Aligned to cuda accelerator

accelerator/xpu_accelerator.py

@@ -7,6 +7,7 @@
 from deepspeed.accelerator.abstract_accelerator import DeepSpeedAccelerator
 import intel_extension_for_pytorch as ipex  # noqa: F401 # type: ignore
 import oneccl_bindings_for_pytorch  # noqa: F401 # type: ignore
+import functools
 
 
 class XPU_Accelerator(DeepSpeedAccelerator):
@@ -191,31 +192,31 @@ def supported_dtypes(self):
 
     @property
     def BFloat16Tensor(self):
-        return torch.xpu.BFloat16Tensor
+        return functools.partial(torch.tensor, dtype=torch.bfloat16, device='xpu')
 
     @property
     def ByteTensor(self):
-        return torch.xpu.ByteTensor
+        return functools.partial(torch.tensor, dtype=torch.uint8, device='xpu')
 
     @property
     def DoubleTensor(self):
-        return torch.xpu.DoubleTensor
+        return functools.partial(torch.tensor, dtype=torch.double, device='xpu')
 
     @property
     def FloatTensor(self):
-        return torch.xpu.FloatTensor
+        return functools.partial(torch.tensor, dtype=torch.float, device='xpu')
 
     @property
     def HalfTensor(self):
-        return torch.xpu.HalfTensor
+        return functools.partial(torch.tensor, dtype=torch.half, device='xpu')
 
     @property
     def IntTensor(self):
-        return torch.xpu.IntTensor
+        return functools.partial(torch.tensor, dtype=torch.int, device='xpu')
 
     @property
     def LongTensor(self):
-        return torch.xpu.LongTensor
+        return functools.partial(torch.tensor, dtype=torch.long, device='xpu')
 
     def pin_memory(self, tensor, align_bytes=1):
         if align_bytes == 1: