!137 add gpu support for fat_deepffm

Merge pull request !137 from Shawny/deepffm

research/recommend/Fat-DeepFFM/README.md

@@ -97,6 +97,31 @@ Fat - DeepFFM consists of three parts. The FFM component is a factorization mach
 
   [hccl tools](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools).
 
+- running on GPU
+
+  ```shell
+  # run training example
+  python train.py \
+    --dataset_path='data/mindrecord' \
+    --ckpt_path='./checkpoint/Fat-DeepFFM' \
+    --eval_file_name='./auc.log' \
+    --loss_file_name='./loss.log' \
+    --device_target='GPU' \
+    --do_eval=True > output.log 2>&1 &
+
+  # run distributed training example
+   bash scripts/run_distribute_train_gpu.sh  8 /dataset_path
+
+  # run evaluation example
+  python eval.py \
+    --dataset_path='dataset/mindrecord' \
+    --ckpt_path='./checkpoint/Fat-DeepFFM.ckpt'\
+    --device_target = 'GPU'\
+    --device_id=0  > eval_output.log 2>&1 &
+  OR
+  bash scripts/run_eval.sh  0 GPU  /dataset_path  /ckpt_path
+  ```
+
 # [Script Description](#contents)
 
 ## [Script and Sample Code](#contents)
@@ -281,17 +306,17 @@ Inference result is saved in current path, you can find result like this in acc.
 
 ### Inference Performance
 
-| Parameters          | Ascend                      |
-| ------------------- | --------------------------- |
-| Model Version       | DeepFM                      |
-| Resource            | Ascend 910; OS Euler2.8     |
-| Uploaded Date       | 06/20/2021 (month/day/year) |
-| MindSpore Version   | 1.2.0                       |
-| Dataset             | Criteo                      |
-| batch_size          | 1000                        |
-| outputs             | AUC                         |
-| AUC                 | 1pc: 80.90%;                |
-| Model for inference | 87.65M (.ckpt file)         |
+| Parameters          | Ascend                      | GPU
+| ------------------- | --------------------------- | ---------------------------
+| Model Version       | DeepFM                      | DeepFM
+| Resource            | Ascend 910; OS Euler2.8     | NV SMX2 V100-32G; OS Euler3.10
+| Uploaded Date       | 06/20/2021 (month/day/year) | 09/04/2021 (month/day/year)
+| MindSpore Version   | 1.2.0                       | 1.3.0
+| Dataset             | Criteo                      | Criteo
+| batch_size          | 1000                        | 1000
+| outputs             | AUC                         | AUC
+| AUC                 | 1pc: 80.90%;                | 8pc: 80.90%;
+| Model for inference | 87.65M (.ckpt file)         | 89.35M (.ckpt file)
 
 # [Description of Random Situation](#contents)
 

research/recommend/Fat-DeepFFM/scripts/run_distribute_train_gpu.sh

+#!/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.
+# ============================================================================
+echo "Please run the script as: "
+echo "bash scripts/run_distribute_train_gpu.sh DEVICE_NUM DATASET_PATH"
+echo "for example: sh scripts/run_distribute_train_gpu.sh 8 /dataset_path"
+echo "After running the script, the network runs in the background, The log will be generated in log/output.log"
+
+if [ $# != 2 ]; then
+  echo "Usage: bash scripts/run_distribute_train_gpu.sh [DEVICE_NUM] [DATASET_PATH]"
+  exit 1
+fi
+
+get_real_path() {
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+dataset_path=$(get_real_path $2)
+echo $dataset_path
+
+if [ ! -d $dataset_path ]
+then
+    echo "error: dataset_path=$dataset_path is not a directory."
+exit 1
+fi
+
+export RANK_SIZE=$1
+export 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 $RANK_SIZE --output-filename log_output --merge-stderr-to-stdout \
+  python -u train.py \
+    --dataset_path=$DATA_URL \
+    --ckpt_path="Fat-DeepFFM" \
+    --eval_file_name='auc.log' \
+    --loss_file_name='loss.log' \
+    --device_target='GPU' \
+    --do_eval=True > output.log 2>&1 &
\ No newline at end of file

research/recommend/Fat-DeepFFM/train.py

@@ -47,22 +47,20 @@ set_seed(1)
 
 if __name__ == '__main__':
     model_config = ModelConfig()
-    if rank_size > 1:
-        device_id = int(os.getenv('DEVICE_ID'))
-        context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target,
-                            device_id=device_id)
-        context.reset_auto_parallel_context()
-        context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL,
-                                          gradients_mean=True,
-                                          all_reduce_fusion_config=[9, 11])
+    device_id = 0
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=device_id)
+    if args.device_target == "Ascend":
+        device_id = int(os.getenv('DEVICE_ID', '0'))
+    if rank_size == 1 or args.device_target == "CPU":
+        rank_id = 0
+    elif rank_size > 1:
         init()
         rank_id = get_rank()
-    else:
-        device_id = int(os.getenv('DEVICE_ID'))
-        context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target,
-                            device_id=device_id)
-        rank_size = None
-        rank_id = None
+        context.reset_auto_parallel_context()
+        context.set_auto_parallel_context(device_num=rank_size,
+                                          parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True)
+        if args.device_target == "Ascend":
+            context.set_auto_parallel_context(all_reduce_fusion_config=[9, 11])
     print("load dataset...")
     ds_train = get_mindrecord_dataset(args.dataset_path, train_mode=True, epochs=1, batch_size=model_config.batch_size,
                                       rank_size=rank_size, rank_id=rank_id, line_per_sample=1000)
@@ -72,12 +70,12 @@ if __name__ == '__main__':
     time_callback = TimeMonitor(data_size=ds_train.get_dataset_size())
     loss_callback = LossCallback(args.loss_file_name)
     cb = [loss_callback, time_callback]
-    if rank_size == 1 or device_id == 0:
+    if rank_id == 0:
         config_ck = CheckpointConfig(save_checkpoint_steps=ds_train.get_dataset_size() * model_config.epoch_size,
                                      keep_checkpoint_max=model_config.keep_checkpoint_max)
         ckpoint_cb = ModelCheckpoint(prefix=args.ckpt_path, config=config_ck)
         cb += [ckpoint_cb]
-    if args.do_eval and device_id == 0:
+    if args.do_eval and rank_id == 0:
         ds_test = get_mindrecord_dataset(args.dataset_path, train_mode=False)
         eval_callback = AUCCallBack(model, ds_test, eval_file_path=args.eval_file_name)
         cb.append(eval_callback)