Add lstm ascend distribute train

model_zoo/official/nlp/lstm/eval.py

@@ -22,11 +22,9 @@ import numpy as np
 
 from src.config import lstm_cfg, lstm_cfg_ascend
 from src.dataset import lstm_create_dataset, convert_to_mindrecord
-from src.lr_schedule import get_lr
 from src.lstm import SentimentNet
 from mindspore import Tensor, nn, Model, context
-from mindspore.nn import Accuracy
-from mindspore.train.callback import LossMonitor
+from mindspore.nn import Accuracy, Recall, F1
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 
 if __name__ == '__main__':
@@ -79,20 +77,8 @@ if __name__ == '__main__':
 
     loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
     ds_eval = lstm_create_dataset(args.preprocess_path, cfg.batch_size, training=False)
-    if cfg.dynamic_lr:
-        lr = Tensor(get_lr(global_step=cfg.global_step,
-                           lr_init=cfg.lr_init, lr_end=cfg.lr_end, lr_max=cfg.lr_max,
-                           warmup_epochs=cfg.warmup_epochs,
-                           total_epochs=cfg.num_epochs,
-                           steps_per_epoch=ds_eval.get_dataset_size(),
-                           lr_adjust_epoch=cfg.lr_adjust_epoch))
-    else:
-        lr = cfg.learning_rate
-
-    opt = nn.Momentum(network.trainable_params(), lr, cfg.momentum)
-    loss_cb = LossMonitor()
 
-    model = Model(network, loss, opt, {'acc': Accuracy()})
+    model = Model(network, loss, metrics={'acc': Accuracy(), 'recall': Recall(), 'f1': F1()})
 
     print("============== Starting Testing ==============")
     param_dict = load_checkpoint(args.ckpt_path)

model_zoo/official/nlp/lstm/script/run_distribute_train_ascend.sh

@@ -0,0 +1,48 @@
+#!/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_ascend.sh RANK_TABLE_FILE DEVICE_NUM ACLIMDB_DIR GLOVE_DIR"
+echo "for example: bash run_distribute_train_ascend.sh /path/hccl.json 8 /path/aclimdb /path/glove"
+echo "It is better to use absolute path."
+echo "=============================================================================================================="
+
+ROOT_PATH=`pwd`
+export RANK_TABLE_FILE=$1
+RANK_SIZE=$2
+ACLIMDB_DIR=$3
+GLOVE_DIR=$4
+
+
+for((i=0;i<${RANK_SIZE};i++));
+do
+    rm ${ROOT_PATH}/device$i/ -rf
+    mkdir ${ROOT_PATH}/device$i
+    cd ${ROOT_PATH}/device$i || exit
+    cp ../../*.py ./
+    cp -r ../../src ./
+    export RANK_ID=$i
+    export DEVICE_ID=$i
+    python train.py  \
+        --device_target="Ascend" \
+        --aclimdb_path=$ACLIMDB_DIR \
+        --glove_path=$GLOVE_DIR \
+        --distribute=true \
+        --device_num=$RANK_SIZE \
+        --preprocess=true  \
+        --preprocess_path=./preprocess > log.txt 2>&1 &
+done

model_zoo/official/nlp/lstm/src/config.py

@@ -33,7 +33,7 @@ lstm_cfg = edict({
     'keep_checkpoint_max': 10
 })
 
-# LSTM CONFIG IN ASCEND
+# LSTM CONFIG IN ASCEND for 1p training
 lstm_cfg_ascend = edict({
     'num_classes': 2,
     'momentum': 0.9,
@@ -53,3 +53,24 @@ lstm_cfg_ascend = edict({
     'warmup_epochs': 1,
     'global_step': 0
 })
+
+# LSTM CONFIG IN ASCEND for 8p training
+lstm_cfg_ascend_8p = edict({
+    'num_classes': 2,
+    'momentum': 0.9,
+    'num_epochs': 20,
+    'batch_size': 64,
+    'embed_size': 300,
+    'num_hiddens': 128,
+    'num_layers': 2,
+    'bidirectional': True,
+    'save_checkpoint_steps': 7800,
+    'keep_checkpoint_max': 10,
+    'dynamic_lr': True,
+    'lr_init': 0.05,
+    'lr_end': 0.01,
+    'lr_max': 0.3,
+    'lr_adjust_epoch': 20,
+    'warmup_epochs': 2,
+    'global_step': 0
+})

model_zoo/official/nlp/lstm/src/dataset.py

@@ -24,14 +24,15 @@ from mindspore.mindrecord import FileWriter
 from .imdb import ImdbParser
 
 
-def lstm_create_dataset(data_home, batch_size, repeat_num=1, training=True):
+def lstm_create_dataset(data_home, batch_size, repeat_num=1, training=True, device_num=1, rank=0):
     """Data operations."""
     ds.config.set_seed(1)
     data_dir = os.path.join(data_home, "aclImdb_train.mindrecord0")
     if not training:
         data_dir = os.path.join(data_home, "aclImdb_test.mindrecord0")
 
-    data_set = ds.MindDataset(data_dir, columns_list=["feature", "label"], num_parallel_workers=4)
+    data_set = ds.MindDataset(data_dir, columns_list=["feature", "label"], num_parallel_workers=4,
+                              num_shards=device_num, shard_id=rank)
 
     # apply map operations on images
     data_set = data_set.shuffle(buffer_size=data_set.get_dataset_size())

model_zoo/official/nlp/lstm/train.py

@@ -20,7 +20,7 @@ import os
 
 import numpy as np
 
-from src.config import lstm_cfg, lstm_cfg_ascend
+from src.config import lstm_cfg, lstm_cfg_ascend, lstm_cfg_ascend_8p
 from src.dataset import convert_to_mindrecord
 from src.dataset import lstm_create_dataset
 from src.lr_schedule import get_lr
@@ -29,6 +29,8 @@ from mindspore import Tensor, nn, Model, context
 from mindspore.nn import Accuracy
 from mindspore.train.callback import LossMonitor, CheckpointConfig, ModelCheckpoint, TimeMonitor
 from mindspore.train.serialization import load_param_into_net, load_checkpoint
+from mindspore.communication.management import init, get_rank
+from mindspore.context import ParallelMode
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='MindSpore LSTM Example')
@@ -46,6 +48,9 @@ if __name__ == '__main__':
                         help='the pretrained checkpoint file path.')
     parser.add_argument('--device_target', type=str, default="Ascend", choices=['GPU', 'CPU', 'Ascend'],
                         help='the target device to run, support "GPU", "CPU". Default: "Ascend".')
+    parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
+    parser.add_argument("--distribute", type=str, default="false", choices=["true", "false"],
+                        help="Run distribute, default is false.")
     args = parser.parse_args()
 
     context.set_context(
@@ -53,8 +58,20 @@ if __name__ == '__main__':
         save_graphs=False,
         device_target=args.device_target)
 
+    rank = 0
+    device_num = 1
+
     if args.device_target == 'Ascend':
         cfg = lstm_cfg_ascend
+        if args.distribute == "true":
+            cfg = lstm_cfg_ascend_8p
+            init()
+            device_num = args.device_num
+            rank = get_rank()
+
+            context.reset_auto_parallel_context()
+            context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
+                                              device_num=device_num)
     else:
         cfg = lstm_cfg
 
@@ -82,7 +99,7 @@ if __name__ == '__main__':
     if args.pre_trained:
         load_param_into_net(network, load_checkpoint(args.pre_trained))
 
-    ds_train = lstm_create_dataset(args.preprocess_path, cfg.batch_size, 1)
+    ds_train = lstm_create_dataset(args.preprocess_path, cfg.batch_size, 1, device_num=device_num, rank=rank)
 
     loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
     if cfg.dynamic_lr: