!12780 add distribute file for naml

From: @zhao_ting_v
Reviewed-by: @wuxuejian,@c_34
Signed-off-by: @wuxuejian

model_zoo/official/recommend/naml/README.md

@@ -74,7 +74,11 @@ You can download the dataset and put the directory in structure as follows:
 You can start training using python or shell scripts. The usage of shell scripts as follows:
 
 ```shell
+# train standalone
 bash run_train.sh [PLATFORM] [DEVICE_ID] [DATASET] [DATASET_PATH]
+# train distribute
+bash run_distribute_train.sh [PLATFORM] [DEVICE_NUM] [DATASET] [DATASET_PATH] [RANK_TABLE_FILE]
+# evaluation
 bash run_eval.sh [PLATFORM] [DEVICE_ID] [DATASET] [DATASET_PATH] [CHECKPOINT_PATH]
 ```
 
@@ -83,6 +87,7 @@ bash run_eval.sh [PLATFORM] [DEVICE_ID] [DATASET] [DATASET_PATH] [CHECKPOINT_PAT
 - `DATASET` MIND dataset, support large, small and demo.
 - `DATASET_PATH` is the dataset path, the structure as [Dataset](#dataset).
 - `CHECKPOINT_PATH` is a pre-trained checkpoint path.
+- `RANK_TABLE_FILE` is HCCL configuration file when running on Ascend.
 
 ## [Model Export](#contents)
 

model_zoo/official/recommend/naml/script/run_distribute_train.sh

@@ -0,0 +1,52 @@
+#!/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 "=============================================================================================================="
+echo "Please run the script as: "
+echo "bash run_distribute_train.sh [PLATFORM] [DEVICE_NUM] [DATASET] [DATASET_PATH] [RANK_TABLE_FILE]"
+echo "for example: bash run_distribute_train.sh Ascend 8 large /path/MINDlarge /path/hccl_8p.json"
+echo "It is better to use absolute path."
+echo "=============================================================================================================="
+
+PLATFORM=$1
+export RANK_SIZE=$2
+DATASET=$3
+DATASET_PATH=$4
+export RANK_TABLE_FILE=$5
+PROJECT_DIR=$(cd "$(dirname "$0")" || exit; pwd)
+CHECKPOINT_PATH=${PROJECT_DIR}/checkpoint
+cd ${PROJECT_DIR}/.. || exit
+for((i=0;i<RANK_SIZE;i++))
+do
+    rm -rf LOG$i
+    mkdir ./LOG$i
+    cp ./*.py ./LOG$i
+    cp -r ./src ./LOG$i
+    cd ./LOG$i || exit
+    export RANK_ID=$i
+    export DEVICE_ID=$i
+    python train.py \
+        --platform=${PLATFORM} \
+        --device_num=${RANK_SIZE} \
+        --device_id=${DEVICE_ID} \
+        --dataset=${DATASET} \
+        --dataset_path=${DATASET_PATH} \
+        --save_checkpoint_path=${CHECKPOINT_PATH} \
+        --weight_decay=False \
+        --sink_mode=True > log.txt 2>&1 &
+    cd ..
+done
+

model_zoo/official/recommend/naml/src/dataset.py

@@ -16,7 +16,7 @@
 import re
 import os
 import random
-from collections import namedtuple
+import math
 import pickle
 
 import numpy as np
@@ -24,9 +24,6 @@ import mindspore.dataset as ds
 
 ds.config.set_prefetch_size(8)
 
-NEWS_ITEMS = ['category', 'subcategory', 'title', 'abstract']
-News = namedtuple('News', NEWS_ITEMS)
-
 class MINDPreprocess:
     """
     MIND dataset Preprocess class.
@@ -279,10 +276,43 @@ class EvalCandidateNews(EvalDatasetBase):
         nid, label = self.preprocess.impression_list[index]
         return uid, nid, label
 
