Fix XPU inference

Though it will complain about "Device 0 is not recognized, available devices are integers(for GPU/XPU),
'mps', 'cpu' and 'disk'", but you cannot just put 0 as device, or it will treat 0 as CUDA device, then complains
again that torch is not compiled with CUDA enabled.

You will need safetensors >= 0.4.2 if using safetensors files.

src/accelerate/utils/modeling.py

@@ -367,6 +367,8 @@ def set_module_tensor_to_device(
         # `torch.Tensor.to(<int num>)` is not supported by `torch_npu` (see this [issue](https://github.com/Ascend/pytorch/issues/16)).
         if is_npu_available() and isinstance(device, int):
             device = f"npu:{device}"
+        if is_xpu_available() and isinstance(device, int):
+            device = f"xpu:{device}"
         if value is None:
             new_value = old_value.to(device)
             if dtype is not None and device in ["meta", torch.device("meta")]:
@@ -427,6 +429,8 @@ def set_module_tensor_to_device(
     # clean pre and post foward hook
     if is_npu_available():
         torch.npu.empty_cache()
+    elif is_xpu_available():
+        torch.xpu.empty_cache()
     else:
         torch.cuda.empty_cache()
 
@@ -1351,7 +1355,12 @@ def load_state_dict(checkpoint_file, device_map=None):
             else:
                 progress_bar = None
             for device in devices:
-                with safe_open(checkpoint_file, framework="pt", device=device) as f:
+                target_device = device
+
+                if is_xpu_available() and isinstance(device, int):
+                    target_device = f"xpu:{device}"
+
+                with safe_open(checkpoint_file, framework="pt", device=target_device) as f:
                     for key in device_weights[device]:
                         if progress_bar is not None:
                             progress_bar.set_postfix(dev=device, refresh=False)

src/accelerate/utils/operations.py

@@ -26,7 +26,7 @@
 from ..state import PartialState
 from .constants import TORCH_DISTRIBUTED_OPERATION_TYPES
 from .dataclasses import DistributedType, TensorInformation
-from .imports import is_npu_available, is_torch_distributed_available, is_torch_version, is_tpu_available
+from .imports import is_npu_available, is_torch_distributed_available, is_torch_version, is_tpu_available, is_xpu_available
 
 
 if is_tpu_available(check_device=False):
@@ -171,6 +171,8 @@ def send_to_device(tensor, device, non_blocking=False, skip_keys=None):
             # `torch.Tensor.to("npu")` could not find context when called for the first time (see this [issue](https://gitee.com/ascend/pytorch/issues/I8KECW?from=project-issue)).
             elif device == torch.device("npu"):
                 device = "npu:0"
+            elif is_xpu_available() and isinstance(device, int):
+                device = f"xpu:{device}"
         try:
             return tensor.to(device, non_blocking=non_blocking)
         except TypeError:  # .to() doesn't accept non_blocking as kwarg