chenbo
5 years ago
parent
06f80f7043
commit
ffd8da9911
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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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"""eval_criteo."""
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import os
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import sys
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import time
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import argparse
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from mindspore import context
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from mindspore.train.model import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.autodis import ModelBuilder, AUCMetric
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from src.config import DataConfig, ModelConfig, TrainConfig
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from src.dataset import create_dataset, DataType
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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parser = argparse.ArgumentParser(description='CTR Prediction')
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parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
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parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
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parser.add_argument('--device_target', type=str, default="Ascend", choices=["Ascend"],
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help='Default: Ascend')
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args_opt, _ = parser.parse_known_args()
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, device_id=device_id)
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def add_write(file_path, print_str):
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with open(file_path, 'a+', encoding='utf-8') as file_out:
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file_out.write(print_str + '\n')
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if __name__ == '__main__':
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data_config = DataConfig()
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model_config = ModelConfig()
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train_config = TrainConfig()
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ds_eval = create_dataset(args_opt.dataset_path, train_mode=False,
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epochs=1, batch_size=train_config.batch_size,
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data_type=DataType(data_config.data_format))
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model_builder = ModelBuilder(ModelConfig, TrainConfig)
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train_net, eval_net = model_builder.get_train_eval_net()
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train_net.set_train()
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eval_net.set_train(False)
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auc_metric = AUCMetric()
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model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
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param_dict = load_checkpoint(args_opt.checkpoint_path)
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load_param_into_net(eval_net, param_dict)
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start = time.time()
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res = model.eval(ds_eval)
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eval_time = time.time() - start
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time_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
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out_str = f'{time_str} AUC: {list(res.values())[0]}, eval time: {eval_time}s.'
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print(out_str)
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add_write('./auc.log', str(out_str))
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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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"""hub config."""
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from src.autodis import ModelBuilder
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from src.config import ModelConfig, TrainConfig
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def create_network(name, *args, **kwargs):
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if name == 'autodis':
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model_config = ModelConfig()
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train_config = TrainConfig()
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model_builder = ModelBuilder(model_config, train_config)
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_, autodis_eval_net = model_builder.get_train_eval_net()
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return autodis_eval_net
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raise NotImplementedError(f"{name} is not implemented in the repo")
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#!/bin/bash
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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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echo "Please run the script as: "
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echo "sh scripts/run_eval.sh DEVICE_ID DEVICE_TARGET DATASET_PATH CHECKPOINT_PATH"
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echo "for example: sh scripts/run_eval.sh 0 GPU /dataset_path /checkpoint_path"
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echo "After running the script, the network runs in the background, The log will be generated in ms_log/eval_output.log"
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export DEVICE_ID=$1
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DEVICE_TARGET=$2
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DATA_URL=$3
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CHECKPOINT_PATH=$4
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mkdir -p ms_log
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CUR_DIR=`pwd`
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export GLOG_log_dir=${CUR_DIR}/ms_log
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export GLOG_logtostderr=0
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python -u eval.py \
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--dataset_path=$DATA_URL \
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--checkpoint_path=$CHECKPOINT_PATH \
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--device_target=$DEVICE_TARGET > ms_log/eval_output.log 2>&1 &
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#!/bin/bash
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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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echo "Please run the script as: "
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echo "sh scripts/run_standalone_train.sh DEVICE_ID/CUDA_VISIBLE_DEVICES DEVICE_TARGET DATASET_PATH"
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echo "for example: sh scripts/run_standalone_train.sh 0 GPU /dataset_path"
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echo "After running the script, the network runs in the background, The log will be generated in ms_log/output.log"
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DEVICE_TARGET=$2
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if [ "$DEVICE_TARGET" = "GPU" ]
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then
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export CUDA_VISIBLE_DEVICES=$1
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fi
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if [ "$DEVICE_TARGET" = "Ascend" ]
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then
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export DEVICE_ID=$1
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fi
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DATA_URL=$3
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mkdir -p ms_log
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CUR_DIR=`pwd`
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export GLOG_log_dir=${CUR_DIR}/ms_log
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export GLOG_logtostderr=0
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python -u train.py \
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--dataset_path=$DATA_URL \
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--ckpt_path="checkpoint" \
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--eval_file_name='auc.log' \
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--loss_file_name='loss.log' \
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--device_target=$DEVICE_TARGET \
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--do_eval=True > ms_log/output.log 2>&1 &
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File diff suppressed because it is too large
Load Diff
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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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# httpwww.apache.orglicensesLICENSE-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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"""
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Defined callback for DeepFM.
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"""
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import time
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from mindspore.train.callback import Callback
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def add_write(file_path, out_str):
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with open(file_path, 'a+', encoding='utf-8') as file_out:
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file_out.write(out_str + '\n')
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class EvalCallBack(Callback):
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"""
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Monitor the loss in training.
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If the loss is NAN or INF terminating training.
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Note
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If per_print_times is 0 do not print loss.
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"""
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def __init__(self, model, eval_dataset, auc_metric, eval_file_path):
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super(EvalCallBack, self).__init__()
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self.model = model
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self.eval_dataset = eval_dataset
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self.aucMetric = auc_metric
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self.aucMetric.clear()
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self.eval_file_path = eval_file_path
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def epoch_end(self, run_context):
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start_time = time.time()
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out = self.model.eval(self.eval_dataset)
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eval_time = int(time.time() - start_time)
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time_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
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out_str = "{} EvalCallBack metric{}; eval_time{}s".format(
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time_str, out.values(), eval_time)
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print(out_str)
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add_write(self.eval_file_path, out_str)
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class LossCallBack(Callback):
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"""
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Monitor the loss in training.
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If the loss is NAN or INF terminating training.
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Note
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If per_print_times is 0 do not print loss.
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Args
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loss_file_path (str) The file absolute path, to save as loss_file;
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per_print_times (int) Print loss every times. Default 1.
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"""
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def __init__(self, loss_file_path, per_print_times=1):
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super(LossCallBack, self).__init__()
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if not isinstance(per_print_times, int) or per_print_times < 0:
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raise ValueError("print_step must be int and >= 0.")
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self.loss_file_path = loss_file_path
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self._per_print_times = per_print_times
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def step_end(self, run_context):
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"""Monitor the loss in training."""
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cb_params = run_context.original_args()
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loss = cb_params.net_outputs.asnumpy()
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cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
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cur_num = cb_params.cur_step_num
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if self._per_print_times != 0 and cur_num % self._per_print_times == 0:
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with open(self.loss_file_path, "a+") as loss_file:
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time_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
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loss_file.write("{} epoch: {} step: {}, loss is {}\n".format(
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time_str, cb_params.cur_epoch_num, cur_step_in_epoch, loss))
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print("epoch: {} step: {}, loss is {}\n".format(
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cb_params.cur_epoch_num, cur_step_in_epoch, loss))
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class TimeMonitor(Callback):
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"""
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Time monitor for calculating cost of each epoch.
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Args
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data_size (int) step size of an epoch.
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"""
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def __init__(self, data_size):
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super(TimeMonitor, self).__init__()
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self.data_size = data_size
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def epoch_begin(self, run_context):
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self.epoch_time = time.time()
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def epoch_end(self, run_context):
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epoch_mseconds = (time.time() - self.epoch_time) * 1000
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per_step_mseconds = epoch_mseconds / self.data_size
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print("epoch time: {0}, per step time: {1}".format(epoch_mseconds, per_step_mseconds), flush=True)
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def step_begin(self, run_context):
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self.step_time = time.time()
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def step_end(self, run_context):
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step_mseconds = (time.time() - self.step_time) * 1000
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print(f"step time {step_mseconds}", flush=True)
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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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"""
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network config setting, will be used in train.py and eval.py
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"""
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class DataConfig:
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"""
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Define parameters of dataset.
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"""
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data_vocab_size = 184965
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train_num_of_parts = 21
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test_num_of_parts = 3
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batch_size = 1000
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data_field_size = 39
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# dataset format, 1: mindrecord, 2: tfrecord, 3: h5
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data_format = 2
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class ModelConfig:
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"""
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Define parameters of model.
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"""
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batch_size = DataConfig.batch_size
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data_field_size = DataConfig.data_field_size
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data_vocab_size = DataConfig.data_vocab_size
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data_emb_dim = 80
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deep_layer_args = [[400, 400, 512], "relu"]
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init_args = [-0.01, 0.01]
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weight_bias_init = ['normal', 'normal']
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keep_prob = 0.9
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split_index = 13
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hash_size = 20
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temperature = 1e-5
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class TrainConfig:
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"""
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Define parameters of training.
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"""
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batch_size = DataConfig.batch_size
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l2_coef = 1e-6
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learning_rate = 1e-5
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epsilon = 1e-8
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loss_scale = 1024.0
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train_epochs = 15
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save_checkpoint = True
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ckpt_file_name_prefix = "autodis"
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save_checkpoint_steps = 1
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keep_checkpoint_max = 15
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eval_callback = True
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loss_callback = True
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File diff suppressed because it is too large
Load Diff
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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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"""train_criteo."""
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import os
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import sys
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import argparse
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from mindspore import context
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from mindspore.context import ParallelMode
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from mindspore.communication.management import init, get_rank
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from mindspore.train.model import Model
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
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from mindspore.common import set_seed
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#from mindspore.profiler import Profiler
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from src.autodis import ModelBuilder, AUCMetric
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from src.config import DataConfig, ModelConfig, TrainConfig
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from src.dataset import create_dataset, DataType
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from src.callback import EvalCallBack, LossCallBack
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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parser = argparse.ArgumentParser(description='CTR Prediction')
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parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
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parser.add_argument('--ckpt_path', type=str, default=None, help='Checkpoint path')
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parser.add_argument('--eval_file_name', type=str, default="./auc.log",
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help='Auc log file path. Default: "./auc.log"')
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parser.add_argument('--loss_file_name', type=str, default="./loss.log",
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help='Loss log file path. Default: "./loss.log"')
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parser.add_argument('--do_eval', type=str, default='True', choices=["True", "False"],
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help='Do evaluation or not, only support "True" or "False". Default: "True"')
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parser.add_argument('--device_target', type=str, default="Ascend", choices=["Ascend"],
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help='Default: Ascend')
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args_opt, _ = parser.parse_known_args()
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args_opt.do_eval = args_opt.do_eval == 'True'
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rank_size = int(os.environ.get("RANK_SIZE", 1))
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set_seed(1)
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if __name__ == '__main__':
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data_config = DataConfig()
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model_config = ModelConfig()
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train_config = TrainConfig()
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if rank_size > 1:
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if args_opt.device_target == "Ascend":
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, device_id=device_id)
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True)
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init()
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rank_id = int(os.environ.get('RANK_ID'))
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else:
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print("Unsupported device_target ", args_opt.device_target)
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exit()
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else:
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if args_opt.device_target == "Ascend":
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, device_id=device_id)
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else:
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print("Unsupported device_target ", args_opt.device_target)
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exit()
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rank_size = None
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rank_id = None
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# Init Profiler
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#profiler = Profiler(output_path='./data', is_detail=True, is_show_op_path=False, subgraph='all')
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ds_train = create_dataset(args_opt.dataset_path,
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train_mode=True,
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||||
epochs=1,
|
||||
batch_size=train_config.batch_size,
|
||||
data_type=DataType(data_config.data_format),
|
||||
rank_size=rank_size,
|
||||
rank_id=rank_id)
|
||||
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
|
||||
|
||||
steps_size = ds_train.get_dataset_size()
|
||||
|
||||
model_builder = ModelBuilder(ModelConfig, TrainConfig)
|
||||
train_net, eval_net = model_builder.get_train_eval_net()
|
||||
auc_metric = AUCMetric()
|
||||
model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
|
||||
|
||||
time_callback = TimeMonitor(data_size=ds_train.get_dataset_size())
|
||||
loss_callback = LossCallBack(loss_file_path=args_opt.loss_file_name)
|
||||
callback_list = [time_callback, loss_callback]
|
||||
|
||||
if train_config.save_checkpoint:
|
||||
if rank_size:
|
||||
train_config.ckpt_file_name_prefix = train_config.ckpt_file_name_prefix + str(get_rank())
|
||||
args_opt.ckpt_path = os.path.join(args_opt.ckpt_path, 'ckpt_' + str(get_rank()) + '/')
|
||||
config_ck = CheckpointConfig(save_checkpoint_steps=train_config.save_checkpoint_steps,
|
||||
keep_checkpoint_max=train_config.keep_checkpoint_max)
|
||||
ckpt_cb = ModelCheckpoint(prefix=train_config.ckpt_file_name_prefix,
|
||||
directory=args_opt.ckpt_path,
|
||||
config=config_ck)
|
||||
callback_list.append(ckpt_cb)
|
||||
|
||||
if args_opt.do_eval:
|
||||
ds_eval = create_dataset(args_opt.dataset_path, train_mode=False,
|
||||
epochs=1,
|
||||
batch_size=train_config.batch_size,
|
||||
data_type=DataType(data_config.data_format))
|
||||
eval_callback = EvalCallBack(model, ds_eval, auc_metric,
|
||||
eval_file_path=args_opt.eval_file_name)
|
||||
callback_list.append(eval_callback)
|
||||
model.train(train_config.train_epochs, ds_train, callbacks=callback_list)
|
||||
Loading…
Reference in new issue