diff --git a/paddle/fluid/operators/reader/buffered_reader.cc b/paddle/fluid/operators/reader/buffered_reader.cc
index f5d55791d86c68bf800b869ee2be981bd6ab63b5..17c84530b23e667d8da4bf18cf44a89d44b1b51e 100644
--- a/paddle/fluid/operators/reader/buffered_reader.cc
+++ b/paddle/fluid/operators/reader/buffered_reader.cc
@@ -68,7 +68,6 @@ BufferedReader::BufferedReader(
     stream_ = platform::NpuStreamResourcePool::Instance().New(dev_idx);
   }
 #endif
-  is_same_place_ = false;
   cpu_buffer_.resize(buffer_size);
   cuda_buffer_.resize(buffer_size);
   npu_buffer_.resize(buffer_size);
@@ -116,7 +115,7 @@ void BufferedReader::ReadAsync(size_t i) {
         std::vector<void *> cuda_pinned_ptrs;
         cuda_pinned_ptrs.reserve(cpu.size());
         platform::RecordEvent record_event("BufferedReader:MemoryCopy");
-        // NODE(chenwehiang): When we use CUDAPinned Memory, we need call
+        // NODE(chenweihang): When we use CUDAPinned Memory, we need call
         // cudaHostAlloc, that is a CUDA API, calling CUDA API need load
         // cuda lib into device, it will cost hundreds of MB of GPU memory.
         // If we don't set Device here, which will use CUDAPlace(0) default.
@@ -126,18 +125,21 @@ void BufferedReader::ReadAsync(size_t i) {
           if (platform::is_cpu_place(cpu[i].place())) {
             cuda[i].Resize(cpu[i].dims());
             cuda[i].set_layout(cpu[i].layout());
-            cuda_pinned_ptrs.emplace_back(
-                cuda[i].mutable_data(cuda_pinned_place, cpu[i].type()));
+            cuda_pinned_ptrs[i] =
+                cuda[i].mutable_data(cuda_pinned_place, cpu[i].type());
             auto size =
                 cpu[i].numel() * paddle::framework::SizeOfType(cpu[i].type());
 
             memory::Copy(cuda_pinned_place, cuda_pinned_ptrs[i],
                          BOOST_GET_CONST(platform::CPUPlace, cpu[i].place()),
                          cpu[i].data<void>(), size);
+
             cuda[i].set_lod(cpu[i].lod());
           } else {
-            // we set same place flag & use cpu[i] directly
-            is_same_place_ = true;
+            // Here the cpu[i]'s place may be CUDAPlace, CUDAPinnedPlace, or
+            // others, we don't copy the memory of it to CUDAPinnedPlace, but
+            // we should share tensor data to cuda[i]
+            cuda[i].ShareDataWith(cpu[i]);
           }
         }
       } else {
@@ -296,9 +298,9 @@ void BufferedReader::ReadNextImpl(std::vector<framework::LoDTensor> *out) {
     return;
   }
 
-  if (platform::is_gpu_place(place_) && !is_same_place_) {
+  if (platform::is_gpu_place(place_)) {
     *out = std::move(cuda_buffer_[i]);
-  } else if (platform::is_npu_place(place_) && !is_same_place_) {
+  } else if (platform::is_npu_place(place_)) {
     *out = std::move(npu_buffer_[i]);
   } else {
     *out = std::move(cpu_buffer_[i]);
diff --git a/paddle/fluid/operators/reader/buffered_reader.h b/paddle/fluid/operators/reader/buffered_reader.h
index 9f7b0e753281eb2e6476bc931b454b3b15340c3c..5b4bbc7d62cd8f1cdb64b0454279dada2f1a0e69 100644
--- a/paddle/fluid/operators/reader/buffered_reader.h
+++ b/paddle/fluid/operators/reader/buffered_reader.h
@@ -67,7 +67,6 @@ class BufferedReader : public framework::DecoratedReader {
   // buffer, just read async and create futures as buffer size. However, to
   // malloc tensors every time is extremely slow. Here we store all data in
   // buffers and prevent alloc every time.
-  bool is_same_place_;
   std::vector<TensorVec> cpu_buffer_;
   std::vector<TensorVec> cuda_buffer_;
   std::vector<TensorVec> npu_buffer_;
diff --git a/python/paddle/fluid/tests/unittests/test_dataloader_dataset.py b/python/paddle/fluid/tests/unittests/test_dataloader_dataset.py
index b8c498fe4a3c71296101bc08e6bbbe0887ac8b6c..08589f0191d8c698ecd8a4017d6c14fa476610b1 100644
--- a/python/paddle/fluid/tests/unittests/test_dataloader_dataset.py
+++ b/python/paddle/fluid/tests/unittests/test_dataloader_dataset.py
@@ -14,9 +14,12 @@
 
 from __future__ import division
 
+import sys
 import unittest
 import numpy as np
 
+import paddle
+import paddle.vision.transforms as transforms
 import paddle.fluid as fluid
 from paddle.io import *
 
@@ -37,5 +40,48 @@ class TestDatasetAbstract(unittest.TestCase):
             pass
 
 
+class TestDatasetWithDiffOutputPlace(unittest.TestCase):
+    def get_dataloader(self, num_workers):
+        dataset = paddle.vision.datasets.MNIST(
+            mode='test', transform=transforms.ToTensor())
+        loader = paddle.io.DataLoader(
+            dataset, batch_size=32, num_workers=num_workers, shuffle=True)
+        return loader
+
+    def run_check_on_cpu(self):
+        paddle.set_device('cpu')
+        loader = self.get_dataloader(0)
+        for image, label in loader:
+            self.assertTrue(image.place.is_cpu_place())
+            self.assertTrue(label.place.is_cpu_place())
+            break
+
+    def test_single_process(self):
+        self.run_check_on_cpu()
+        if paddle.is_compiled_with_cuda():
+            # Get (image, label) tuple from MNIST dataset
+            # - the image is on CUDAPlace, label is on CPUPlace
+            paddle.set_device('gpu')
+            loader = self.get_dataloader(0)
+            for image, label in loader:
+                self.assertTrue(image.place.is_gpu_place())
+                self.assertTrue(label.place.is_cuda_pinned_place())
+                break
+
+    def test_multi_process(self):
+        # DataLoader with multi-process mode is not supported on MacOs and Windows currently
+        if sys.platform != 'darwin' and sys.platform != 'win32':
+            self.run_check_on_cpu()
+            if paddle.is_compiled_with_cuda():
+                # Get (image, label) tuple from MNIST dataset
+                # - the image and label are on CPUPlace
+                paddle.set_device('gpu')
+                loader = self.get_dataloader(1)
+                for image, label in loader:
+                    self.assertTrue(image.place.is_cuda_pinned_place())
+                    self.assertTrue(label.place.is_cuda_pinned_place())
+                    break
+
+
 if __name__ == '__main__':
     unittest.main()