add xpu support

src/lightning/fabric/accelerators/__init__.py

@@ -16,7 +16,11 @@
 from lightning.fabric.accelerators.mps import MPSAccelerator  # noqa: F401
 from lightning.fabric.accelerators.registry import _AcceleratorRegistry, call_register_accelerators
 from lightning.fabric.accelerators.xla import XLAAccelerator  # noqa: F401
-
+from lightning.fabric.utilities.imports import _LIGHTNING_XPU_AVAILABLE
 _ACCELERATORS_BASE_MODULE = "lightning.fabric.accelerators"
 ACCELERATOR_REGISTRY = _AcceleratorRegistry()
 call_register_accelerators(ACCELERATOR_REGISTRY, _ACCELERATORS_BASE_MODULE)
+if _LIGHTNING_XPU_AVAILABLE:
+    if "xpu" not in ACCELERATOR_REGISTRY:
+        from lightning_xpu.fabric import XPUAccelerator
+        XPUAccelerator.register_accelerators(ACCELERATOR_REGISTRY)

src/lightning/fabric/cli.py

@@ -24,12 +24,15 @@
 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.device_parser import _parse_gpu_ids
+from lightning.fabric.utilities.imports import _LIGHTNING_XPU_AVAILABLE
 
 _log = logging.getLogger(__name__)
 
 _CLICK_AVAILABLE = RequirementCache("click")
 
-_SUPPORTED_ACCELERATORS = ("cpu", "gpu", "cuda", "mps", "tpu")
+_SUPPORTED_ACCELERATORS = ["cpu", "gpu", "cuda", "mps", "tpu"]
+if _LIGHTNING_XPU_AVAILABLE:
+    _SUPPORTED_ACCELERATORS.append("xpu")
 
 
 def _get_supported_strategies() -> List[str]:
@@ -146,13 +149,16 @@ 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 == "gpu":
-        parsed_devices = _parse_gpu_ids(devices, include_cuda=True, include_mps=True)
+        parsed_devices = _parse_gpu_ids(devices, include_cuda=True, include_mps=True, include_xpu=True)
     elif accelerator == "cuda":
         parsed_devices = CUDAAccelerator.parse_devices(devices)
     elif accelerator == "mps":
         parsed_devices = MPSAccelerator.parse_devices(devices)
     elif accelerator == "tpu":
         raise ValueError("Launching processes for TPU through the CLI is not supported.")
+    elif accelerator == "xpu":
+        from lightning_xpu.fabric import XPUAccelerator
+        parsed_devices = XPUAccelerator.parse_devices(devices)
     else:
         return CPUAccelerator.parse_devices(devices)
     return len(parsed_devices) if parsed_devices is not None else 0

src/lightning/fabric/connector.py

@@ -23,6 +23,7 @@
 from lightning.fabric.accelerators.cuda import CUDAAccelerator
 from lightning.fabric.accelerators.mps import MPSAccelerator
 from lightning.fabric.accelerators.xla import XLAAccelerator
+from lightning.fabric.utilities.imports import _LIGHTNING_XPU_AVAILABLE
 from lightning.fabric.plugins import (
     CheckpointIO,
     DeepSpeedPrecision,
@@ -288,6 +289,13 @@ def _check_config_and_set_final_flags(
                             f" but accelerator set to {self._accelerator_flag}, please choose one device type"
                         )
                     self._accelerator_flag = "cuda"
+                if self._strategy_flag.parallel_devices[0].type == "xpu":
+                    if self._accelerator_flag and self._accelerator_flag not in ("auto", "xpu", "gpu"):
+                        raise ValueError(
+                            f"GPU parallel_devices set through {self._strategy_flag.__class__.__name__} class,"
+                            f" but accelerator set to {self._accelerator_flag}, please choose one device type"
+                        )
+                    self._accelerator_flag = "xpu"
                 self._parallel_devices = self._strategy_flag.parallel_devices
 
     def _check_device_config_and_set_final_flags(self, devices: Union[List[int], str, int], num_nodes: int) -> None:
@@ -313,6 +321,11 @@ def _choose_auto_accelerator(self) -> str:
             return "mps"
         if CUDAAccelerator.is_available():
             return "cuda"
+        if _LIGHTNING_XPU_AVAILABLE:
+            from lightning_xpu.fabric import XPUAccelerator
+            if XPUAccelerator.is_available():
+                return "xpu"
+
         return "cpu"
 
     @staticmethod
@@ -321,6 +334,10 @@ def _choose_gpu_accelerator_backend() -> str:
             return "mps"
         if CUDAAccelerator.is_available():
             return "cuda"
+        if _LIGHTNING_XPU_AVAILABLE:
+            from lightning_xpu.fabric import XPUAccelerator
+            if XPUAccelerator.is_available():
+                return "xpu"
         raise RuntimeError("No supported gpu backend found!")
 
