diff --git a/test/test_operations.py b/test/test_operations.py
index e4ee4c3e954..e1dad566536 100644
--- a/test/test_operations.py
+++ b/test/test_operations.py
@@ -2634,6 +2634,37 @@ def test_dlpack_pytorch_cuda_to_xla(self):
     t3_cuda.fill_(6)
     self.assertTrue(torch.allclose(xla_t3.cpu(), t3_cuda.cpu()))
 
+  @onlyIfTorchSupportsCUDA
+  @onlyIfPJRTDeviceIsCUDA
+  def test_dlpack_pytorch_cuda_to_xla_protocol_conversion(self):
+    # Unlike the test_dlpack_pytorch_cuda_to_xla,
+    # torch_cuda_tensor has attribute __dlpack__ and __dlpack_device__.
+    # From cuda tensors to xla tensors, the synchronization is handdled implicitly.
+    t1_cuda = torch.arange(5).cuda()
+    xla_t1 = xdlpack.from_dlpack(t1_cuda)
+    self.assertEqual(xla_t1.device.type, 'xla')
+    self.assertEqual(xla_t1.device.index, t1_cuda.device.index)
+    t1_cuda[0] = t1_cuda[0] + 20
+    self.assertTrue(torch.allclose(xla_t1.cpu(), t1_cuda.cpu()))
+
+    t2_cuda = torch.tensor(5).cuda()
+    xla_t2 = xdlpack.from_dlpack(t2_cuda)
+    self.assertEqual(xla_t2.device.type, 'xla')
+    self.assertEqual(xla_t2.device.index, t2_cuda.device.index)
+    t2_cuda.fill_(6)
+    self.assertTrue(torch.allclose(xla_t2.cpu(), t2_cuda.cpu()))
+
+    cuda1 = torch.device('cuda:1')
+    t3_cuda = torch.tensor(5, device=cuda1)
+    xla_t3 = xdlpack.from_dlpack(t3_cuda)
+    self.assertEqual(xla_t3.device.type, 'xla')
+    self.assertEqual(
+        xla_t3.device.index,
+        t3_cuda.device.index,
+        msg='both value should 1. xla_t3.device should be xla:1.')
+    t3_cuda.fill_(6)
+    self.assertTrue(torch.allclose(xla_t3.cpu(), t3_cuda.cpu()))
+
   @onlyIfTorchSupportsCUDA
   @onlyIfPJRTDeviceIsCUDA
   def test_dlpack_xla_to_pytorch_cuda(self):
diff --git a/torch_xla/csrc/init_python_bindings.cpp b/torch_xla/csrc/init_python_bindings.cpp
index 5d7707d5699..12e955adf38 100644
--- a/torch_xla/csrc/init_python_bindings.cpp
+++ b/torch_xla/csrc/init_python_bindings.cpp
@@ -1296,6 +1296,9 @@ void InitXlaModuleBindings(py::module m) {
       return runtime::GetComputationClient()->GetLocalDevices();
     }
   });
+  m.def("_get_stream_for_cuda_device", [](const int device_id) {
+    return runtime::GetComputationClient()->GetCudaStreamForDevice(device_id);
+  });
   m.def("_xla_num_devices", []() -> int64_t {
     if (UseVirtualDevice()) {
       return 1;
diff --git a/torch_xla/csrc/runtime/computation_client.h b/torch_xla/csrc/runtime/computation_client.h
index 93664900ebd..a66ae2a7fa4 100644
--- a/torch_xla/csrc/runtime/computation_client.h
+++ b/torch_xla/csrc/runtime/computation_client.h
@@ -361,6 +361,8 @@ class ComputationClient {
   virtual absl::StatusOr<xla::PjRtDevice*> LookupAddressableDevice(
       int local_device_id) const = 0;
 
