export script for tinybert and transformer

model_zoo/official/nlp/tinybert/export.py

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+# 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 re
+import argparse
+import numpy as np
+
+from mindspore import Tensor, context
+from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
+
+from src.td_config import td_student_net_cfg
+from src.tinybert_model import BertModelCLS
+
+parser = argparse.ArgumentParser(description='tinybert task distill')
+parser.add_argument('--ckpt_file', type=str, required=True, help='tinybert ckpt file.')
+parser.add_argument('--output_file', type=str, default='tinybert.air', help='tinybert output air name.')
+parser.add_argument('--task_name', type=str, default='SST-2', choices=['SST-2', 'QNLI', 'MNLI'], help='task name')
+args = parser.parse_args()
+
+DEFAULT_NUM_LABELS = 2
+DEFAULT_SEQ_LENGTH = 128
+task_params = {"SST-2": {"num_labels": 2, "seq_length": 64},
+               "QNLI": {"num_labels": 2, "seq_length": 128},
+               "MNLI": {"num_labels": 3, "seq_length": 128}}
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+class Task:
+    """
+    Encapsulation class of get the task parameter.
+    """
+    def __init__(self, task_name):
+        self.task_name = task_name
+
+    @property
+    def num_labels(self):
+        if self.task_name in task_params and "num_labels" in task_params[self.task_name]:
+            return task_params[self.task_name]["num_labels"]
+        return DEFAULT_NUM_LABELS
+
+    @property
+    def seq_length(self):
+        if self.task_name in task_params and "seq_length" in task_params[self.task_name]:
+            return task_params[self.task_name]["seq_length"]
+        return DEFAULT_SEQ_LENGTH
+
+if __name__ == '__main__':
+    task = Task(args.task_name)
+    td_student_net_cfg.seq_length = task.seq_length
+
+    eval_model = BertModelCLS(td_student_net_cfg, False, task.num_labels, 0.0, phase_type="student")
+    param_dict = load_checkpoint(args.ckpt_file)
+    new_param_dict = {}
+    for key, value in param_dict.items():
+        new_key = re.sub('tinybert_', 'bert_', key)
+        new_key = re.sub('^bert.', '', new_key)
+        new_param_dict[new_key] = value
+
+    load_param_into_net(eval_model, new_param_dict)
+    eval_model.set_train(False)
+
+    input_ids = Tensor(np.zeros((td_student_net_cfg.batch_size, task.seq_length), np.int32))
+    token_type_id = Tensor(np.zeros((td_student_net_cfg.batch_size, task.seq_length), np.int32))
+    input_mask = Tensor(np.zeros((td_student_net_cfg.batch_size, task.seq_length), np.int32))
+
+    export(eval_model, input_ids, token_type_id, input_mask, file_name=args.output_file, file_format="AIR")

model_zoo/official/nlp/transformer/export.py

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+# 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 numpy as np
+
+from mindspore import Tensor, context
+from mindspore.train.serialization import load_param_into_net, export
+
+from src.transformer_model import TransformerModel
+from src.eval_config import cfg, transformer_net_cfg
+from eval import load_weights
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+if __name__ == '__main__':
+    tfm_model = TransformerModel(config=transformer_net_cfg, is_training=False, use_one_hot_embeddings=False)
+
+    parameter_dict = load_weights(cfg.model_file)
+    load_param_into_net(tfm_model, parameter_dict)
+
+    source_ids = Tensor(np.ones((1, 128)).astype(np.int32))
+    source_mask = Tensor(np.ones((1, 128)).astype(np.int32))
+
+    dec_len = transformer_net_cfg.max_decode_length
+
+    export(tfm_model, source_ids, source_mask, file_name="len" + str(dec_len) + ".air", file_format="AIR")