fix_modelzoo_widedeep_run_multinup_train

model_zoo/wide_and_deep/README.md

@@ -13,26 +13,28 @@ The Criteo datasets are used for model training and evaluation.
 The entire code structure is as following:
 ```
 |--- wide_and_deep/
-    train_and_test.py            "Entrance of Wide&Deep model training and evaluation"
-    test.py                      "Entrance of Wide&Deep model evaluation"
-    train.py                     "Entrance of Wide&Deep model training"
-    train_and_test_multinpu.py   "Entrance of Wide&Deep model data parallel training and evaluation"
-    |--- src/                    "entrance of training and evaluation"
-        config.py                "parameters configuration"
-        dataset.py               "Dataset loader class"
-        process_data.py          "process dataset"
-        preprocess_data.py       "pre_process dataset"
-        WideDeep.py              "Model structure"
-        callbacks.py             "Callback class for training and evaluation"
-        metrics.py               "Metric class"
-    |--- script/                 "run shell dir"
-        run_multinpu_train.sh    "run data parallel"
+    train_and_eval.py                "Entrance of Wide&Deep model training and evaluation"
+    eval.py                          "Entrance of Wide&Deep model evaluation"
+    train.py                         "Entrance of Wide&Deep model training"
+    train_and_eval_multinpu.py       "Entrance of Wide&Deep model data parallel training and evaluation"
+    train_and_eval_auto_parallel.py
+    |--- src/                        "Entrance of training and evaluation"
+        config.py                    "Parameters configuration"
+        dataset.py                   "Dataset loader class"
+        process_data.py              "Process dataset"
+        preprocess_data.py           "Pre_process dataset"
+        wide_and_deep.py             "Model structure"
+        callbacks.py                 "Callback class for training and evaluation"
+        metrics.py                   "Metric class"
+    |--- script/                     "Run shell dir"
+        run_multinpu_train.sh        "Run data parallel"
+        run_auto_parallel_train.sh   "Run auto parallel"
 ```
 
 ### Train and evaluate model
 To train and evaluate the model, command as follows:
 ```
-python train_and_test.py
+python train_and_eval.py
 ```
 Arguments:
   * `--data_path`: This should be set to the same directory given to the data_download's data_dir argument.
@@ -44,6 +46,7 @@ Arguments:
   * `--emb_dim`： The dense embedding dimension of sparse feature.
   * `--deep_layers_dim`： The dimension of all deep layers.
   * `--deep_layers_act`： The activation of all deep layers.
+  * `--dropout_flag`： Whether do dropout.
   * `--keep_prob`： The rate to keep in dropout layer.
   * `--ckpt_path`：The location of the checkpoint file.
   * `--eval_file_name` : Eval output file.
@@ -63,6 +66,7 @@ Arguments:
   * `--emb_dim`： The dense embedding dimension of sparse feature.
   * `--deep_layers_dim`： The dimension of all deep layers.
   * `--deep_layers_act`： The activation of all deep layers.
+  * `--dropout_flag`： Whether do dropout.
   * `--keep_prob`： The rate to keep in dropout layer.
   * `--ckpt_path`：The location of the checkpoint file.
   * `--eval_file_name` : Eval output file.
@@ -70,13 +74,17 @@ Arguments:
 
 To train the model in distributed, command as follows:
 ```
-# configure environment path, RANK_TABLE_FILE, RANK_SIZE, MINDSPORE_HCCL_CONFIG_PATH before training
-bash run_multinpu_train.sh
+# configure environment path before training
+bash run_multinpu_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE 
+```
+```
+# configure environment path before training
+bash run_auto_parallel_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE 
 ```
 
 To evaluate the model, command as follows:
 ```
-python test.py
+python eval.py
 ```
 Arguments:
   * `--data_path`: This should be set to the same directory given to the data_download's data_dir argument.