+  virtual std::intptr_t GetCudaStreamForDevice(int local_device_id) const = 0;
+
   virtual size_t GetNumDevices() const = 0;
 
   virtual std::vector<std::string> GetLocalDevices() const = 0;
diff --git a/torch_xla/csrc/runtime/ifrt_computation_client.h b/torch_xla/csrc/runtime/ifrt_computation_client.h
index 6f71da8422f..59664d045e8 100644
--- a/torch_xla/csrc/runtime/ifrt_computation_client.h
+++ b/torch_xla/csrc/runtime/ifrt_computation_client.h
@@ -96,6 +96,10 @@ class IfrtComputationClient : public ComputationClient {
     XLA_ERROR() << __FUNCTION__ << " not implemented";
   }
 
+  std::intptr_t GetCudaStreamForDevice(int local_device_id) const override {
+    XLA_ERROR() << __FUNCTION__ << " not implemented";
+  }
+
   std::vector<std::string> GetLocalDevices() const override;
 
   std::vector<std::string> GetAllDevices() const override;
diff --git a/torch_xla/csrc/runtime/pjrt_computation_client.h b/torch_xla/csrc/runtime/pjrt_computation_client.h
index 5ed4326d283..1d31107e6b9 100644
--- a/torch_xla/csrc/runtime/pjrt_computation_client.h
+++ b/torch_xla/csrc/runtime/pjrt_computation_client.h
@@ -104,6 +104,17 @@ class PjRtComputationClient : public ComputationClient {
         xla::PjRtLocalDeviceId(local_device_id));
   }
 
+  std::intptr_t GetCudaStreamForDevice(int local_device_id) const override {
+    absl::StatusOr<xla::PjRtDevice*> pjrt_device =
+        client_->LookupAddressableDevice(
+            xla::PjRtLocalDeviceId(local_device_id));
+    XLA_CHECK(pjrt_device.ok()) << "Failed to get a PjRt device.";
+    absl::StatusOr<std::intptr_t> stream =
+        pjrt_device.value()->GetStreamForExternalReadyEvents();
+    XLA_CHECK(stream.ok()) << "Failed to get a stream.";
+    return stream.value();
+  }
+
   std::vector<std::string> GetLocalDevices() const override;
 
   std::vector<std::string> GetAllDevices() const override;
diff --git a/torch_xla/utils/dlpack.py b/torch_xla/utils/dlpack.py
index 9f93d532b27..c49083e4403 100644
--- a/torch_xla/utils/dlpack.py
+++ b/torch_xla/utils/dlpack.py
@@ -1,4 +1,5 @@
 from typing import Any
+import enum
 import torch_xla
 
 
@@ -6,5 +7,30 @@ def to_dlpack(xla_tensor: Any):
   return torch_xla._XLAC._to_dlpack(xla_tensor)
 
 
+class DLDeviceType(enum.IntEnum):
+  # Enums as in DLPack specification (aten/src/ATen/dlpack.h)
+  kDLCPU = 1,
+  kDLGPU = 2,
+  kDLCPUPinned = 3,
+  kDLOpenCL = 4,
+  kDLVulkan = 7,
+  kDLMetal = 8,
+  kDLVPI = 9,
+  kDLROCM = 10,
+  kDLExtDev = 12,
+  kDLOneAPI = 14,
+
+
 def from_dlpack(ext_tensor: Any):
-  return torch_xla._XLAC._from_dlpack(ext_tensor)
+  if hasattr(ext_tensor, '__dlpack_device__') and hasattr(
+      ext_tensor, '__dlpack__'):
+    device_type, device_id = ext_tensor.__dlpack_device__()
+    if device_type == DLDeviceType.kDLGPU:
+      stream = torch_xla._XLAC._get_stream_for_cuda_device(device_id)
+      dlpack = ext_tensor.__dlpack__(stream=stream)
+    else:
+      dlpack = ext_tensor.__dlpack__()
+  else:
+    dlpack = ext_tensor
+
+  return torch_xla._XLAC._from_dlpack(dlpack)