Add basic device APIs to the top-level torch_xla module.

test/run_tests.sh

@@ -211,6 +211,7 @@ function run_xla_op_tests3 {
   run_test "$CDIR/test_torch_distributed_xla_backend.py"
   run_torchrun "$CDIR/pjrt/test_torchrun.py"
   run_test "$CDIR/test_persistent_cache.py"
+  run_test "$CDIR/test_devices.py"
   # NOTE: this line below is testing export and don't care about GPU
   PJRT_DEVICE=CPU CPU_NUM_DEVICES=1 run_coverage "$CDIR/test_core_aten_ops.py"
 }

test/test_devices.py

@@ -0,0 +1,34 @@
+import os
+
+from absl.testing import absltest, parameterized
+import torch
+import torch_xla as xla
+import torch_xla.runtime as xr
+
+
+class TestDevices(parameterized.TestCase):
+
+  def setUpClass():
+    xr.set_device_type('CPU')
+    os.environ['CPU_NUM_DEVICES'] = '4'
+
+  @parameterized.parameters((None, torch.device('xla:0')),
+                            (0, torch.device('xla:0')),
+                            (3, torch.device('xla:3')))
+  def test_device(self, index, expected):
+    device = xla.device(index)
+    self.assertEqual(device, expected)
+
+  def test_devices(self):
+    self.assertEqual(xla.devices(),
+                     [torch.device(f'xla:{i}') for i in range(4)])
+
+  def test_real_devices(self):
+    self.assertEqual(xla.real_devices(), [f'CPU:{i}' for i in range(4)])
+
+  def test_device_count(self):
+    self.assertEqual(xla.device_count(), 4)
+
+
+if __name__ == "__main__":
+  absltest.main()

torch_xla/__init__.py

@@ -182,3 +182,5 @@ def _init_xla_lazy_backend():
 if os.getenv('XLA_REGISTER_INSTALLED_PLUGINS') == '1':
   plugins.use_dynamic_plugins()
   plugins.register_installed_plugins()
+
+from .torch_xla import *

torch_xla/csrc/init_python_bindings.cpp

@@ -1188,14 +1188,20 @@ void InitXlaModuleBindings(py::module m) {
         runtime::GetComputationClient()->GetAllDevices();
     return all_devices;
   });
-  m.def("_xla_real_devices", [](const std::vector<std::string>& devices) {
-    std::vector<std::string> xla_devices;
-    {
-      NoGilSection nogil;
-      xla_devices = GetXlaDevices(devices);
-    }
-    return xla_devices;
-  });
+  m.def("_xla_real_devices",
+        [](const std::optional<std::vector<std::string>> devices) {
+          if (!devices) {
+            return runtime::GetComputationClient()->GetLocalDevices();
+          }
+
+          std::vector<std::string> xla_devices;
+          {
+            NoGilSection nogil;
+            xla_devices = GetXlaDevices(*devices);
+          }
+          return xla_devices;
+        },
+        py::arg("devices") = std::nullopt);
   m.def("_xla_set_replication_devices",
         [](const std::vector<std::string>& devices) {
           auto replication_devices =

torch_xla/torch_xla.py

@@ -0,0 +1,47 @@
+from typing import List
+import torch
+import torch_xla
+import torch_xla.core.xla_model as xm
+
+
+def device(index: int = None) -> torch.device:
+  """Returns a given instance of an XLA device.
+
+  If SPMD enables, returns a virtual device that wraps all devices available
+  to this process.
+
+  Args:
+    index: index of the XLA device to be returned. Corresponds to index in
+      `torch_xla.devices()`.
+
+  Returns:
+    An XLA `torch.device`.
+  """
+
+  return xm.xla_device(index)
+
+
+def devices() -> List[torch.device]:
+  """Returns all devices available in the current process.
+
+  Returns:
+    A list of XLA `torch.devices`.
+  """
+
+  return [torch.device(d) for d in xm.get_xla_supported_devices()]
+
+
+def real_devices() -> List[str]:
+  """Returns local XLA device types and indices.
+
+  Returns:
+    A list strings representing the XLA devices available in the current
+    process, e.g. `['TPU:0', 'TPU:1', ...]`.
+  """
+
+  return torch_xla._XLAC._xla_real_devices()
+
+
+def device_count() -> int:
+  """Returns number of addressable devices in the current process."""
+  return len(real_devices())