     def _set_parallel_devices_and_init_accelerator(self) -> None:
@@ -378,8 +395,14 @@ def _choose_strategy(self) -> Union[Strategy, str]:
             return "ddp"
         if len(self._parallel_devices) <= 1:
             # TODO: Change this once gpu accelerator was renamed to cuda accelerator
-            if isinstance(self._accelerator_flag, (CUDAAccelerator, MPSAccelerator)) or (
-                isinstance(self._accelerator_flag, str) and self._accelerator_flag in ("cuda", "gpu", "mps")
+            supported_accelerators = [CUDAAccelerator, MPSAccelerator]
+            supported_accelerators_str = ["cuda", "gpu", "mps"]
+            if _LIGHTNING_XPU_AVAILABLE:
+                from lightning_xpu.fabric import XPUAccelerator
+                supported_accelerators.append(XPUAccelerator)
+                supported_accelerators_str.append("xpu")
+            if isinstance(self._accelerator_flag, tuple(supported_accelerators)) or (
+                isinstance(self._accelerator_flag, str) and self._accelerator_flag in tuple(supported_accelerators_str)
             ):
                 device = _determine_root_gpu_device(self._parallel_devices)
             else:
@@ -462,7 +485,11 @@ def _check_and_init_precision(self) -> Precision:
                 if self._precision_input == "16-mixed"
                 else "Using bfloat16 Automatic Mixed Precision (AMP)"
             )
-            device = "cpu" if self._accelerator_flag == "cpu" else "cuda"
+            device = "cuda"
+            if self._accelerator_flag == "cpu":
+                device = "cpu"
+            elif self._accelerator_flag == "xpu":
+                device = "xpu"
 
             if isinstance(self.strategy, FSDPStrategy):
                 return FSDPPrecision(precision=self._precision_input, device=device)  # type: ignore[arg-type]

src/lightning/fabric/strategies/ddp.py

@@ -115,9 +115,12 @@ def setup_environment(self) -> None:
     def setup_module(self, module: Module) -> DistributedDataParallel:
         """Wraps the model into a :class:`~torch.nn.parallel.distributed.DistributedDataParallel` module."""
         device_ids = self._determine_ddp_device_ids()
-        # https://pytorch.org/docs/stable/notes/cuda.html#id5
-        ctx = torch.cuda.stream(torch.cuda.Stream()) if device_ids is not None else nullcontext()
-        with ctx:
+        if self.root_device.type == "cuda":
+            # https://pytorch.org/docs/stable/notes/cuda.html#id5
+            ctx = torch.cuda.stream(torch.cuda.Stream()) if device_ids is not None else nullcontext()
+            with ctx:
+                return DistributedDataParallel(module=module, device_ids=device_ids, **self._ddp_kwargs)
+        else:
             return DistributedDataParallel(module=module, device_ids=device_ids, **self._ddp_kwargs)
 
     def module_to_device(self, module: Module) -> None:

src/lightning/fabric/utilities/device_parser.py

@@ -17,6 +17,7 @@
 from lightning.fabric.plugins.environments.torchelastic import TorchElasticEnvironment
 from lightning.fabric.utilities.exceptions import MisconfigurationException
 from lightning.fabric.utilities.types import _DEVICE
+from lightning.fabric.utilities.imports import _LIGHTNING_XPU_AVAILABLE
 
 
 def _determine_root_gpu_device(gpus: List[_DEVICE]) -> Optional[_DEVICE]:
@@ -49,6 +50,7 @@ def _parse_gpu_ids(
     gpus: Optional[Union[int, str, List[int]]],
     include_cuda: bool = False,
     include_mps: bool = False,
+    include_xpu: bool = False,
 ) -> Optional[List[int]]:
     """
     Parses the GPU IDs given in the format as accepted by the
@@ -62,6 +64,7 @@ def _parse_gpu_ids(
             Any int N > 0 indicates that GPUs [0..N) should be used.
         include_cuda: A boolean value indicating whether to include CUDA devices for GPU parsing.
         include_mps: A boolean value indicating whether to include MPS devices for GPU parsing.
+        include_xpu: A boolean value indicating whether to include Intel GPU devices for GPU parsing.
 
     Returns:
         A list of GPUs to be used or ``None`` if no GPUs were requested
@@ -71,7 +74,7 @@ def _parse_gpu_ids(
             If no GPUs are available but the value of gpus variable indicates request for GPUs
 
     .. note::
-        ``include_cuda`` and ``include_mps`` default to ``False`` so that you only
+        ``include_cuda``, ``include_mps`` and ``include_xpu`` default to ``False`` so that you only
         have to specify which device type to use and all other devices are not disabled.
     """
     # Check that gpus param is None, Int, String or Sequence of Ints
@@ -84,22 +87,22 @@ def _parse_gpu_ids(
     # We know the user requested GPUs therefore if some of the
     # requested GPUs are not available an exception is thrown.
     gpus = _normalize_parse_gpu_string_input(gpus)
-    gpus = _normalize_parse_gpu_input_to_list(gpus, include_cuda=include_cuda, include_mps=include_mps)
+    gpus = _normalize_parse_gpu_input_to_list(gpus, include_cuda=include_cuda, include_mps=include_mps, include_xpu=include_xpu)
     if not gpus:
         raise MisconfigurationException("GPUs requested but none are available.")
 
     if (
         TorchElasticEnvironment.detect()
         and len(gpus) != 1
-        and len(_get_all_available_gpus(include_cuda=include_cuda, include_mps=include_mps)) == 1
+        and len(_get_all_available_gpus(include_cuda=include_cuda, include_mps=include_mps, include_xpu=include_xpu)) == 1
     ):
         # Omit sanity check on torchelastic because by default it shows one visible GPU per process
         return gpus
 
     # Check that GPUs are unique. Duplicate GPUs are not supported by the backend.
     _check_unique(gpus)
 
-    return _sanitize_gpu_ids(gpus, include_cuda=include_cuda, include_mps=include_mps)
+    return _sanitize_gpu_ids(gpus, include_cuda=include_cuda, include_mps=include_mps, include_xpu=include_xpu)
 
 
 def _normalize_parse_gpu_string_input(s: Union[int, str, List[int]]) -> Union[int, List[int]]:
@@ -112,7 +115,7 @@ def _normalize_parse_gpu_string_input(s: Union[int, str, List[int]]) -> Union[in
     return int(s.strip())
 
 
-def _sanitize_gpu_ids(gpus: List[int], include_cuda: bool = False, include_mps: bool = False) -> List[int]:
+def _sanitize_gpu_ids(gpus: List[int], include_cuda: bool = False, include_mps: bool = False, include_xpu: bool = False) -> List[int]:
     """Checks that each of the GPUs in the list is actually available. Raises a MisconfigurationException if any of
     the GPUs is not available.
 
@@ -126,9 +129,9 @@ def _sanitize_gpu_ids(gpus: List[int], include_cuda: bool = False, include_mps:
         MisconfigurationException:
             If machine has fewer available GPUs than requested.
     """
-    if sum((include_cuda, include_mps)) == 0:
+    if sum((include_cuda, include_mps, include_xpu)) == 0:
         raise ValueError("At least one gpu type should be specified!")
-    all_available_gpus = _get_all_available_gpus(include_cuda=include_cuda, include_mps=include_mps)
+    all_available_gpus = _get_all_available_gpus(include_cuda=include_cuda, include_mps=include_mps, include_xpu=include_xpu)
     for gpu in gpus:
         if gpu not in all_available_gpus:
             raise MisconfigurationException(
@@ -138,7 +141,7 @@ def _sanitize_gpu_ids(gpus: List[int], include_cuda: bool = False, include_mps:
 
 
 def _normalize_parse_gpu_input_to_list(
-    gpus: Union[int, List[int], Tuple[int, ...]], include_cuda: bool, include_mps: bool
+    gpus: Union[int, List[int], Tuple[int, ...]], include_cuda: bool, include_mps: bool, include_xpu: bool,
 ) -> Optional[List[int]]:
     assert gpus is not None
     if isinstance(gpus, (MutableSequence, tuple)):
@@ -148,19 +151,23 @@ def _normalize_parse_gpu_input_to_list(
     if not gpus:  # gpus==0
         return None
     if gpus == -1:
-        return _get_all_available_gpus(include_cuda=include_cuda, include_mps=include_mps)
+        return _get_all_available_gpus(include_cuda=include_cuda, include_mps=include_mps, include_xpu=include_xpu)
 
     return list(range(gpus))
 
 
-def _get_all_available_gpus(include_cuda: bool = False, include_mps: bool = False) -> List[int]:
+def _get_all_available_gpus(include_cuda: bool = False, include_mps: bool = False, include_xpu: bool = False) -> List[int]:
     """
     Returns:
         A list of all available GPUs
     """
     cuda_gpus = accelerators.cuda._get_all_visible_cuda_devices() if include_cuda else []
     mps_gpus = accelerators.mps._get_all_available_mps_gpus() if include_mps else []
-    return cuda_gpus + mps_gpus
+    xpu_gpus = []
+    if _LIGHTNING_XPU_AVAILABLE:
+        import lightning_xpu.fabric as accelerator_xpu
+        xpu_gpus += accelerator_xpu._get_all_visible_xpu_devices() if include_xpu else []
+    return cuda_gpus + mps_gpus + xpu_gpus
 
 
 def _check_unique(device_ids: List[int]) -> None:

