forked from mindspore-Ecosystem/mindspore
!10225 fix ncf export bug, modify export script for bert, mass, transformer to support mindir and GPU
From: @yuzhenhua666 Reviewed-by: @liangchenghui,@c_34 Signed-off-by: @c_34
This commit is contained in:
Gitee
commit
66741fb545
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@ -16,30 +16,30 @@
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import argparse
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import numpy as np
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from mindspore import Tensor, context
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import mindspore.common.dtype as mstype
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from mindspore.train.serialization import load_checkpoint, export
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from mindspore import Tensor, context, load_checkpoint, export
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from src.finetune_eval_model import BertCLSModel, BertSquadModel, BertNERModel
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from src.finetune_eval_config import bert_net_cfg
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from src.bert_for_finetune import BertNER
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from src.utils import convert_labels_to_index
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parser = argparse.ArgumentParser(description='Bert export')
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parser = argparse.ArgumentParser(description="Bert export")
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument('--use_crf', type=str, default="false", help='Use cfg, default is false.')
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parser.add_argument('--downstream_task', type=str, choices=["NER", "CLS", "SQUAD"], default="NER",
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help='at present，support NER only')
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parser.add_argument('--num_class', type=int, default=41, help='The number of class, default is 41.')
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parser.add_argument("--use_crf", type=str, default="false", help="Use cfg, default is false.")
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parser.add_argument("--downstream_task", type=str, choices=["NER", "CLS", "SQUAD"], default="NER",
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help="at present，support NER only")
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parser.add_argument("--num_class", type=int, default=41, help="The number of class, default is 41.")
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parser.add_argument("--batch_size", type=int, default=16, help="batch size")
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parser.add_argument('--label_file_path', type=str, default="", help='label file path, used in clue benchmark.')
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parser.add_argument('--ckpt_file', type=str, required=True, help='Bert ckpt file.')
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parser.add_argument('--output_file', type=str, default='Bert', help='bert output air 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("--label_file_path", type=str, default="", help="label file path, used in clue benchmark.")
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parser.add_argument("--ckpt_file", type=str, required=True, help="Bert ckpt file.")
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parser.add_argument("--file_name", type=str, default="Bert", help="bert output air 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("--device_target", type=str, default="Ascend",
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choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)")
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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args.device_id)
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)
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label_list = []
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with open(args.label_file_path) as f:
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@ -56,8 +56,7 @@ if args.use_crf.lower() == "true":
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else:
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number_labels = args.num_class
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if __name__ == '__main__':
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if __name__ == "__main__":
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if args.downstream_task == "NER":
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if args.use_crf.lower() == "true":
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net = BertNER(bert_net_cfg, args.batch_size, False, num_labels=number_labels,
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@ -83,4 +82,4 @@ if __name__ == '__main__':
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input_data = [input_ids, input_mask, token_type_id, label_ids]
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else:
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input_data = [input_ids, input_mask, token_type_id]
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export(net, *input_data, file_name=args.output_file, file_format=args.file_format)
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export(net, *input_data, file_name=args.file_name, file_format=args.file_format)
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@ -26,13 +26,18 @@ from src.utils.load_weights import load_infer_weights
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from src.transformer.transformer_for_infer import TransformerInferModel
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from config import TransformerConfig
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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parser = argparse.ArgumentParser(description='mass')
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parser = argparse.ArgumentParser(description="mass export")
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--file_name", type=str, default="mass", 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("--device_target", type=str, default="Ascend",
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choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)")
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parser.add_argument('--gigaword_infer_config', type=str, required=True, help='gigaword config file')
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parser.add_argument('--vocab_file', type=str, required=True, help='vocabulary file')
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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)
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def get_config(config_file):
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tfm_config = TransformerConfig.from_json_file(config_file)
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tfm_config.compute_type = mstype.float16
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@ -40,12 +45,10 @@ def get_config(config_file):
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return tfm_config
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if __name__ == '__main__':
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vocab = Dictionary.load_from_persisted_dict(args.vocab_file)
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config = get_config(args.gigaword_infer_config)
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dec_len = config.max_decode_length
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output_file_name = 'giga_' + str(dec_len) + '.air'
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tfm_model = TransformerInferModel(config=config, use_one_hot_embeddings=False)
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tfm_model.init_parameters_data()
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@ -79,4 +82,4 @@ if __name__ == '__main__':
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source_ids = Tensor(np.ones((1, config.seq_length)).astype(np.int32))
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source_mask = Tensor(np.ones((1, config.seq_length)).astype(np.int32))
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export(tfm_model, source_ids, source_mask, file_name=output_file_name, file_format="AIR")
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export(tfm_model, source_ids, source_mask, file_name=args.file_name, file_format=args.file_format)
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@ -29,9 +29,11 @@ parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--batch_size", type=int, default=1, help="batch size")
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parser.add_argument("--file_name", type=str, default="transformer", 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("--device_target", type=str, default="Ascend",
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choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)")
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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args.device_id)
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)
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if __name__ == '__main__':
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tfm_model = TransformerModel(config=transformer_net_cfg, is_training=False, use_one_hot_embeddings=False)
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@ -42,6 +44,4 @@ if __name__ == '__main__':
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source_ids = Tensor(np.ones((args.batch_size, transformer_net_cfg.seq_length)).astype(np.int32))
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source_mask = Tensor(np.ones((args.batch_size, transformer_net_cfg.seq_length)).astype(np.int32))
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dec_len = transformer_net_cfg.max_decode_length
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export(tfm_model, source_ids, source_mask, file_name=args.file_name + str(dec_len), file_format=args.file_format)
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export(tfm_model, source_ids, source_mask, file_name=args.file_name, file_format=args.file_format)
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@ -0,0 +1,68 @@
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# 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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"""ncf export file"""
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import argparse
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import numpy as np
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from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export
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import src.constants as rconst
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from src.config import cfg
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from ncf import NCFModel, PredictWithSigmoid
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parser = argparse.ArgumentParser(description='ncf export')
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
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parser.add_argument("--dataset", type=str, default="ml-1m", choices=["ml-1m", "ml-20m"], help="Dataset.")
