xpu: support xpu backend from stock pytorch (>=2.4)

Fixes: huggingface/transformers#31237

XPU backend is available in the stock PyTorch starting from
version 2.4, see [1]. This commit extends huggingface accelerate
to support XPU from both IPEX and the stock pytorch. IPEX is being
tried first.

See: pytorch/pytorch#114842
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>

src/accelerate/accelerator.py

@@ -78,7 +78,6 @@
     is_bf16_available,
     is_deepspeed_available,
     is_fp8_available,
-    is_ipex_available,
     is_lomo_available,
     is_megatron_lm_available,
     is_mlu_available,
@@ -473,6 +472,8 @@ def __init__(
                 self.scaler = torch.mlu.amp.GradScaler(**kwargs)
             elif is_npu_available():
                 self.scaler = torch.npu.amp.GradScaler(**kwargs)
+            elif is_xpu_available():
+                self.scaler = torch.amp.GradScaler('xpu', **kwargs)
             else:
                 self.scaler = torch.cuda.amp.GradScaler(**kwargs)
 
@@ -1284,9 +1285,9 @@ def prepare(self, *args, device_placement=None):
 
         if self.distributed_type in [DistributedType.MULTI_CPU, DistributedType.MULTI_XPU, DistributedType.NO]:
             if self.device.type == "cpu" and self.state.use_ipex:
-                args = self._prepare_ipex(*args)
+                args = self._prepare_xpu(*args)
             elif self.device.type == "xpu" and is_xpu_available():
-                args = self._prepare_ipex(*args)
+                args = self._prepare_xpu(*args)
         if self.distributed_type == DistributedType.DEEPSPEED:
             result = self._prepare_deepspeed(*args)
         elif self.distributed_type == DistributedType.MEGATRON_LM:
@@ -1887,14 +1888,11 @@ def _prepare_megatron_lm(self, *args):
             )
         return tuple(result)
 
-    def _prepare_ipex(self, *args):
-        if not is_ipex_available():
+    def _prepare_xpu(self, *args):
+        if not is_xpu_available():
             raise ImportError(
-                "IPEX is not installed or IPEX's version does not match current PyTorch version. Please refer"
-                " to https://github.com/intel/intel-extension-for-pytorch."
+                "XPU is not available neither from IPEX nor from stock PyTorch (>=2.4)"
             )
-        else:
-            import intel_extension_for_pytorch as ipex
 
         model = None
         optimizer = None
@@ -1907,13 +1905,13 @@ def _prepare_ipex(self, *args):
                 optimizer = obj
         if optimizer is not None and model is not None:
             dtype = torch.bfloat16 if self.state.mixed_precision == "bf16" else None
-            if self.device.type == "xpu" and is_xpu_available():
+            if self.device.type == "xpu":
                 model = model.to(self.device)
-                model, optimizer = torch.xpu.optimize(
-                    model, optimizer=optimizer, dtype=dtype, inplace=True, level="O1"
-                )
-            else:
-                model, optimizer = ipex.optimize(model, optimizer=optimizer, dtype=dtype, inplace=True, level="O1")
+                # torch.xpu.optimize is available only for xpu via IPEX
+                if hasattr(torch.xpu, "optimize"):
+                    model, optimizer = torch.xpu.optimize(
+                        model, optimizer=optimizer, dtype=dtype, inplace=True, level="O1"
+                    )
         for i in range(len(result)):
             if isinstance(result[i], torch.nn.Module):
                 result[i] = model

src/accelerate/utils/imports.py

@@ -374,16 +374,22 @@ def is_xpu_available(check_device=False):
     "check if user disables it explicitly"
     if not parse_flag_from_env("ACCELERATE_USE_XPU", default=True):
         return False
-    "Checks if `intel_extension_for_pytorch` is installed and potentially if a XPU is in the environment"
-    if is_ipex_available():
-        import torch
+    """
+    Checks if XPU acceleration is available either via `intel_extension_for_pytorch` or
+    via stock PyTorch (>=2.4) and potentially if a XPU is in the environment
+    """
+    if importlib.util.find_spec("torch") is None:
+        return False
 
+    import torch
+    if is_ipex_available():
         if is_torch_version("<=", "1.12"):
             return False
-    else:
-        return False
 
-    import intel_extension_for_pytorch  # noqa: F401
+        import intel_extension_for_pytorch  # noqa: F401
+    else:
+        if is_torch_version("<=", "2.3"):
+            return False
 
     if check_device:
         try: