!2554 [华为大学][高校贡献][MindSpore]-NCF问题单回归修改

Merge pull request !2554 from 迎接光辉岁月/r1.2

official/recommend/ncf/modelarts/dataset.py

-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2022 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.

official/recommend/ncf/modelarts/export_for_infer.py

+# Copyright 2022 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.
+# ============================================================================
+"""Training entry file"""
+
+import argparse
+import ast
+
+import numpy as np
+from absl import logging
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore import context, export, Tensor
+from mindspore.common import set_seed
+from ncf import NCFModel, PredictWithSigmoid
+import src.constants as rconst
+from src.config import cfg
+
+
+set_seed(1)
+
+logging.set_verbosity(logging.INFO)
+
+
+parser = argparse.ArgumentParser(description='ncf export')
+
+parser.add_argument("--device_id", type=int, default=0, help="Device id")
+parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
+parser.add_argument("--dataset", type=str, default="ml-1m", choices=["ml-1m", "ml-20m"], help="Dataset.")
+parser.add_argument("--file_name", type=str, default="ncf", help="output file name.")
+parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format')
+parser.add_argument("--device_target", type=str, default="Ascend",
+                    choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)")
+parser.add_argument("--is_row_vector_input", type=ast.literal_eval, default=True,
+                    help="Change model input into row vector for MindX SDK inference")
+args = parser.parse_args()
+
+def export_for_infer():
+    """export method"""
+    if args.dataset == "ml-1m":
+        num_eval_users = 6040
+        num_eval_items = 3706
+    elif args.dataset == "ml-20m":
+        num_eval_users = 138493
+        num_eval_items = 26744
+    else:
+        raise ValueError("not supported dataset")
+
+    layers = cfg.layers
+    num_factors = cfg.num_factors
+    ncf_net = NCFModel(num_users=num_eval_users,
+                       num_items=num_eval_items,
+                       num_factors=num_factors,
+                       model_layers=layers,
+                       mf_regularization=0,
+                       mlp_reg_layers=[0.0, 0.0, 0.0, 0.0],
+                       mf_dim=16,
+                       is_row_vector_input=args.is_row_vector_input)
+
+    frozen_to_air_args = {'ckpt_file': args.ckpt_file,
+                          'topk': rconst.TOP_K,
+                          'num_eval_neg': rconst.NUM_EVAL_NEGATIVES,
+                          'file_name': args.file_name,
+                          'file_format': args.file_format}
+    frozen_to_air(ncf_net, frozen_to_air_args)
+
+def frozen_to_air(net, args_net):
+    """frozen net parameters in the format of air"""
+    param_dict = load_checkpoint(args_net.get("ckpt_file"))
+    load_param_into_net(net, param_dict)
+    network = PredictWithSigmoid(net, args_net.get("topk"), args_net.get("num_eval_neg"))
+    users = Tensor(np.zeros([1, cfg.eval_batch_size]).astype(np.int32))
+    items = Tensor(np.zeros([1, cfg.eval_batch_size]).astype(np.int32))
+    masks = Tensor(np.zeros([1, cfg.eval_batch_size]).astype(np.float32))
+    input_data = [users, items, masks]
+    export(network, *input_data, file_name=args_net.get("file_name"), file_format=args_net.get("file_format"))
+
+context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+if args.device_target == "Ascend":
+    context.set_context(device_id=args.device_id)
+
+if __name__ == '__main__':
+    export_for_infer()

official/recommend/ncf/modelarts/ncf.py

-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2022 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.

official/recommend/ncf/modelarts/run_distribute_train.sh

+#!/bin/bash
+# Copyright 2022 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.
+# ============================================================================
+echo "Please run the script as: "
+echo "sh scripts/run_distribute_train.sh DEVICE_NUM DATASET_PATH RANK_TABLE_FILE"
+echo "for example: sh scripts/run_distribute_train.sh 8 /dataset_path /rank_table_8p.json"
+
+current_exec_path=$(pwd)
+echo ${current_exec_path}
+
+export RANK_SIZE=$1
+data_path=$2
+export RANK_TABLE_FILE=$3
+
+for((i=0;i<=RANK_SIZE;i++));
+do
+    rm ${current_exec_path}/device_$i/ -rf
+    mkdir ${current_exec_path}/device_$i
+    cd ${current_exec_path}/device_$i || exit
+    export RANK_ID=$i
+    export DEVICE_ID=$i
+    python3 -u ${current_exec_path}/train_for_infer.py  \
+     --data_path $data_path \
+     --dataset 'ml-1m'  \
+     --train_epochs 50 \
+     --output_path './output/' \
+     --loss_file_name 'loss.log'  \
+     --checkpoint_path './checkpoint/' \
+     --device_target="Ascend" \
+     --device_id=$i \
+     --is_distributed=1 \
+     >log_$i.log 2>&1 &
+done
+

official/recommend/ncf/modelarts/run_train.sh

+#!/bin/bash
+# Copyright 2022 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.
+# ============================================================================
+echo "Please run the script as: "
+echo "sh scripts/run_train.sh DATASET_PATH CKPT_FILE"
+echo "for example: sh scripts/run_train.sh /dataset_path /ncf.ckpt"
+
+data_path=$1
+ckpt_file=$2
+python3 ./train_for_infer.py --data_path $data_path --dataset 'ml-1m'  --train_epochs 20 --batch_size 256  --checkpoint_path $ckpt_file

official/recommend/ncf/modelarts/start.py

-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2022 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.

official/recommend/ncf/modelarts/train_for_infer.py

+# Copyright 2022 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.
+# ============================================================================
+"""Training entry file"""
+import os
+
+import argparse
+import ast
+
+from absl import logging
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore import context, Model
+from mindspore.context import ParallelMode
+from mindspore.communication.management import get_rank, get_group_size, init
+from mindspore.common import set_seed
+from dataset import create_dataset
+from ncf import NCFModel, NetWithLossClass, TrainStepWrap
+from src.config import cfg
+
+
+set_seed(1)
+
+logging.set_verbosity(logging.INFO)
+
+
+parser = argparse.ArgumentParser(description='NCF')
+parser.add_argument("--data_path", type=str, default="./dataset/")  # The location of the input data.
+parser.add_argument("--dataset", type=str, default="ml-1m", choices=["ml-1m", "ml-20m"])  # Dataset to be trained and evaluated. ["ml-1m", "ml-20m"]
+parser.add_argument("--train_epochs", type=int, default=14)  # The number of epochs used to train.
+parser.add_argument("--batch_size", type=int, default=256)  # Batch size for training and evaluation
+parser.add_argument("--num_neg", type=int, default=4)  # The Number of negative instances to pair with a positive instance.
+parser.add_argument("--checkpoint_path", type=str, default="./checkpoint/")
+parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
+                    help='device where the code will be implemented. (Default: Ascend)')
+parser.add_argument('--device_id', type=int, default=0, help='device id of GPU or Ascend. (Default: None)')
+parser.add_argument('--is_distributed', type=int, default=0, help='if multi device')
+parser.add_argument('--rank', type=int, default=0, help='local rank of distributed')
+parser.add_argument('--group_size', type=int, default=1, help='world size of distributed')
+parser.add_argument("--is_row_vector_input", type=ast.literal_eval, default=True,
+                    help="Change model input into row vector for MindX SDK inference")
+args = parser.parse_args()
+
+def train():
+    """train method"""
+    if args.is_distributed:
+        if args.device_target == "Ascend":
+            init()
+            context.set_context(device_id='0')
+        elif args.device_target == "GPU":
+            init()
+
+        args.rank = get_rank()
+        args.group_size = get_group_size()
+        device_num = args.group_size
+        context.reset_auto_parallel_context()
+        context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
+                                          parameter_broadcast=True, gradients_mean=True)
+    else:
+        context.set_context(device_id=args.device_id)
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+
+    layers = cfg.layers
+    num_factors = cfg.num_factors
+    epochs = args.train_epochs
+
+
+    if not os.path.exists(args.checkpoint_path):
+        os.makedirs(args.checkpoint_path)
+
+    CKPT_SAVE_DIR = args.checkpoint_path
+
+
+
+    ds_train, num_train_users, num_train_items = create_dataset(test_train=True, data_dir=args.data_path,
+                                                                dataset=args.dataset, train_epochs=1,
+                                                                batch_size=args.batch_size, num_neg=args.num_neg,
+                                                                row_vector=args.is_row_vector_input)
+    print("ds_train.size: {}".format(ds_train.get_dataset_size()))
+
+
+    ncf_net = NCFModel(num_users=num_train_users,
+                       num_items=num_train_items,
+                       num_factors=num_factors,
+                       model_layers=layers,
+                       mf_regularization=0,
+                       mlp_reg_layers=[0.0, 0.0, 0.0, 0.0],
+                       mf_dim=16,
+                       is_row_vector_input=args.is_row_vector_input)
+
+    loss_net = NetWithLossClass(ncf_net)
+    train_net = TrainStepWrap(loss_net, ds_train.get_dataset_size() * (epochs + 1))
+
+    train_net.set_train()
+
+    model = Model(train_net)
+    callback = LossMonitor(per_print_times=ds_train.get_dataset_size())
+    ckpt_config = CheckpointConfig(save_checkpoint_steps=(4970845+args.batch_size-1)//(args.batch_size),
+                                   keep_checkpoint_max=100)
+    ckpoint_cb = ModelCheckpoint(prefix='ncf', directory=CKPT_SAVE_DIR, config=ckpt_config)
+    model.train(epochs,
+                ds_train,
+                callbacks=[TimeMonitor(ds_train.get_dataset_size()), callback, ckpoint_cb],
+                dataset_sink_mode=True)
+
+if __name__ == '__main__':
+    train()
+    
\ No newline at end of file