support mp on xpu (#49531)

python/paddle/distributed/fleet/utils/hybrid_parallel_util.py

@@ -138,15 +138,23 @@ def _broadcast_data_help(data, shape, dtype, hcg):
 
 def broadcast_input_data(hcg, *inputs, **kwargs):
     cur_device = paddle.get_device()
+    dev = cur_device.split(":")[0]
+    assert dev in [
+        "xpu",
+        "gpu",
+        "npu",
+    ], f"Only support xpu, gpu and npu now, but this is {dev}"
+    dev_idx = int(cur_device.split(':')[1])
+    if dev == "gpu":
+        place = paddle.CUDAPlace(dev_idx)
+    else:
+        place = eval(f"paddle.{dev.upper()}Place")(dev_idx)
+
     for v in inputs:
         if isinstance(v, (core.VarBase, core.eager.Tensor)):
             with framework.no_grad():
-                if (
-                    "gpu" in cur_device
-                    and in_dygraph_mode()
-                    and not v.place.is_gpu_place()
-                ):
-                    v_gpu = v.cuda(int(cur_device.split(":")[1]))
+                if in_dygraph_mode() and not eval(f"v.place.is_{dev}_place")():
+                    v_gpu = v._copy_to(place, True)
                     v._clear_data()
                     v_gpu._share_buffer_to(v)
                 _broadcast_data_help(v, v.shape, v.dtype, hcg)
@@ -156,12 +164,8 @@ def broadcast_input_data(hcg, *inputs, **kwargs):
     for k, v in kwargs.items():
         if isinstance(v, (core.VarBase, core.eager.Tensor)):
             with framework.no_grad():
-                if (
-                    "gpu" in cur_device
-                    and in_dygraph_mode()
-                    and not v.place.is_gpu_place()
-                ):
-                    v_gpu = v.cuda(int(cur_device.split(":")[1]))
+                if in_dygraph_mode() and not eval(f"v.place.is_{dev}_place")():
+                    v_gpu = v._copy_to(place, True)
                     v._clear_data()
                     v_gpu._share_buffer_to(v)
                 _broadcast_data_help(v, v.shape, v.dtype, hcg)