_name = 'giga_' + str(dec_len) + '.air'
+
+    tfm_model = TransformerInferModel(config=config, use_one_hot_embeddings=False)
+    tfm_model.init_parameters_data()
+
+    params = tfm_model.trainable_params()
+    weights = load_infer_weights(config)
+
+    for param in params:
+        value = param.data
+        name = param.name
+
+        if name not in weights:
+            raise ValueError(f'{name} is not found in weights.')
+
+        with open('weight_after_deal.txt', 'a+') as f:
+            weights_name = name
+            f.write(weights_name + '\n')
+
+            if isinstance(value, Tensor):
+                if weights_name in weights:
+                    assert weights_name in weights
+                    param.set_data(Tensor(weights[weights_name], mstype.float32))
+                else:
+                    raise ValueError(f'{weights_name} is not found in checkpoint')
+            else:
+                raise TypeError(f'Type of {weights_name} is not Tensor')
+
+    print('    |    Load weights successfully.')
+    tfm_model.set_train(False)
+
+    source_ids = Tensor(np.ones((1, config.seq_length)).astype(np.int32))
+    source_mask = Tensor(np.ones((1, config.seq_length)).astype(np.int32))
+
+    export(tfm_model, source_ids, source_mask, file_name=output_file_name, file_format="AIR")

model_zoo/official/nlp/tinybert/export.py

@@ -32,6 +32,7 @@ args = parser.parse_args()
 
 DEFAULT_NUM_LABELS = 2
 DEFAULT_SEQ_LENGTH = 128
+DEFAULT_BS = 32
 task_params = {"SST-2": {"num_labels": 2, "seq_length": 64},
                "QNLI": {"num_labels": 2, "seq_length": 128},
                "MNLI": {"num_labels": 3, "seq_length": 128}}
@@ -60,6 +61,7 @@ class Task:
 if __name__ == '__main__':
     task = Task(args.task_name)
     td_student_net_cfg.seq_length = task.seq_length
+    td_student_net_cfg.batch_size = DEFAULT_BS
 
     eval_model = BertModelCLS(td_student_net_cfg, False, task.num_labels, 0.0, phase_type="student")
     param_dict = load_checkpoint(args.ckpt_file)