feat(utils): add support for Intel XPU backend

src/lerobot/utils/utils.py

@@ -45,6 +45,9 @@ def auto_select_torch_device() -> torch.device:
     elif torch.backends.mps.is_available():
         logging.info("Metal backend detected, using mps.")
         return torch.device("mps")
+    elif torch.xpu.is_available():
+        logging.info("Intel XPU backend detected, using xpu.")
+        return torch.device("xpu")
     else:
         logging.warning("No accelerated backend detected. Using default cpu, this will be slow.")
         return torch.device("cpu")
@@ -61,6 +64,9 @@ def get_safe_torch_device(try_device: str, log: bool = False) -> torch.device:
         case "mps":
             assert torch.backends.mps.is_available()
             device = torch.device("mps")
+        case "xpu":
+            assert torch.xpu.is_available()
+            device = torch.device("xpu")
         case "cpu":
             device = torch.device("cpu")
             if log:
@@ -81,6 +87,21 @@ def get_safe_dtype(dtype: torch.dtype, device: str | torch.device):
         device = device.type
     if device == "mps" and dtype == torch.float64:
         return torch.float32
+    if device == "xpu" and dtype == torch.float64:
+        if hasattr(torch.xpu, "get_device_capability"):
+            device_capability = torch.xpu.get_device_capability()
+            # NOTE: Some Intel XPU devices do not support double precision (FP64).
+            # The `has_fp64` flag is returned by `torch.xpu.get_device_capability()`
+            # when available; if False, we fall back to float32 for compatibility.
+            if not device_capability.get("has_fp64", False):
+                logging.warning(f"Device {device} does not support float64, using float32 instead.")
+                return torch.float32
+        else:
+            logging.warning(
+                f"Device {device} capability check failed. Assuming no support for float64, using float32 instead."
+            )
+            return torch.float32
+        return dtype
     else:
         return dtype
 
@@ -91,14 +112,16 @@ def is_torch_device_available(try_device: str) -> bool:
         return torch.cuda.is_available()
     elif try_device == "mps":
         return torch.backends.mps.is_available()
+    elif try_device == "xpu":
+        return torch.xpu.is_available()
     elif try_device == "cpu":
         return True
     else:
-        raise ValueError(f"Unknown device {try_device}. Supported devices are: cuda, mps or cpu.")
+        raise ValueError(f"Unknown device {try_device}. Supported devices are: cuda, mps, xpu or cpu.")
 
 
 def is_amp_available(device: str):
-    if device in ["cuda", "cpu"]:
+    if device in ["cuda", "xpu", "cpu"]:
         return True
     elif device == "mps":
         return False

tests/async_inference/test_helpers.py

@@ -389,7 +389,7 @@ def test_raw_observation_to_observation_device_handling():
     # Check that all expected keys produce tensors (device placement handled by preprocessor later)
     for key, value in observation.items():
         if isinstance(value, torch.Tensor):
-            assert value.device.type in ["cpu", "cuda", "mps"], f"Tensor {key} on unexpected device"
+            assert value.device.type in ["cpu", "cuda", "mps", "xpu"], f"Tensor {key} on unexpected device"
 
 
 def test_raw_observation_to_observation_deterministic():