add gpu support for deepfm model

fixed pylint errors

model_zoo/official/recommend/deepfm/eval.py

@@ -24,16 +24,16 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
 
 from src.deepfm import ModelBuilder, AUCMetric
 from src.config import DataConfig, ModelConfig, TrainConfig
-from src.dataset import create_dataset
+from src.dataset import create_dataset, DataType
 
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 parser = argparse.ArgumentParser(description='CTR Prediction')
 parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
 parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
-
+parser.add_argument('--device_target', type=str, default="Ascend", help='Ascend, GPU, or CPU')
 args_opt, _ = parser.parse_known_args()
 device_id = int(os.getenv('DEVICE_ID'))
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id)
+context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, device_id=device_id)
 
 
 def add_write(file_path, print_str):
@@ -47,7 +47,8 @@ if __name__ == '__main__':
     train_config = TrainConfig()
 
     ds_eval = create_dataset(args_opt.dataset_path, train_mode=False,
-                             epochs=1, batch_size=train_config.batch_size)
+                             epochs=1, batch_size=train_config.batch_size,
+                             data_type=DataType(data_config.data_format))
     model_builder = ModelBuilder(ModelConfig, TrainConfig)
     train_net, eval_net = model_builder.get_train_eval_net()
     train_net.set_train()

model_zoo/official/recommend/deepfm/scripts/run_distribute_train_gpu.sh

+#!/bin/bash
+# Copyright 2020 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"
+echo "for example: sh scripts/run_distribute_train.sh 8 /dataset_path"
+echo "After running the script, the network runs in the background, The log will be generated in log/output.log"
+
+
+export RANK_SIZE=$1
+DATA_URL=$2
+
+rm -rf log
+mkdir ./log
+cp *.py ./log
+cp -r src ./log
+cd ./log || exit
+env > env.log
+mpirun --allow-run-as-root -n $RANK_SIZE \
+  python -u train.py \
+    --dataset_path=$DATA_URL \
+    --ckpt_path="checkpoint" \
+    --eval_file_name='auc.log' \
+    --loss_file_name='loss.log' \
+    --device_target='GPU' \
+    --do_eval=True > output.log 2>&1 &

model_zoo/official/recommend/deepfm/scripts/run_eval.sh

@@ -14,13 +14,14 @@
 # limitations under the License.
 # ============================================================================
 echo "Please run the script as: "
-echo "sh scripts/run_eval.sh DEVICE_ID DATASET_PATH CHECKPOINT_PATH"
-echo "for example: sh scripts/run_eval.sh 0 /dataset_path /checkpoint_path"
+echo "sh scripts/run_eval.sh DEVICE_ID DEVICE_TARGET DATASET_PATH CHECKPOINT_PATH"
+echo "for example: sh scripts/run_eval.sh 0 GPU /dataset_path /checkpoint_path"
 echo "After running the script, the network runs in the background, The log will be generated in ms_log/eval_output.log"
 
 export DEVICE_ID=$1
-DATA_URL=$2
-CHECKPOINT_PATH=$3
+DEVICE_TARGET=$2
+DATA_URL=$3
+CHECKPOINT_PATH=$4
 
 mkdir -p ms_log
 CUR_DIR=`pwd`
@@ -29,4 +30,5 @@ export GLOG_logtostderr=0
 
 python -u eval.py \
     --dataset_path=$DATA_URL \
-    --checkpoint_path=$CHECKPOINT_PATH > ms_log/eval_output.log 2>&1 &
+    --checkpoint_path=$CHECKPOINT_PATH \
\ No newline at end of file
+    --device_target=$DEVICE_TARGET > ms_log/eval_output.log 2>&1 &

model_zoo/official/recommend/deepfm/scripts/run_standalone_train.sh

@@ -14,12 +14,13 @@
 # limitations under the License.
 # ============================================================================
 echo "Please run the script as: "
-echo "sh scripts/run_standalone_train.sh DEVICE_ID DATASET_PATH"
-echo "for example: sh scripts/run_standalone_train.sh 0 /dataset_path"
+echo "sh scripts/run_standalone_train.sh DEVICE_ID DEVICE_TARGET DATASET_PATH"
+echo "for example: sh scripts/run_standalone_train.sh 0 GPU /dataset_path"
 echo "After running the script, the network runs in the background, The log will be generated in ms_log/output.log"
 
 export DEVICE_ID=$1
-DATA_URL=$2
+DEVICE_TARGET=$2
+DATA_URL=$3
 
 mkdir -p ms_log
 CUR_DIR=`pwd`
@@ -31,4 +32,5 @@ python -u train.py \
     --ckpt_path="checkpoint" \
     --eval_file_name='auc.log' \
     --loss_file_name='loss.log' \
+    --device_target=$DEVICE_TARGET \
     --do_eval=True > ms_log/output.log 2>&1 &

model_zoo/official/recommend/deepfm/train.py

@@ -16,11 +16,14 @@
 import os
 import sys
 import argparse
+import random
+import numpy as np
 
 from mindspore import context, ParallelMode
-from mindspore.communication.management import init
+from mindspore.communication.management import init, get_rank, get_group_size
 from mindspore.train.model import Model
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
+import mindspore.dataset.engine as de
 
 from src.deepfm import ModelBuilder, AUCMetric
 from src.config import DataConfig, ModelConfig, TrainConfig
@@ -34,24 +37,41 @@ parser.add_argument('--ckpt_path', type=str, default=None, help='Checkpoint path
 parser.add_argument('--eval_file_name', type=str, default="./auc.log", help='eval file path')
 parser.add_argument('--loss_file_name', type=str, default="./loss.log", help='loss file path')
 parser.add_argument('--do_eval', type=bool, default=True, help='Do evaluation or not.')
-
+parser.add_argument('--device_target', type=str, default="Ascend", help='Ascend, GPU, or CPU')
 args_opt, _ = parser.parse_known_args()
-device_id = int(os.getenv('DEVICE_ID'))
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id)
+rank_size = int(os.environ.get("RANK_SIZE", 1))
 
+random.seed(1)
+np.random.seed(1)
+de.config.set_seed(1)
 
 if __name__ == '__main__':
     data_config = DataConfig()
     model_config = ModelConfig()
     train_config = TrainConfig()
 
-    rank_size = int(os.environ.get("RANK_SIZE", 1))
     if rank_size > 1:
-        context.reset_auto_parallel_context()
-        context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True)
-        init()
-        rank_id = int(os.environ.get('RANK_ID'))
+        if args_opt.device_target == "Ascend":
+            device_id = int(os.getenv('DEVICE_ID'))
+            context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, device_id=device_id)
+            context.reset_auto_parallel_context()
+            context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True)
+            init()
+            rank_id = int(os.environ.get('RANK_ID'))
+        elif args_opt.device_target == "GPU":
+            init("nccl")
+            context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
+            context.reset_auto_parallel_context()
+            context.set_auto_parallel_context(device_num=get_group_size(),
+                                              parallel_mode=ParallelMode.DATA_PARALLEL,
+                                              mirror_mean=True)
+            rank_id = get_rank()
+        else:
+            print("Unsupported device_target ", args_opt.device_target)
+            exit()
     else:
+        device_id = int(os.getenv('DEVICE_ID'))
+        context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, device_id=device_id)
         rank_size = None
         rank_id = None
 
@@ -73,6 +93,8 @@ if __name__ == '__main__':
     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())
         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,