src/lightning/fabric/utilities/distributed.py

@@ -217,7 +217,7 @@ def _init_dist_connection(
 
     Args:
         cluster_environment: ``ClusterEnvironment`` instance
-        torch_distributed_backend: Backend to use (includes `nccl` and `gloo`)
+        torch_distributed_backend: Backend to use (includes `nccl`, `gloo` and `ccl`)
         global_rank: Rank of the current process
         world_size: Number of processes in the group
         kwargs: Kwargs for ``init_process_group``
@@ -248,7 +248,12 @@ def _init_dist_connection(
 
 
 def _get_default_process_group_backend_for_device(device: torch.device) -> str:
-    return "nccl" if device.type == "cuda" else "gloo"
+    if device.type == "cuda":
+        return "nccl"
+    elif device.type == "xpu":
+        return "ccl"
+    else:
+        return "gloo"
 
 
 class _DatasetSamplerWrapper(Dataset):

src/lightning/fabric/utilities/imports.py

@@ -16,7 +16,7 @@
 import platform
 import sys
 
-from lightning_utilities.core.imports import compare_version
+from lightning_utilities.core.imports import compare_version, RequirementCache
 
 _IS_WINDOWS = platform.system() == "Windows"
 
@@ -25,8 +25,10 @@
 # 2. The inspection mode via `python -i`: https://stackoverflow.com/a/6879085/1162383
 _IS_INTERACTIVE = hasattr(sys, "ps1") or bool(sys.flags.interactive)
 
-_TORCH_GREATER_EQUAL_1_12 = compare_version("torch", operator.ge, "1.12.0")
-_TORCH_GREATER_EQUAL_1_13 = compare_version("torch", operator.ge, "1.13.0")
-_TORCH_GREATER_EQUAL_2_0 = compare_version("torch", operator.ge, "2.0.0")
+_TORCH_GREATER_EQUAL_1_12 = compare_version("torch", operator.ge, "1.12.0", use_base_version=True)
+_TORCH_GREATER_EQUAL_1_13 = compare_version("torch", operator.ge, "1.13.0", use_base_version=True)
+_TORCH_GREATER_EQUAL_2_0 = compare_version("torch", operator.ge, "2.0.0", use_base_version=True)
 _TORCH_GREATER_EQUAL_2_1 = compare_version("torch", operator.ge, "2.1.0", use_base_version=True)
 _TORCH_EQUAL_2_0 = _TORCH_GREATER_EQUAL_2_0 and not _TORCH_GREATER_EQUAL_2_1
+
+_LIGHTNING_XPU_AVAILABLE = RequirementCache("lightning-xpu")

src/lightning/pytorch/_graveyard/xpu.py

@@ -0,0 +1,34 @@
+import sys
+from typing import Any
+
+import lightning.pytorch as pl
+
+
+def _patch_sys_modules() -> None:
+    self = sys.modules[__name__]
+    sys.modules["lightning.pytorch.accelerators.xpu"] = self
+
+
+class XPUAccelerator:
+    auto_device_count = ...
+    get_parallel_devices = ...
+    is_available = ...
+    parse_devices = ...
+    setup_device = ...
+    teardown = ...
+
+    def __init__(self, *_: Any, **__: Any) -> None:
+        raise NotImplementedError(
+            "The `XPUAccelerator` class has been moved to an external package."
+            " Install the extension package as `pip install lightning-xpu`"
+            " and import with `from lightning_xpu import XPUAccelerator`."
+            " Please see: https://github.com/Lightning-AI/lightning-XPU for more details."
+        )
+
+
+def _patch_classes() -> None:
+    setattr(pl.accelerators, "XPUAccelerator", XPUAccelerator)
+
+
+_patch_sys_modules()
+_patch_classes()

src/lightning/pytorch/strategies/ddp.py

@@ -184,9 +184,12 @@ def _setup_model(self, model: Module) -> DistributedDataParallel:
         """Wraps the model into a :class:`~torch.nn.parallel.distributed.DistributedDataParallel` module."""
         device_ids = self.determine_ddp_device_ids()
         log.debug(f"setting up DDP model with device ids: {device_ids}, kwargs: {self._ddp_kwargs}")
-        # https://pytorch.org/docs/stable/notes/cuda.html#id5
-        ctx = torch.cuda.stream(torch.cuda.Stream()) if device_ids is not None else nullcontext()
-        with ctx:
+        if self.root_device.type == "cuda":
+            # https://pytorch.org/docs/stable/notes/cuda.html#id5
+            ctx = torch.cuda.stream(torch.cuda.Stream()) if device_ids is not None else nullcontext()
+            with ctx:
+                return DistributedDataParallel(module=model, device_ids=device_ids, **self._ddp_kwargs)
+        else:
             return DistributedDataParallel(module=model, device_ids=device_ids, **self._ddp_kwargs)
 
     def setup_distributed(self) -> None: