Add the XPU device check for pipeline mode (#28326)

* Add the XPU check for pipeline mode

When setting xpu device for pipeline, It needs to use is_torch_xpu_available to load ipex and determine whether the device is available.
Signed-off-by: yuanwu <yuan.wu@intel.com>

* Don't move model to device when hf_device_map isn't None

1. Don't move model to device when hf_device_map is not None
2. The device string maybe includes the device index, so use 'in'instead of equal
Signed-off-by: yuanwu <yuan.wu@intel.com>

* Raise the error when xpu is not available
Signed-off-by: yuanwu <yuan.wu@intel.com>

* Update src/transformers/pipelines/base.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/pipelines/base.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Modify the error message
Signed-off-by: yuanwu <yuan.wu@intel.com>

* Change message format.
Signed-off-by: yuanwu <yuan.wu@intel.com>

---------
Signed-off-by: yuanwu <yuan.wu@intel.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

src/transformers/pipelines/base.py

@@ -34,7 +34,16 @@ from ..image_processing_utils import BaseImageProcessor
 from ..modelcard import ModelCard
 from ..models.auto.configuration_auto import AutoConfig
 from ..tokenization_utils import PreTrainedTokenizer
-from ..utils import ModelOutput, add_end_docstrings, infer_framework, is_tf_available, is_torch_available, logging
+from ..utils import (
+    ModelOutput,
+    add_end_docstrings,
+    infer_framework,
+    is_tf_available,
+    is_torch_available,
+    is_torch_cuda_available,
+    is_torch_xpu_available,
+    logging,
+)
 
 
 GenericTensor = Union[List["GenericTensor"], "torch.Tensor", "tf.Tensor"]
@@ -792,10 +801,6 @@ class Pipeline(_ScikitCompat):
                 "discard the `device` argument when creating your pipeline object."
             )
 
-        # We shouldn't call `model.to()` for models loaded with accelerate
-        if self.framework == "pt" and device is not None and not (isinstance(device, int) and device < 0):
-            self.model.to(device)
-
         if device is None:
             if hf_device_map is not None:
                 # Take the first device used by `accelerate`.
@@ -805,18 +810,35 @@ class Pipeline(_ScikitCompat):
 
         if is_torch_available() and self.framework == "pt":
             if isinstance(device, torch.device):
+                if device.type == "xpu" and not is_torch_xpu_available(check_device=True):
+                    raise ValueError(f'{device} is not available, you should use device="cpu" instead')
                 self.device = device
             elif isinstance(device, str):
+                if "xpu" in device and not is_torch_xpu_available(check_device=True):
+                    raise ValueError(f'{device} is not available, you should use device="cpu" instead')
                 self.device = torch.device(device)
             elif device < 0:
                 self.device = torch.device("cpu")
-            else:
+            elif is_torch_cuda_available():
                 self.device = torch.device(f"cuda:{device}")
+            elif is_torch_xpu_available(check_device=True):
+                self.device = torch.device(f"xpu:{device}")
+            else:
+                raise ValueError(f"{device} unrecognized or not available.")
         else:
             self.device = device if device is not None else -1
         self.torch_dtype = torch_dtype
         self.binary_output = binary_output
 
+        # We shouldn't call `model.to()` for models loaded with accelerate
+        if (
+            self.framework == "pt"
+            and self.device is not None
+            and not (isinstance(self.device, int) and self.device < 0)
+            and hf_device_map is None
+        ):
+            self.model.to(self.device)
+
         # Update config and generation_config with task specific parameters
         task_specific_params = self.model.config.task_specific_params
         if task_specific_params is not None and task in task_specific_params: