refactor(mge/distributed): using nccl as default in distributed training

GitOrigin-RevId: 81268e84bc

imperative/python/megengine/distributed/group.py

@@ -104,7 +104,7 @@ class Group:
 WORLD = Group([])
 
 _devices = {"gpu", "cuda", "rocm"}
-_backends = {"nccl", "rccl", "ucx", "auto"}
+_backends = {"nccl", "rccl", "shm", "auto"}
 
 
 def init_process_group(

imperative/python/megengine/distributed/launcher.py

@@ -89,7 +89,7 @@ class launcher:
         master_ip="localhost",
         port=0,
         device_type="xpu",
-        backend="auto",
+        backend="nccl",
     ):
         self.func = func
         self.n_gpus = n_gpus if n_gpus is not None else get_device_count(device_type)

imperative/python/test/unit/utils/test_network_node.py

@@ -14,6 +14,10 @@ from megengine.core._imperative_rt.core2 import apply
 from megengine.core._wrap import Device
 from megengine.core.ops import builtin
 from megengine.device import get_device_count, is_cuda_available
+from megengine.functional.debug_param import (
+    get_execution_strategy,
+    set_execution_strategy,
+)
 from megengine.functional.external import tensorrt_runtime_opr
 from megengine.jit.tracing import trace
 from megengine.tensor import Tensor
@@ -106,10 +110,13 @@ def test_matmul():
     def fwd(data1, data2):
         return F.matmul(data1, data2)
 
+    old = get_execution_strategy()
+    set_execution_strategy("HEURISTIC_REPRODUCIBLE")
     data1 = Tensor(np.random.random((32, 64)))
     data2 = Tensor(np.random.random((64, 16)))
     result = fwd(data1, data2)
     check_pygraph_dump(fwd, [data1, data2], [result])
+    set_execution_strategy(old)
 
 
 def test_batchmatmul():