model_zoo/wide_and_deep/script/run_auto_parallel_train.sh

@@ -0,0 +1,35 @@
+#!/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.
+# ============================================================================
+
+# bash run_multinpu_train.sh
+execute_path=$(pwd)
+script_self=$(readlink -f "$0")
+self_path=$(dirname "${script_self}")
+export RANK_SIZE=$1
+export EPOCH_SIZE=$2
+export DATASET=$3
+export RANK_TABLE_FILE=$4
+export MINDSPORE_HCCL_CONFIG_PATH=$4
+
+for((i=0;i<$RANK_SIZE;i++));
+do
+  rm -rf ${execute_path}/device_$i/
+  mkdir ${execute_path}/device_$i/
+  cd ${execute_path}/device_$i/ || exit
+  export RANK_ID=$i
+  export DEVICE_ID=$i
+  python -s ${self_path}/../train_and_eval_auto_parallel.py --data_path=$DATASET --epochs=$EPOCH_SIZE >train_deep$i.log 2>&1 &
+done

model_zoo/wide_and_deep/script/run_multinpu_train.sh

@@ -24,12 +24,12 @@ export DATASET=$3
 export RANK_TABLE_FILE=$4
 export MINDSPORE_HCCL_CONFIG_PATH=$4
 
-for((i=0;i<=$RANK_SIZE;i++));
+for((i=0;i<$RANK_SIZE;i++));
 do
   rm -rf ${execute_path}/device_$i/
   mkdir ${execute_path}/device_$i/
   cd ${execute_path}/device_$i/ || exit
   export RANK_ID=$i
   export DEVICE_ID=$i
-  python -s ${self_path}/../train_and_test_multinpu.py --data_path=$DATASET --epochs=$EPOCH_SIZE >train_deep$i.log 2>&1 &
+  python -s ${self_path}/../train_and_eval_multinpu.py --data_path=$DATASET --epochs=$EPOCH_SIZE >train_deep$i.log 2>&1 &
 done

model_zoo/wide_and_deep/src/config.py

@@ -31,7 +31,7 @@ def argparse_init():
     parser.add_argument("--deep_layer_dim", type=int, nargs='+', default=[1024, 512, 256, 128])
     parser.add_argument("--deep_layer_act", type=str, default='relu')
     parser.add_argument("--keep_prob", type=float, default=1.0)
-
+    parser.add_argument("--dropout_flag", type=int, default=0)
     parser.add_argument("--output_path", type=str, default="./output/")
     parser.add_argument("--ckpt_path", type=str, default="./checkpoints/")
     parser.add_argument("--eval_file_name", type=str, default="eval.log")
@@ -86,7 +86,7 @@ class WideDeepConfig():
         self.weight_bias_init = ['normal', 'normal']
         self.emb_init = 'normal'
         self.init_args = [-0.01, 0.01]
-        self.dropout_flag = False
+        self.dropout_flag = bool(args.dropout_flag)
         self.l2_coef = 8e-5
 
         self.output_path = args.output_path

model_zoo/wide_and_deep/src/wide_and_deep.py

@@ -19,7 +19,7 @@ import mindspore.common.dtype as mstype
 from mindspore.ops import functional as F
 from mindspore.ops import composite as C
 from mindspore.ops import operations as P
-# from mindspore.nn import Dropout
+from mindspore.nn import Dropout
 from mindspore.nn.optim import Adam, FTRL
 # from mindspore.nn.metrics import Metric
 from mindspore.common.initializer import Uniform, initializer
@@ -82,7 +82,7 @@ class DenseLayer(nn.Cell):
     """
 
     def __init__(self, input_dim, output_dim, weight_bias_init, act_str,
-                 keep_prob=0.7, scale_coef=1.0, convert_dtype=True):
+                 keep_prob=0.7, scale_coef=1.0, convert_dtype=True, drop_out=False):
         super(DenseLayer, self).__init__()
         weight_init, bias_init = weight_bias_init
         self.weight = init_method(
@@ -92,11 +92,12 @@ class DenseLayer(nn.Cell):
         self.matmul = P.MatMul(transpose_b=False)
         self.bias_add = P.BiasAdd()
         self.cast = P.Cast()
-        #self.dropout = Dropout(keep_prob=keep_prob)
+        self.dropout = Dropout(keep_prob=keep_prob)
         self.mul = P.Mul()
         self.realDiv = P.RealDiv()
         self.scale_coef = scale_coef
         self.convert_dtype = convert_dtype
+        self.drop_out = drop_out
 
     def _init_activation(self, act_str):
         act_str = act_str.lower()
@@ -110,8 +111,8 @@ class DenseLayer(nn.Cell):
 
     def construct(self, x):
         x = self.act_func(x)
-        # if self.training:
-        #    x = self.dropout(x)
+        if self.training and self.drop_out:
+            x = self.dropout(x)
         x = self.mul(x, self.scale_coef)
         if self.convert_dtype:
             x = self.cast(x, mstype.float16)
@@ -163,23 +164,28 @@ class WideDeepModel(nn.Cell):
         self.dense_layer_1 = DenseLayer(self.all_dim_list[0],
                                         self.all_dim_list[1],
                                         self.weight_bias_init,
-                                        self.deep_layer_act, convert_dtype=True)
+                                        self.deep_layer_act,
+                                        convert_dtype=True, drop_out=config.dropout_flag)
         self.dense_layer_2 = DenseLayer(self.all_dim_list[1],
                                         self.all_dim_list[2],
                                         self.weight_bias_init,
-                                        self.deep_layer_act, convert_dtype=True)
+                                        self.deep_layer_act,
+                                        convert_dtype=True, drop_out=config.dropout_flag)
         self.dense_layer_3 = DenseLayer(self.all_dim_list[2],
                                         self.all_dim_list[3],
                                         self.weight_bias_init,
-                                        self.deep_layer_act, convert_dtype=True)
+                                        self.deep_layer_act,
+                                        convert_dtype=True, drop_out=config.dropout_flag)
         self.dense_layer_4 = DenseLayer(self.all_dim_list[3],
                                         self.all_dim_list[4],
                                         self.weight_bias_init,
-                                        self.deep_layer_act, convert_dtype=True)
+                                        self.deep_layer_act,
+                                        convert_dtype=True, drop_out=config.dropout_flag)
         self.dense_layer_5 = DenseLayer(self.all_dim_list[4],
                                         self.all_dim_list[5],
                                         self.weight_bias_init,
-                                        self.deep_layer_act, convert_dtype=True)
+                                        self.deep_layer_act,
+                                        convert_dtype=True, drop_out=config.dropout_flag)
 
         self.gather_v2 = P.GatherV2()
         self.mul = P.Mul()

model_zoo/wide_and_deep/train_and_eval_auto_parallel.py

@@ -71,11 +71,10 @@ class ModelBuilder():
         return get_WideDeep_net(config)
 
 
-def test_train_eval():
+def train_and_eval(config):
     """
     test_train_eval
     """
-    config = WideDeepConfig()
     data_path = config.data_path
     batch_size = config.batch_size
     epochs = config.epochs
@@ -109,9 +108,12 @@ def test_train_eval():
     ckptconfig = CheckpointConfig(save_checkpoint_steps=ds_train.get_dataset_size(), keep_checkpoint_max=5)
     ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
                                  directory=config.ckpt_path, config=ckptconfig)
+    context.set_auto_parallel_context(strategy_ckpt_save_file="./strategy_train.ckpt")
     model.train(epochs, ds_train,
                 callbacks=[TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback, ckpoint_cb])
 
 
 if __name__ == "__main__":
-    test_train_eval()
+    wide_deep_config = WideDeepConfig()
+    wide_deep_config.argparse_init()
+    train_and_eval(wide_deep_config)

model_zoo/wide_and_deep/train_and_eval_multinpu.py

@@ -66,7 +66,7 @@ class ModelBuilder():
         return get_WideDeep_net(config)
 
 
-def test_train_eval(config):
+def train_and_eval(config):
     """
     test_train_eval
     """
@@ -105,4 +105,4 @@ def test_train_eval(config):
 if __name__ == "__main__":
     wide_deep_config = WideDeepConfig()
     wide_deep_config.argparse_init()
-    test_train_eval(wide_deep_config)
+    train_and_eval(wide_deep_config)