diff --git a/tensorflow/core/common_runtime/eager/execute.cc b/tensorflow/core/common_runtime/eager/execute.cc
index 595afb9c22e..1d44e29f4dc 100644
--- a/tensorflow/core/common_runtime/eager/execute.cc
+++ b/tensorflow/core/common_runtime/eager/execute.cc
@@ -1197,9 +1197,7 @@ Status LocalEagerCopyToDevice(TensorHandle* h, EagerContext* ctx, Device* dstd,
   } else {
     tensorflow::Tensor tensor;
     TF_RETURN_IF_ERROR(h->CopyToDevice(ctx, dstd, &tensor));
-    const bool dst_cpu = dstd->tensorflow_gpu_device_info() == nullptr;
-    return TensorHandle::CreateLocalHandle(tensor, dst_cpu ? nullptr : dstd,
-                                           ctx, result);
+    return TensorHandle::CreateLocalHandle(tensor, dstd, ctx, result);
     return Status::OK();
   }
 }
diff --git a/tensorflow/core/common_runtime/eager/tensor_handle.cc b/tensorflow/core/common_runtime/eager/tensor_handle.cc
index d93ccf967ef..a6a2feef60a 100644
--- a/tensorflow/core/common_runtime/eager/tensor_handle.cc
+++ b/tensorflow/core/common_runtime/eager/tensor_handle.cc
@@ -381,9 +381,10 @@ void TensorHandle::Poison(Status status) {
 Status TensorHandle::CopyToDevice(EagerContext* ctx, tensorflow::Device* dstd,
                                   tensorflow::Tensor* output) {
   tensorflow::Device* srcd = DeviceOrHostCPU(ctx);
-  bool is_same_device = (srcd == dstd) || (srcd->name() == dstd->name());
   const bool dst_cpu = dstd->tensorflow_gpu_device_info() == nullptr;
   const bool src_cpu = srcd->tensorflow_gpu_device_info() == nullptr;
+  bool is_same_device =
+      (srcd == dstd) || (srcd->name() == dstd->name()) || (dst_cpu && src_cpu);
 
   const tensorflow::Tensor* src = nullptr;
   TF_RETURN_IF_ERROR(Tensor(&src));
diff --git a/tensorflow/python/eager/core_test.py b/tensorflow/python/eager/core_test.py
index 1c5756914f4..7b26a3cf962 100644
--- a/tensorflow/python/eager/core_test.py
+++ b/tensorflow/python/eager/core_test.py
@@ -31,6 +31,7 @@ from tensorflow.python.eager import core
 from tensorflow.python.eager import def_function
 from tensorflow.python.eager import execute as execute_lib
 from tensorflow.python.eager import test
+from tensorflow.python.framework import config
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
@@ -62,8 +63,21 @@ def current_device():
   return constant_op.constant(1.).device
 
 
+def configure_virtual_cpus():
+  cpus = config.list_physical_devices('CPU')
+  # Set 2 virtual CPUs
+  config.set_virtual_device_configuration(cpus[0], [
+      context.VirtualDeviceConfiguration(),
+      context.VirtualDeviceConfiguration()
+  ])
+
+
 class TFETest(test_util.TensorFlowTestCase):
 
+  def setUp(self):
+    super(TFETest, self).setUp()
+    configure_virtual_cpus()
+
   def testContext(self):
     ctx = context.Context()
     self.assertTrue(ctx.executing_eagerly())
@@ -130,6 +144,13 @@ class TFETest(test_util.TensorFlowTestCase):
                      cpu_stats.device)
     self.assertGreaterEqual(len(cpu_stats.node_stats), 1)
 
+  def testMultiCpuPlacement(self):
+    with ops.device('cpu:1'):
+      x = constant_op.constant(1.0)
+    y = array_ops.identity(x)
+    self.assertEqual(x.device, '/job:localhost/replica:0/task:0/device:CPU:1')
+    self.assertEqual(y.device, '/job:localhost/replica:0/task:0/device:CPU:0')
+
   @test_util.run_gpu_only
   def testShouldCopy(self):
     with ops.device('gpu:0'):
@@ -758,6 +779,10 @@ class SendRecvTest(test_util.TensorFlowTestCase):
                'recv_device', device_name,
                'client_terminated', False))[0]
 
+  def setUp(self):
+    super(SendRecvTest, self).setUp()
+    configure_virtual_cpus()
+
   def testBasic(self):
     t0 = constant_op.constant(1.0)
     t1 = constant_op.constant(2.0)
@@ -789,6 +814,10 @@ class SendRecvTest(test_util.TensorFlowTestCase):
 
 class EagerTensorCacheTest(test_util.TensorFlowTestCase):
 
+  def setUp(self):
+    super(EagerTensorCacheTest, self).setUp()
+    configure_virtual_cpus()
+
   def testCacheSkipsTensorsTooLarge(self):
     cache = context._EagerTensorCache(max_items=100, max_tensor_size=3)
     cache.put('1', array_ops.zeros((2, 2)))