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parser.add_argument("--file_name", type=str, default="ncf", 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("--device_target", type=str, default="Ascend",
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choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)")
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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)
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if __name__ == "__main__":
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topk = rconst.TOP_K
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num_eval_neg = rconst.NUM_EVAL_NEGATIVES
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if args.dataset == "ml-1m":
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num_eval_users = 6040
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num_eval_items = 3706
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elif args.dataset == "ml-20m":
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num_eval_users = 138493
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num_eval_items = 26744
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else:
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raise ValueError("not supported dataset")
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ncf_net = NCFModel(num_users=num_eval_users,
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num_items=num_eval_items,
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num_factors=cfg.num_factors,
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model_layers=cfg.layers,
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mf_regularization=0,
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mlp_reg_layers=[0.0, 0.0, 0.0, 0.0],
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mf_dim=16)
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param_dict = load_checkpoint(args.ckpt_file)
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load_param_into_net(ncf_net, param_dict)
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network = PredictWithSigmoid(ncf_net, topk, num_eval_neg)
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users = Tensor(np.zeros([cfg.eval_batch_size, 1]).astype(np.int32))
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items = Tensor(np.zeros([cfg.eval_batch_size, 1]).astype(np.int32))
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masks = Tensor(np.zeros([cfg.eval_batch_size, 1]).astype(np.float32))
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input_data = [users, items, masks]
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export(network, *input_data, file_name=args.file_name, file_format=args.file_format)
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@ -1,112 +0,0 @@
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# 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 NCF air file."""
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import os
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import argparse
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from absl import logging
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from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
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from mindspore import Tensor, context, Model
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import constants as rconst
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from dataset import create_dataset
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from metrics import NCFMetric
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from ncf import NCFModel, NetWithLossClass, TrainStepWrap, PredictWithSigmoid
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logging.set_verbosity(logging.INFO)
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def argparse_init():
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"""Argparse init method"""
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parser = argparse.ArgumentParser(description='NCF')
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parser.add_argument("--data_path", type=str, default="./dataset/") # The location of the input data.
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parser.add_argument("--dataset", type=str, default="ml-1m", choices=["ml-1m", "ml-20m"]) # Dataset to be trained and evaluated. ["ml-1m", "ml-20m"]
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parser.add_argument("--eval_batch_size", type=int, default=160000) # The batch size used for evaluation.
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parser.add_argument("--layers", type=int, default=[64, 32, 16]) # The sizes of hidden layers for MLP
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parser.add_argument("--num_factors", type=int, default=16) # The Embedding size of MF model.
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parser.add_argument("--output_path", type=str, default="./output/") # The location of the output file.
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parser.add_argument("--eval_file_name", type=str, default="eval.log") # Eval output file.
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parser.add_argument("--checkpoint_file_path", type=str, default="./checkpoint/NCF.ckpt") # The location of the checkpoint file.
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return parser
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def export_air_file():
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""""Export file for eval"""
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parser = argparse_init()
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args, _ = parser.parse_known_args()
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if not os.path.exists(args.output_path):
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os.makedirs(args.output_path)
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layers = args.layers
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num_factors = args.num_factors
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topk = rconst.TOP_K
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num_eval_neg = rconst.NUM_EVAL_NEGATIVES
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ds_eval, num_eval_users, num_eval_items = create_dataset(test_train=False, data_dir=args.data_path,
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dataset=args.dataset, train_epochs=0,
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eval_batch_size=args.eval_batch_size)
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print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
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ncf_net = NCFModel(num_users=num_eval_users,
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num_items=num_eval_items,
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num_factors=num_factors,
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model_layers=layers,
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mf_regularization=0,
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mlp_reg_layers=[0.0, 0.0, 0.0, 0.0],
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mf_dim=16)
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param_dict = load_checkpoint(args.checkpoint_file_path)
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load_param_into_net(ncf_net, param_dict)
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loss_net = NetWithLossClass(ncf_net)
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train_net = TrainStepWrap(loss_net)
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train_net.set_train()
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eval_net = PredictWithSigmoid(ncf_net, topk, num_eval_neg)
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ncf_metric = NCFMetric()
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model = Model(train_net, eval_network=eval_net, metrics={"ncf": ncf_metric})
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ncf_metric.clear()
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out = model.eval(ds_eval)
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eval_file_path = os.path.join(args.output_path, args.eval_file_name)
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eval_file = open(eval_file_path, "a+")
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eval_file.write("EvalCallBack: HR = {}, NDCG = {}\n".format(out['ncf'][0], out['ncf'][1]))
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eval_file.close()
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print("EvalCallBack: HR = {}, NDCG = {}".format(out['ncf'][0], out['ncf'][1]))
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param_dict = load_checkpoint(args.checkpoint_file_path)
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# load the parameter into net
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load_param_into_net(eval_net, param_dict)
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input_tensor_list = []
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for data in ds_eval:
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for j in data:
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input_tensor_list.append(Tensor(j))
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print(len(a))
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break
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print(input_tensor_list)
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export(eval_net, *input_tensor_list, file_name='NCF.air', file_format='AIR')
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if __name__ == '__main__':
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devid = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE,
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device_target="Davinci",
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save_graphs=True,
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device_id=devid)
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export_air_file()
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