return empty string if accelerator is not available.

Author: GdoongMathew

src/lightning/pytorch/accelerators/mps.py

@@ -91,9 +91,9 @@ def register_accelerators(cls, accelerator_registry: _AcceleratorRegistry) -> No
     @override
     def device_name(cls, device: Optional[_DEVICE] = None) -> str:
         # todo: implement a better way to get the device name
-        available = cls.is_available()
-        gpu_type = " (mps)" if available else ""
-        return f"{available}{gpu_type}"
+        if not cls.is_available():
+            return ""
+        return "True (mps)"
 
 
 # device metrics

src/lightning/pytorch/accelerators/xla.py

@@ -60,7 +60,7 @@ def register_accelerators(cls, accelerator_registry: _AcceleratorRegistry) -> No
     def device_name(cls, device: Optional[_DEVICE] = None) -> str:
         is_available = cls.is_available()
         if not is_available:
-            return str(is_available)
+            return ""
 
         if _XLA_GREATER_EQUAL_2_1:
             from torch_xla._internal import tpu

src/lightning/pytorch/trainer/setup.py

@@ -156,7 +156,10 @@ def _log_device_info(trainer: "pl.Trainer") -> None:
     rank_zero_info(f"GPU available: {device_name}, using: {gpu_used} devices.")
 
     num_tpu_cores = trainer.num_devices if isinstance(trainer.accelerator, XLAAccelerator) else 0
-    rank_zero_info(f"TPU available: {XLAAccelerator.device_name()}, using: {num_tpu_cores} TPU cores")
+    rank_zero_info(
+        f"TPU available: {XLAAccelerator.device_name() if XLAAccelerator.is_available() else str(False)}, "
+        f"using: {num_tpu_cores} TPU cores"
+    )
 
     if _habana_available_and_importable():
         from lightning_habana import HPUAccelerator