+class DistributedSampler():
+    """
+    sampling the dataset.
+
+    Args:
+    Returns:
+        num_samples, number of samples.
+    """
+    def __init__(self, preprocess: MINDPreprocess, rank, group_size, shuffle=True, seed=0):
+        self.preprocess = preprocess
+        self.rank = rank
+        self.group_size = group_size
+        self.dataset_length = preprocess.total_count
+        self.num_samples = int(math.ceil(self.dataset_length * 1.0 / self.group_size))
+        self.total_size = self.num_samples * self.group_size
+        self.shuffle = shuffle
+        self.seed = seed
+
+    def __iter__(self):
+        if self.shuffle:
+            self.seed = (self.seed + 1) & 0xffffffff
+            np.random.seed(self.seed)
+            indices = np.random.permutation(self.dataset_length).tolist()
+        else:
+            indices = list(range(len(self.dataset_length)))
+
+        indices += indices[:(self.total_size - len(indices))]
+        indices = indices[self.rank::self.group_size]
+        return iter(indices)
+
+    def __len__(self):
+        return self.num_samples
 
-def create_dataset(mindpreprocess, batch_size=64):
+def create_dataset(mindpreprocess, batch_size=64, rank=0, group_size=1):
     """Get generator dataset when training."""
-    dataset = ds.GeneratorDataset(mindpreprocess, mindpreprocess.column_names, shuffle=True)
+    sampler = DistributedSampler(mindpreprocess, rank, group_size, shuffle=True)
+    dataset = ds.GeneratorDataset(mindpreprocess, mindpreprocess.column_names, sampler=sampler)
     dataset = dataset.batch(batch_size, drop_remainder=True)
     return dataset
 

model_zoo/official/recommend/naml/src/option.py

@@ -16,6 +16,7 @@
 import argparse
 import ast
 import os
+import math
 from mindspore.context import ParallelMode
 from mindspore import context
 from mindspore.communication.management import init, get_rank
@@ -52,7 +53,6 @@ def get_args(phase):
     parser.add_argument('--batch_size', type=int, default=64, help='size of each batch')
     # Training specifications
     if phase == "train":
-        parser.add_argument('--train_dataset_path', type=str, default=None, help='training set directory')
         parser.add_argument('--epochs', type=int, default=None, help='number of epochs for training')
         parser.add_argument('--lr', type=float, default=None, help='learning rate')
         parser.add_argument('--beta1', type=float, default=0.9, help='ADAM beta1')
@@ -72,7 +72,6 @@ def get_args(phase):
                             help="Save checkpoint path, default is checkpoint.")
         parser.add_argument('--dropout_ratio', type=float, default=0.2, help='ratio of dropout')
     if phase == "eval":
-        parser.add_argument('--eval_dataset_path', type=str, default=None)
         parser.add_argument('--neg_sample', type=int, default=-1, \
                             help='number of negative samples in negative sampling')
     if phase == "export":
@@ -100,7 +99,7 @@ def get_args(phase):
     args.n_sub_categories = cfg.n_sub_categories
     args.n_words = cfg.n_words
     if phase == "train":
-        args.epochs = cfg.epochs if args.epochs is None else args.epochs
+        args.epochs = cfg.epochs if args.epochs is None else args.epochs * math.ceil(args.device_num ** 0.5)
         args.lr = cfg.lr if args.lr is None else args.lr
         args.print_times = cfg.print_times if args.print_times is None else args.print_times
     args.embedding_file = cfg.embedding_file.format(args.dataset_path)

model_zoo/official/recommend/naml/train.py

@@ -34,7 +34,8 @@ if __name__ == '__main__':
     if args.checkpoint_path is not None:
         load_checkpoint(args.pretrain_checkpoint, net_with_loss)
     mindpreprocess_train = MINDPreprocess(vars(args), dataset_path=args.train_dataset_path)
-    dataset = create_dataset(mindpreprocess_train, batch_size=args.batch_size)
+    dataset = create_dataset(mindpreprocess_train, batch_size=args.batch_size, rank=args.rank,
+                             group_size=args.device_num)
     args.dataset_size = dataset.get_dataset_size()
     args.print_times = min(args.dataset_size, args.print_times)
     if args.weight_decay: