diff --git a/test/test_operations.py b/test/test_operations.py
index b36700bae8e1..e6fe22330607 100644
--- a/test/test_operations.py
+++ b/test/test_operations.py
@@ -1757,6 +1757,35 @@ def test_wait_device_ops(self):
                     "ExecuteChainedTime" in met.metric_names())
 
 
+class TestDebuggingUtil(test_utils.XlaTestCase):
+
+  def test_get_xla_tensor_debug_info(self):
+    if xu.getenv_as('XLA_USE_EAGER_DEBUG_MODE', str, '1'):
+      # ignore this test for eager debug mode since it will
+      # mess up the IR.
+      return
+    device = xm.xla_device()
+    # test non xla tensor
+    cpu_t1 = torch.randn(5)
+    cpu_t1_info = torch_xla._XLAC._get_xla_tensor_debug_info(cpu_t1)
+    self.assertIn('Not a XLATensor', cpu_t1_info)
+
+    # test a tensor with IR
+    t1 = cpu_t1.to(device)
+    t2 = t1 + 5
+    t2_info = torch_xla._XLAC._get_xla_tensor_debug_info(t2)
+    self.assertIn('XLA Shape: f32[5]', t2_info)
+    self.assertIn('aten::add', t2_info)
+    self.assertIn('XLAData: None', t2_info)
+
+    # after makr_step XLAData should present
+    xm.mark_step()
+    t2_info_new = torch_xla._XLAC._get_xla_tensor_debug_info(t2)
+    self.assertNotIn('XLAData: None', t2_info_new)
+    self.assertIn('Data Shape: f32[5]', t2_info_new)
+    self.assertIn('IR: None', t2_info_new)
+
+
 class TestOpBuilder(test_utils.XlaTestCase):
 
   def runOpBuilderTest(self,
diff --git a/torch_xla/csrc/init_python_bindings.cpp b/torch_xla/csrc/init_python_bindings.cpp
index e8c70a719ab4..6e501e997f15 100644
--- a/torch_xla/csrc/init_python_bindings.cpp
+++ b/torch_xla/csrc/init_python_bindings.cpp
@@ -361,6 +361,47 @@ std::string GetTensorsHloGraph(const std::vector<at::Tensor>& tensors,
   return XLAGraphExecutor::Get()->DumpHloComputation(xtensors, get_stable_hlo);
 }
 
+std::string GetXLATensorDebugInfo(const at::Tensor& tensor) {
+  auto xtensor = bridge::TryGetXlaTensor(tensor);
+  if (!xtensor) {
+    return "Not a XLATensor\n";
+  }
+  std::stringstream ss;
+  ss << "XLATensor {\n";
+  ss << "TensorID: " << xtensor->GetUniqueId() << "\n";
+  ss << "Device: " << xtensor->GetDevice() << "\n";
+  ss << "XLA Shape: " << xtensor->shape().get().ToString() << "\n";
+
+  torch::lazy::Value ir_value = xtensor->CurrentIrValue();
+  ss << "IR: ";
+  if (ir_value) {
+    ss << ir_value.node->ToString() << "\n";
+  } else {
+    ss << "None\n";
+  }
+
+  torch::lazy::BackendDataPtr handle = xtensor->CurrentDataHandle();
+  ss << "XLAData: ";
+  if (handle) {
+    auto data = UnwrapXlaData(handle);
+    ss << "\n  Data Device: " << data->device() << "\n";
+    ss << "  Data Shape: " << data->shape().ToString() << "\n";
+  } else {
+    ss << "None\n";
+  }
+
+  auto at_tensor = xtensor->CurrentTensorData();
+  ss << "Tensor on host: ";
+  if (at_tensor) {
+    ss << " with size " << at_tensor->sizes() << "\n";
+  } else {
+    ss << "None\n";
+  }
+
+  ss << "}\n";
+  return ss.str();
+}
+
 std::string GetLiveTensorsReport(size_t nodes_threshold,
                                  const std::string& device_str) {
   auto opt_device = GetOptionalDevice(device_str);
@@ -853,6 +894,10 @@ void InitXlaModuleBindings(py::module m) {
         [](const std::vector<at::Tensor>& tensors) -> std::string {
           return GetTensorsHloGraph(tensors);
         });
+  m.def("_get_xla_tensor_debug_info",
+        [](const at::Tensor& tensor) -> std::string {
+          return GetXLATensorDebugInfo(tensor);
+        });
   py::class_<XLATensor::ShardingSpec, XLATensor::ShardingSpecPtr>(
       m, "XlaShardingSpec")
       .def(py::init([](at::Tensor tensor, const py::list& tile_assignment,