Revert "[XPU] fix the dataloader problem in RDMA env (#54150)" (#55150)

This reverts commit 15c8752.

cmake/external/xpu.cmake

@@ -134,7 +134,8 @@ ExternalProject_Add(
     ${CMAKE_SOURCE_DIR}/tools/xpu/pack_paddle_depence.sh ${XPU_XRE_URL}
     ${XPU_XRE_DIR_NAME} ${XPU_XDNN_URL} ${XPU_XDNN_DIR_NAME} ${XPU_XCCL_URL}
     ${XPU_XCCL_DIR_NAME} && wget ${XPU_XFT_GET_DEPENCE_URL} && bash
-    get_xft_dependence.sh ${XPU_XFT_URL} ${XPU_XFT_DIR_NAME} && bash
+    get_xft_dependence.sh ${XPU_XFT_URL} ${XPU_XFT_DIR_NAME} &&
+    WITH_XPTI=${WITH_XPTI} bash
     ${CMAKE_SOURCE_DIR}/tools/xpu/get_xpti_dependence.sh ${XPU_XPTI_URL}
     ${XPU_XPTI_DIR_NAME}
   DOWNLOAD_NO_PROGRESS 1

python/paddle/io/dataloader/dataloader_iter.py

@@ -427,21 +427,7 @@ def __init__(self, loader):
         self._shutdown = False
 
     def _init_workers(self):
-        # NOTE(zhangxiaoci): When trained in XPU multi-node RDMA environment, an unexpected
-        # segmentfault will be raised in dataloader process, where the traceback goes all
-        # back to a runtime error that dataloader workers exit unexpectedly. Similar problems
-        # have been discussed that lead to a misbehavior of OpenCV working in multiprocessing
-        # environment. A possible solution is to change default 'fork' mode of multiprocessing
-        # start method to 'spawn'. See https://stackoverflow.com/questions/54013846 for details.
-        # NOTE(zhangxiaoci): Replace multiprocessing with multiprocess since in some training
-        # environments the former will raise 'AttributeError: Can't pickle local object xxx',
-        # which is a side effect of changing the default start method.
-        if paddle.is_compiled_with_xpu():
-            import multiprocess as multiprocessing
-
-            multiprocessing.set_start_method('spawn', force=True)
-        else:
-            from paddle.incubate import multiprocessing
+        from paddle.incubate import multiprocessing
 
         # multiprocess worker and indice queue list initial as empty
         self._workers = []