Fabric: Enable auto for devices and accelerator cli` arguments

src/lightning/fabric/cli.py

@@ -25,7 +25,7 @@
 from lightning.fabric.plugins.precision.precision import _PRECISION_INPUT_STR, _PRECISION_INPUT_STR_ALIAS
 from lightning.fabric.strategies import STRATEGY_REGISTRY
 from lightning.fabric.utilities.consolidate_checkpoint import _process_cli_args
-from lightning.fabric.utilities.device_parser import _parse_gpu_ids
+from lightning.fabric.utilities.device_parser import _parse_gpu_ids, _select_auto_accelerator_fabric
 from lightning.fabric.utilities.distributed import _suggested_max_num_threads
 from lightning.fabric.utilities.load import _load_distributed_checkpoint
 
@@ -34,7 +34,7 @@
 _CLICK_AVAILABLE = RequirementCache("click")
 _LIGHTNING_SDK_AVAILABLE = RequirementCache("lightning_sdk")
 
-_SUPPORTED_ACCELERATORS = ("cpu", "gpu", "cuda", "mps", "tpu")
+_SUPPORTED_ACCELERATORS = ("cpu", "gpu", "cuda", "mps", "tpu", "auto")
 
 
 def _get_supported_strategies() -> list[str]:
@@ -187,6 +187,14 @@ def _set_env_variables(args: Namespace) -> None:
 
 def _get_num_processes(accelerator: str, devices: str) -> int:
     """Parse the `devices` argument to determine how many processes need to be launched on the current machine."""
+
+    if accelerator == "auto" or accelerator is None:
+        accelerator = _select_auto_accelerator_fabric()
+    if devices == "auto":
+        if accelerator == "cuda" or accelerator == "mps" or accelerator == "cpu":
+            devices = "1"
+        else:
+            raise ValueError(f"Cannot default to '1' device for accelerator='{accelerator}'")
     if accelerator == "gpu":
         parsed_devices = _parse_gpu_ids(devices, include_cuda=True, include_mps=True)
     elif accelerator == "cuda":

src/lightning/fabric/utilities/device_parser.py

@@ -204,3 +204,18 @@ def _check_data_type(device_ids: object) -> None:
                 raise TypeError(f"{msg} a sequence of {type(id_).__name__}.")
     elif type(device_ids) not in (int, str):
         raise TypeError(f"{msg} {device_ids!r}.")
+
+
+def _select_auto_accelerator_fabric() -> str:
+    """Choose the accelerator type (str) based on availability."""
+    from lightning.fabric.accelerators.cuda import CUDAAccelerator
+    from lightning.fabric.accelerators.mps import MPSAccelerator
+    from lightning.fabric.accelerators.xla import XLAAccelerator
+
+    if XLAAccelerator.is_available():
+        return "tpu"
+    if MPSAccelerator.is_available():
+        return "mps"
+    if CUDAAccelerator.is_available():
+        return "cuda"
+    return "cpu"

src/lightning/pytorch/trainer/connectors/accelerator_connector.py

@@ -29,7 +29,7 @@
     SLURMEnvironment,
     TorchElasticEnvironment,
 )
-from lightning.fabric.utilities.device_parser import _determine_root_gpu_device
+from lightning.fabric.utilities.device_parser import _determine_root_gpu_device, _select_auto_accelerator_fabric
 from lightning.fabric.utilities.imports import _IS_INTERACTIVE
 from lightning.pytorch.accelerators import AcceleratorRegistry
 from lightning.pytorch.accelerators.accelerator import Accelerator
@@ -332,18 +332,12 @@ def _check_device_config_and_set_final_flags(self, devices: Union[list[int], str
     @staticmethod
     def _choose_auto_accelerator() -> str:
         """Choose the accelerator type (str) based on availability."""
-        if XLAAccelerator.is_available():
-            return "tpu"
         if _habana_available_and_importable():
             from lightning_habana import HPUAccelerator
 
             if HPUAccelerator.is_available():
                 return "hpu"
-        if MPSAccelerator.is_available():
-            return "mps"
-        if CUDAAccelerator.is_available():
-            return "cuda"
-        return "cpu"
+        return _select_auto_accelerator_fabric()
 
     @staticmethod
     def _choose_gpu_accelerator_backend() -> str:

tests/tests_fabric/test_cli.py

@@ -46,7 +46,7 @@ def test_run_env_vars_defaults(monkeypatch, fake_script):
     assert "LT_PRECISION" not in os.environ
 
 
-@pytest.mark.parametrize("accelerator", ["cpu", "gpu", "cuda", pytest.param("mps", marks=RunIf(mps=True))])
+@pytest.mark.parametrize("accelerator", ["cpu", "gpu", "cuda", "auto", pytest.param("mps", marks=RunIf(mps=True))])
 @mock.patch.dict(os.environ, os.environ.copy(), clear=True)
 @mock.patch("lightning.fabric.accelerators.cuda.num_cuda_devices", return_value=2)
 def test_run_env_vars_accelerator(_, accelerator, monkeypatch, fake_script):
@@ -85,7 +85,7 @@ def test_run_env_vars_unsupported_strategy(strategy, fake_script):
     assert f"Invalid value for '--strategy': '{strategy}'" in ioerr.getvalue()
 
 
-@pytest.mark.parametrize("devices", ["1", "2", "0,", "1,0", "-1"])
+@pytest.mark.parametrize("devices", ["1", "2", "0,", "1,0", "-1", "auto"])
 @mock.patch.dict(os.environ, os.environ.copy(), clear=True)
 @mock.patch("lightning.fabric.accelerators.cuda.num_cuda_devices", return_value=2)
 def test_run_env_vars_devices_cuda(_, devices, monkeypatch, fake_script):
@@ -97,7 +97,7 @@ def test_run_env_vars_devices_cuda(_, devices, monkeypatch, fake_script):
 
 
 @RunIf(mps=True)
-@pytest.mark.parametrize("accelerator", ["mps", "gpu"])
+@pytest.mark.parametrize("accelerator", ["mps", "gpu", "auto"])
 @mock.patch.dict(os.environ, os.environ.copy(), clear=True)
 def test_run_env_vars_devices_mps(accelerator, monkeypatch, fake_script):
     monkeypatch.setitem(sys.modules, "torch.distributed.run", Mock())