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add bgcf gpu

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pull/11728/head
牛黄解毒片 5 years ago committed by panfengfeng
parent 54b8d53780
commit 1bff5f043d
7 changed files with 342 additions and 124 deletions
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235
model_zoo/official/gnn/bgcf/README.md
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109
model_zoo/official/gnn/bgcf/README_CN.md
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@ -40,39 +40,46 @@ BGCF包含两个主要模块。首先是抽样，它生成基于节点复制的
所用数据集的统计信息摘要如下： 所用数据集的统计信息摘要如下：
| | Amazon-Beauty | | | Amazon-Beauty |
| ------------------ | -----------------------:| | ------------------ | ------------------ |
| 任务 | 推荐 | | 任务 | 推荐 |
| # 用户 | 7068 (1图) | | # 用户 | 7068 (1图) |
| # 物品 | 3570 | | # 物品 | 3570 |
| # 交互 | 79506 | | # 交互 | 79506 |
| # 训练数据 | 60818 | | # 训练数据 | 60818 |
| # 测试数据 | 18688 | | # 测试数据 | 18688 |
| # 密度 | 0.315% | | # 密度 | 0.315% |
- 数据准备 - 数据准备
- 将数据集放到任意路径，文件夹应该包含如下文件（以Amazon-Beauty数据集为例）： - 将数据集放到任意路径，文件夹应该包含如下文件（以Amazon-Beauty数据集为例）：
```text ```text
. .
└─data └─data
├─ratings_Beauty.csv ├─ratings_Beauty.csv
``` ```
- 为Amazon-Beauty生成MindRecord格式的数据集 - 为Amazon-Beauty生成MindRecord格式的数据集
```builddoutcfg ```builddoutcfg
cd ./scripts cd ./scripts
# SRC_PATH是您下载的数据集文件路径 # SRC_PATH是您下载的数据集文件路径
sh run_process_data_ascend.sh [SRC_PATH] sh run_process_data_ascend.sh [SRC_PATH]
``` ```
- 启动 - 启动
```text ```text
# 为Amazon-Beauty生成MindRecord格式的数据集 # 为Amazon-Beauty生成MindRecord格式的数据集
sh ./run_process_data_ascend.sh ./data sh ./run_process_data_ascend.sh ./data
```
# 特性 # 特性
## 混合精度 ## 混合精度
@ -81,7 +88,7 @@ BGCF包含两个主要模块。首先是抽样，它生成基于节点复制的
# 环境要求 # 环境要求
- 硬件（Ascend） - 硬件（Ascend/GPU）
- 框架 - 框架
- [MindSpore](https://www.mindspore.cn/install) - [MindSpore](https://www.mindspore.cn/install)
- 如需查看详情，请参见如下资源： - 如需查看详情，请参见如下资源：
@ -104,6 +111,18 @@ BGCF包含两个主要模块。首先是抽样，它生成基于节点复制的
``` ```
- GPU处理器环境运行
```text
# 使用Amazon-Beauty数据集运行训练示例
sh run_train_gpu.sh 0 dataset_path
# 使用Amazon-Beauty数据集运行评估示例
sh run_eval_gpu.sh 0 dataset_path
```
# 脚本说明 # 脚本说明
## 脚本及样例代码 ## 脚本及样例代码
@ -113,9 +132,11 @@ BGCF包含两个主要模块。首先是抽样，它生成基于节点复制的
└─bgcf └─bgcf
├─README.md ├─README.md
├─scripts ├─scripts
| ├─run_eval_ascend.sh # 启动评估 | ├─run_eval_ascend.sh # Ascend启动评估
| ├─run_eval_gpu.sh # GPU启动评估
| ├─run_process_data_ascend.sh # 生成MindRecord格式的数据集 | ├─run_process_data_ascend.sh # 生成MindRecord格式的数据集
| └─run_train_ascend.sh # 启动训练 | └─run_train_ascend.sh # Ascend启动训练
| └─run_train_gpu.sh # GPU启动训练
| |
├─src ├─src
| ├─bgcf.py # BGCF模型 | ├─bgcf.py # BGCF模型
@ -178,7 +199,25 @@ BGCF包含两个主要模块。首先是抽样，它生成基于节点复制的
Epoch 598 iter 12 loss 3640.7612 Epoch 598 iter 12 loss 3640.7612
Epoch 599 iter 12 loss 3654.9087 Epoch 599 iter 12 loss 3654.9087
Epoch 600 iter 12 loss 3632.4585 Epoch 600 iter 12 loss 3632.4585
...
```
- GPU处理器环境运行
```python
sh run_train_gpu.sh 0 dataset_path
```
训练结果将保存在脚本路径下，文件夹名称以“train”开头。您可在日志中找到结果，如下所示。
```python
Epoch 001 iter 12 loss 34696.242
Epoch 002 iter 12 loss 34275.508
Epoch 003 iter 12 loss 30620.635
Epoch 004 iter 12 loss 21628.908
``` ```
@ -212,7 +251,23 @@ BGCF包含两个主要模块。首先是抽样，它生成基于节点复制的
sedp_@10:0.01896, sedp_@20:0.01504, nov_@10:7.57995, nov_@20:7.79439 sedp_@10:0.01896, sedp_@20:0.01504, nov_@10:7.57995, nov_@20:7.79439
epoch:600, recall_@10:0.09926, recall_@20:0.15080, ndcg_@10:0.07283, ndcg_@20:0.09016, epoch:600, recall_@10:0.09926, recall_@20:0.15080, ndcg_@10:0.07283, ndcg_@20:0.09016,
sedp_@10:0.01890, sedp_@20:0.01517, nov_@10:7.58277, nov_@20:7.80038 sedp_@10:0.01890, sedp_@20:0.01517, nov_@10:7.58277, nov_@20:7.80038
...
```
- GPU评估
```python
sh run_eval_gpu.sh 0 dataset_path
```
评估结果将保存在脚本路径下，文件夹名称以“eval”开头。您可在日志中找到结果，如下所示。
```python
epoch:680, recall_@10:0.10383, recall_@20:0.15524, ndcg_@10:0.07503, ndcg_@20:0.09249,
sedp_@10:0.01926, sedp_@20:0.01547, nov_@10:7.60851, nov_@20:7.81969
``` ```
@ -220,19 +275,19 @@ BGCF包含两个主要模块。首先是抽样，它生成基于节点复制的
## 性能 ## 性能
| 参数 | BGCF | | 参数 | BGCF Ascend | BGCF GPU |
| ------------------------------------ | ----------------------------------------- | | -------------------------- | ------------------------------------------ | ------------------------------------------ |
| 资源 | Ascend 910 | | 资源 | Ascend 910 | Tesla V100-PCIE |
| 上传日期 | 09/23/2020(月/日/年) | | 上传日期 | 09/23/2020(月/日/年) | 01/28/2021(月/日/年) |
| MindSpore版本 | 1.0.0 | | MindSpore版本 | 1.0.0 | Master(4b3e53b4) |
| 数据集 | Amazon-Beauty | | 数据集 | Amazon-Beauty | Amazon-Beauty |
| 训练参数 | epoch=600 | | 训练参数 | epoch=600,steps=12,batch_size=5000,lr=0.001| epoch=680,steps=12,batch_size=5000,lr=0.001|
| 优化器 | Adam | | 优化器 | Adam | Adam |
| 损失函数 | BPR loss | | 损失函数 | BPR loss | BPR loss |
| Recall@20 | 0.1534 | | Recall@20 | 0.1534 | 0.15524 |
| NDCG@20 | 0.0912 | | NDCG@20 | 0.0912 | 0.09249 |
| 训练成本 | 25min | | 训练成本 | 25min | 60min |
| 脚本 | [bgcf脚本](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/gnn/bgcf) | | 脚本 | [bgcf脚本](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/gnn/bgcf) | [bgcf脚本](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/gnn/bgcf) |
# 随机情况说明 # 随机情况说明

12
model_zoo/official/gnn/bgcf/eval.py
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@ -19,6 +19,7 @@ import datetime
import mindspore.context as context import mindspore.context as context
from mindspore.train.serialization import load_checkpoint from mindspore.train.serialization import load_checkpoint
from mindspore.common import set_seed
from src.bgcf import BGCF from src.bgcf import BGCF
from src.utils import BGCFLogger from src.utils import BGCFLogger
@ -27,6 +28,7 @@ from src.metrics import BGCFEvaluate
from src.callback import ForwardBGCF, TestBGCF from src.callback import ForwardBGCF, TestBGCF
from src.dataset import TestGraphDataset, load_graph from src.dataset import TestGraphDataset, load_graph
set_seed(1)
def evaluation(): def evaluation():
"""evaluation""" """evaluation"""
@ -34,7 +36,8 @@ def evaluation():
num_item = train_graph.graph_info()["node_num"][1] num_item = train_graph.graph_info()["node_num"][1]
eval_class = BGCFEvaluate(parser, train_graph, test_graph, parser.Ks) eval_class = BGCFEvaluate(parser, train_graph, test_graph, parser.Ks)
for _epoch in range(parser.eval_interval, parser.num_epoch+1, parser.eval_interval): for _epoch in range(parser.eval_interval, parser.num_epoch+1, parser.eval_interval) \
if parser.device_target == "Ascend" else range(parser.num_epoch, parser.num_epoch+1):
bgcfnet_test = BGCF([parser.input_dim, num_user, num_item], bgcfnet_test = BGCF([parser.input_dim, num_user, num_item],
parser.embedded_dimension, parser.embedded_dimension,
parser.activation, parser.activation,
@ -79,9 +82,10 @@ def evaluation():
if __name__ == "__main__": if __name__ == "__main__":
parser = parser_args() parser = parser_args()
context.set_context(mode=context.GRAPH_MODE, context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend", device_target=parser.device_target,
save_graphs=False, save_graphs=False)
device_id=int(parser.device)) if parser.device_target == "Ascend":
context.set_context(device_id=int(parser.device))
train_graph, test_graph, sampled_graph_list = load_graph(parser.datapath) train_graph, test_graph, sampled_graph_list = load_graph(parser.datapath)
test_graph_dataset = TestGraphDataset(train_graph, sampled_graph_list, num_samples=parser.raw_neighs, test_graph_dataset = TestGraphDataset(train_graph, sampled_graph_list, num_samples=parser.raw_neighs,

47
model_zoo/official/gnn/bgcf/scripts/run_eval_gpu.sh
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@ -0,0 +1,47 @@
#!/bin/bash
# Copyright 2021 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.
# ============================================================================
ulimit -u unlimited
if [ $# -lt 2 ]
then
echo "Usage: sh run_eval_gpu.sh [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]"
exit 1
fi
export DEVICE_NUM=1
DATASET_PATH=$2
if [ -d "eval" ];
then
rm -rf ./eval
fi
mkdir ./eval
cp ../*.py ./eval
cp *.sh ./eval
cp -r ../src ./eval
cd ./eval || exit
env > env.log
echo "start evaluation"
export CUDA_VISIBLE_DEVICES="$1"
python eval.py --datapath=$DATASET_PATH --ckptpath=../ckpts \
--device_target='GPU' --num_epoch=680 \
--dist_reg=0 > log 2>&1 &
cd ..

51
model_zoo/official/gnn/bgcf/scripts/run_train_gpu.sh
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@ -0,0 +1,51 @@
#!/bin/bash
# Copyright 2021 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.
# ============================================================================
if [ $# -lt 2 ]
then
echo "Usage: sh run_train_gpu.sh [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]"
exit 1
fi
export DEVICE_NUM=1
DATASET_PATH=$2
if [ -d "train" ];
then
rm -rf ./train
fi
mkdir ./train
if [ -d "ckpts" ];
then
rm -rf ./ckpts
fi
mkdir ./ckpts
cp ../*.py ./train
cp *.sh ./train
cp -r ../src ./train
cd ./train || exit
env > env.log
echo "start training"
export CUDA_VISIBLE_DEVICES="$1"
python train.py --datapath=$DATASET_PATH --ckptpath=../ckpts \
--device_target='GPU' --num_epoch=680 \
--dist_reg=0 > log 2>&1 &
cd ..

1
model_zoo/official/gnn/bgcf/src/config.py
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@ -49,4 +49,5 @@ def parser_args():
parser.add_argument("-emb", "--embedded_dimension", type=int, default=64, help="output embedding dim") parser.add_argument("-emb", "--embedded_dimension", type=int, default=64, help="output embedding dim")
parser.add_argument('--dist_reg', type=float, default=0.003, help="distance loss coefficient") parser.add_argument('--dist_reg', type=float, default=0.003, help="distance loss coefficient")
parser.add_argument('--device_target', type=str, default='Ascend', choices=('Ascend', 'GPU'), help='device target')
return parser.parse_args() return parser.parse_args()

11
model_zoo/official/gnn/bgcf/train.py
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@ -21,6 +21,7 @@ from mindspore import Tensor
import mindspore.context as context import mindspore.context as context
from mindspore.common import dtype as mstype from mindspore.common import dtype as mstype
from mindspore.train.serialization import save_checkpoint from mindspore.train.serialization import save_checkpoint
from mindspore.common import set_seed
from src.bgcf import BGCF from src.bgcf import BGCF
from src.config import parser_args from src.config import parser_args
@ -28,6 +29,7 @@ from src.utils import convert_item_id
from src.callback import TrainBGCF from src.callback import TrainBGCF
from src.dataset import load_graph, create_dataset from src.dataset import load_graph, create_dataset
set_seed(1)
def train(): def train():
"""Train""" """Train"""
@ -102,10 +104,13 @@ def train():
if __name__ == "__main__": if __name__ == "__main__":
parser = parser_args() parser = parser_args()
context.set_context(mode=context.GRAPH_MODE, context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend", device_target=parser.device_target,
save_graphs=False, save_graphs=False)
device_id=int(parser.device))
if parser.device_target == "Ascend":
context.set_context(device_id=int(parser.device))
train_graph, _, sampled_graph_list = load_graph(parser.datapath) train_graph, _, sampled_graph_list = load_graph(parser.datapath)
train_ds = create_dataset(train_graph, sampled_graph_list, parser.workers, batch_size=parser.batch_pairs, train_ds = create_dataset(train_graph, sampled_graph_list, parser.workers, batch_size=parser.batch_pairs,

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