Clarify xr.device_type() API and use them

test/ds/test_dynamic_shape_models.py

@@ -44,7 +44,7 @@ def forward(self, x):
 
 
 @unittest.skipIf(
-    xm.xla_device_hw(torch_xla.device()) != 'TPU',
+    xr.device_type() != 'TPU',
     f"The tests fail on CPU. See https://github.com/pytorch/xla/issues/4298 for more detail."
 )
 class TestDynamicShapeModels(unittest.TestCase):

test/pjrt/test_dynamic_plugin_tpu.py

@@ -20,7 +20,7 @@ def setUpClass(cls):
   @staticmethod
   def _assert_tpus_exist(index=0):
     del index
-    assert xm.xla_device_hw(torch_xla.device()) == 'TPU'
+    assert xr.device_type() == 'TPU'
 
   def test_single_process(self):
     with concurrent.futures.ProcessPoolExecutor(max_workers=1) as executor:

test/test_autocast.py

@@ -348,8 +348,7 @@ def compare(first, second):
     self.assertFalse(self.is_autocast_enabled())
 
 
-@unittest.skipIf(
-    xm.xla_device_hw(torch_xla.device()) != 'TPU', f"TPU autocast test.")
+@unittest.skipIf(xr.device_type() != 'TPU', f"TPU autocast test.")
 class TestAutocastTPU(TestAutocastBase):
 
   @classmethod
@@ -405,7 +404,7 @@ class TestOtherOps(unittest.TestCase):
 
   # On TPU, the input of batch norm is casted into fp32, see torch_xla/csrc/autocast_mode.cpp
   @unittest.skipIf(
-      xm.xla_device_hw(torch_xla.device()) != 'TPU',
+      xr.device_type() != 'TPU',
       "the behavior of batch_norm autocast on TPU is different from others")
   def test_batch_norm_tpu(self):
     device = torch_xla.device()

torch_xla/distributed/spmd/xla_sharding.py

@@ -334,7 +334,7 @@ def _get_physical_tpu_mesh(self, devices: np.ndarray) -> np.ndarray:
         A np.ndarray of device logical ordinals with shape [global_x, global_y, global_z]. On
           v2 and v3, global_z is instead cores_per_chip (i.e., 2).
     """
-    assert xm.xla_device_hw(torch_xla.device()) == 'TPU'
+    assert xr.device_type() == 'TPU'
     # coords is a 3-dims tuple representing the device in physical mesh
     device_coords = [self.device_attributes[d]['coords'] for d in devices]
     dims = tuple(d + 1 for d in max(device_coords))

torch_xla/runtime.py

@@ -82,12 +82,12 @@ def _maybe_select_default_device():
 
 
 def device_type() -> Optional[str]:
-  """Returns the current PjRt device type.
+  """Returns the current PJRT device type.
 
   Selects a default device if none has been configured
 
   Returns:
-    A string representation of the device.
+    A string representation of the PJRT device: "CPU", "TPU", etc.
   """
   pjrt_device = xu.getenv_as(xenv.PJRT_DEVICE, str)
   return pjrt_device.split('_')[0] if pjrt_device else pjrt_device