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98 lines
4.0 KiB
98 lines
4.0 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""export checkpoint file into air models"""
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import argparse
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import numpy as np
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from mindspore import Tensor, context, Parameter
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from mindspore.common import dtype as mstype
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from mindspore.train.serialization import export
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from config import GNMTConfig
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from src.gnmt_model.gnmt import GNMT
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from src.gnmt_model.gnmt_for_infer import GNMTInferCell
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from src.utils import zero_weight
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from src.utils.load_weights import load_infer_weights
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parser = argparse.ArgumentParser(description="gnmt_v2 export")
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parser.add_argument("--file_name", type=str, default="gnmt_v2", help="output file name.")
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parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
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parser.add_argument('--infer_config', type=str, required=True, help='gnmt_v2 config file')
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parser.add_argument("--existed_ckpt", type=str, required=True, help="existed checkpoint address.")
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parser.add_argument('--vocab_file', type=str, required=True, help='vocabulary file')
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parser.add_argument("--bpe_codes", type=str, required=True, help="bpe codes to use.")
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args = parser.parse_args()
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context.set_context(
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mode=context.GRAPH_MODE,
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save_graphs=False,
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device_target="Ascend",
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reserve_class_name_in_scope=False)
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def get_config(config_file):
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tfm_config = GNMTConfig.from_json_file(config_file)
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tfm_config.compute_type = mstype.float16
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tfm_config.dtype = mstype.float32
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return tfm_config
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if __name__ == '__main__':
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config = get_config(args.infer_config)
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config.existed_ckpt = args.existed_ckpt
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vocab = args.vocab_file
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bpe_codes = args.bpe_codes
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tfm_model = GNMT(config=config,
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is_training=False,
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use_one_hot_embeddings=False)
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params = tfm_model.trainable_params()
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weights = load_infer_weights(config)
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for param in params:
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value = param.data
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weights_name = param.name
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if weights_name not in weights:
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raise ValueError(f"{weights_name} is not found in weights.")
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if isinstance(value, Tensor):
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if weights_name in weights:
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assert weights_name in weights
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if isinstance(weights[weights_name], Parameter):
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if param.data.dtype == "Float32":
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param.set_data(Tensor(weights[weights_name].data.asnumpy(), mstype.float32))
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elif param.data.dtype == "Float16":
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param.set_data(Tensor(weights[weights_name].data.asnumpy(), mstype.float16))
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elif isinstance(weights[weights_name], Tensor):
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param.set_data(Tensor(weights[weights_name].asnumpy(), config.dtype))
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elif isinstance(weights[weights_name], np.ndarray):
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param.set_data(Tensor(weights[weights_name], config.dtype))
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else:
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param.set_data(weights[weights_name])
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else:
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print("weight not found in checkpoint: " + weights_name)
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param.set_data(zero_weight(value.asnumpy().shape))
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print(" | Load weights successfully.")
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tfm_infer = GNMTInferCell(tfm_model)
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tfm_infer.set_train(False)
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source_ids = Tensor(np.ones((config.batch_size, config.seq_length)).astype(np.int32))
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source_mask = Tensor(np.ones((config.batch_size, config.seq_length)).astype(np.int32))
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export(tfm_infer, source_ids, source_mask, file_name=args.file_name, file_format=args.file_format)
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