diff --git a/PyTorch/dev/cv/image_classification/NeuMF_ID0351_for_PyTorch/src/data.py b/PyTorch/dev/cv/image_classification/NeuMF_ID0351_for_PyTorch/src/data.py
index ebdafffd1f85ca5e91a6ad0322b0022c742dcb3c..4c7fb24a47e9721405b49bb00ae73b2e7cf1be3f 100644
--- a/PyTorch/dev/cv/image_classification/NeuMF_ID0351_for_PyTorch/src/data.py
+++ b/PyTorch/dev/cv/image_classification/NeuMF_ID0351_for_PyTorch/src/data.py
@@ -123,8 +123,8 @@ class SampleGenerator(object):
                 users.append(int(row.userId))
                 items.append(int(row.negatives[i]))
                 ratings.append(float(0))  # negative samples get 0 rating
-        dataset = UserItemRatingDataset(user_tensor=torch.LongTensor(users),
-                                        item_tensor=torch.LongTensor(items),
+        dataset = UserItemRatingDataset(user_tensor=torch.IntTensor(users),
+                                        item_tensor=torch.IntTensor(items),
                                         target_tensor=torch.FloatTensor(ratings))
         return DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=16)
 
diff --git a/PyTorch/dev/cv/image_classification/NeuMF_ID0351_for_PyTorch/src/engine.py b/PyTorch/dev/cv/image_classification/NeuMF_ID0351_for_PyTorch/src/engine.py
index 41c71feef54c8aad8721a3fe4b7d4685f9fcbbda..405a722b3ee4b7209b004283ce60ff33eb639fe7 100644
--- a/PyTorch/dev/cv/image_classification/NeuMF_ID0351_for_PyTorch/src/engine.py
+++ b/PyTorch/dev/cv/image_classification/NeuMF_ID0351_for_PyTorch/src/engine.py
@@ -72,30 +72,30 @@ class Engine(object):
 
     def train_single_batch(self, users, items, ratings):
         assert hasattr(self, 'model'), 'Please specify the exact model !'
-        #if self.config['use_npu'] is True:
-        users, items, ratings = users.npu(), items.npu(), ratings.npu()
         self.opt.zero_grad()
         ratings_pred = self.model(users, items)
         loss = self.crit(ratings_pred.view(-1), ratings)
-        #loss.backward()
         with amp.scale_loss(loss, self.opt) as scaled_loss:
             scaled_loss.backward()
         self.opt.step()
-        loss = loss.item()
+        loss = loss.detach()
         return loss
 
     def train_an_epoch(self, train_loader, epoch_id):
         assert hasattr(self, 'model'), 'Please specify the exact model !'
         self.model.train()
         total_loss = 0
-        for batch_id, batch in enumerate(train_loader):
+        batch_id = 0
+        from prefetcher import Prefetcher
+        prefetcher = Prefetcher(train_loader)
+        user, item, rating = prefetcher.next()
+        while user is not None:
             start_time=time.time()
-            assert isinstance(batch[0], torch.LongTensor)
-            user, item, rating = batch[0], batch[1], batch[2]
-            rating = rating.float()
             loss = self.train_single_batch(user, item, rating)
             print('[Training Epoch {}] Batch {}/{}, Loss:{:.3f}, Train-time:{:.4f}'.format(epoch_id, batch_id, len(train_loader), loss,time.time()-start_time))
             total_loss += loss
+            batch_id += 1
+            user, item, rating = prefetcher.next()
         self._writer.add_scalar('model/loss', total_loss, epoch_id)
 
     def evaluate(self, evaluate_data, epoch_id):
diff --git a/PyTorch/dev/cv/image_classification/NeuMF_ID0351_for_PyTorch/src/prefetcher.py b/PyTorch/dev/cv/image_classification/NeuMF_ID0351_for_PyTorch/src/prefetcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e05ead50092cc817ea11c9a17e4a724d0e0deb8
--- /dev/null
+++ b/PyTorch/dev/cv/image_classification/NeuMF_ID0351_for_PyTorch/src/prefetcher.py
@@ -0,0 +1,67 @@
+# Copyright (c) 2020 Huawei Technologies Co., Ltd
+# Copyright (c) 2019, Facebook CORPORATION.
+# All rights reserved.
+#
+# Licensed under the BSD 3-Clause License  (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://opensource.org/licenses/BSD-3-Clause
+#
+# 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.
+
+import torch
+
+
+class Prefetcher(object):
+    """Prefetcher using on npu device.
+
+    Origin Code URL:
+    https://github.com/implus/PytorchInsight/blob/master/classification/imagenet_fast.py#L280
+
+    Args:
+        loder (torch.utils.data.DataLoader or DataLoader like iterator):
+            Using to generate inputs after preprocessing.
+        stream (torch.npu.Stream): Default None.
+            Because of the limitation of NPU's memory mechanism,
+            if prefetcher is initialized repeatedly during training,
+            a defined stream should be introduced to prevent memory leakage;
+            if prefetcher is initialized only once during training,
+            a defined stream is not necessary.
+
+    Returns:
+        float: tensors of shape (k, 5) and (k, 1). Labels are 0-based.
+    """
+
+    def __init__(self, loader, stream=None):
+        self.loader = iter(loader)
+        self.stream = stream if stream is not None else torch.npu.Stream()
+        self.preload()
+
+    def preload(self):
+        try:
+            self.user, self.item, self.rating = next(self.loader)
+            assert isinstance(self.user, torch.IntTensor)
+            self.rating = self.rating.float()
+        except StopIteration:
+            self.user = None
+            self.item = None
+            return
+
+        with torch.npu.stream(self.stream):
+            self.user = self.user.npu(non_blocking=True)
+            self.item = self.item.npu(non_blocking=True)
+            self.rating = self.rating.npu(non_blocking=True)
+
+    def next(self):
+        torch.npu.current_stream().wait_stream(self.stream)
+        user = self.user
+        item = self.item
+        rating = self.rating
+        if user is not None:
+            self.preload()
+        return user, item, rating