add_no_elimilate_for_comm_op

mindspore/ops/operations/comm_ops.py

@@ -155,6 +155,7 @@ class AllReduce(PrimitiveWithInfer):
         self.add_prim_attr('group', _get_group(group))
         self.add_prim_attr('fusion', 0)
         self.add_prim_attr('index', 0)
+        self.add_prim_attr('no_elimilate', True)
 
     def infer_shape(self, x_shape):
         return x_shape
@@ -228,6 +229,7 @@ class AllGather(PrimitiveWithInfer):
         self.add_prim_attr('group', _get_group(group))
         self.add_prim_attr('fusion', 0)
         self.add_prim_attr('mean_flag', False)
+        self.add_prim_attr('no_elimilate', True)
 
     def infer_shape(self, x_shape):
         validator.check_positive_int(len(x_shape), "x shape", self.name)
@@ -345,6 +347,7 @@ class _HostAllGather(PrimitiveWithInfer):
             validator.check_value_type("rank_id", r, (int,), self.name)
         self.group_size = len(group)
         self.add_prim_attr('group', group)
+        self.add_prim_attr('no_elimilate', True)
 
     def infer_shape(self, x_shape):
         validator.check_positive_int(len(x_shape), "x shape", self.name)
@@ -419,6 +422,7 @@ class ReduceScatter(PrimitiveWithInfer):
         self.add_prim_attr('rank_size', self.rank_size)
         self.add_prim_attr('group', _get_group(group))
         self.add_prim_attr('fusion', 0)
+        self.add_prim_attr('no_elimilate', True)
 
     def infer_shape(self, x_shape):
         if self.rank_size == 0:
@@ -472,6 +476,7 @@ class _HostReduceScatter(PrimitiveWithInfer):
         self.op = op
         self.group_size = len(group)
         self.add_prim_attr('group', group)
+        self.add_prim_attr('no_elimilate', True)
 
     def infer_shape(self, x_shape):
         if x_shape[0] % self.group_size != 0:
@@ -548,6 +553,7 @@ class Broadcast(PrimitiveWithInfer):
         validator.check_value_type('group', _get_group(group), (str,), self.name)
         check_hcom_group_valid(group)
         self.add_prim_attr('group', _get_group(group))
+        self.add_prim_attr('no_elimilate', True)
 
     def infer_shape(self, x_shape):
         return x_shape
@@ -594,6 +600,7 @@ class AllSwap(PrimitiveWithCheck):
         validator.check_value_type('group', _get_group(group), (str,), self.name)
         self.init_prim_io_names(inputs=['tensor_in', 'send_size', 'recv_size'], outputs=['tensor_out'])
         self.add_prim_attr('group', _get_group(group))
+        self.add_prim_attr('no_elimilate', True)
 
     def __check__(self, tensor_in, send_size, recv_size):
         validator.check_subclass("tensor_in", tensor_in['dtype'], mstype.tensor, self.name)
@@ -638,6 +645,7 @@ class NeighborExchange(Primitive):
         self.recv_shapes = recv_shapes
         self.send_shapes = send_shapes
         self.recv_type = recv_type
+        self.add_prim_attr('no_elimilate', True)
 
     def __call__(self, tensor):
         raise NotImplementedError
@@ -677,6 +685,7 @@ class AlltoAll(PrimitiveWithInfer):
         self.split_dim = split_dim
         self.concat_dim = concat_dim
         self.add_prim_attr('group', _get_group(group))
+        self.add_prim_attr('no_elimilate', True)
 
     def infer_shape(self, x_shape):
         rank_size = get_group_size(_get_group(self.group))

mindspore/parallel/_auto_parallel_context.py

@@ -107,6 +107,9 @@ class _AutoParallelContext:
             raise TypeError("The type of pipeline_stage_num must be int.")
         if stages < 1:
             raise ValueError("pipeline_stage_num can't be less than 1.")
+        backend = context.get_context("device_target")
+        if backend == "GPU" and stages > 1:
+            raise RuntimeError("Now GPU don't support pipeline parallel.")
         self.check_context_handle()
         self._context_handle.set_pipeline_stage_split_num(stages)
 

tests/ut/cpp/parallel/step_parallel_test.cc

@@ -320,6 +320,9 @@ TEST_F(TestStepParallel, CreatOpInstance) {
         } else if (name == "index") {
           parse::ConvertData(py::cast<py::object>(item.second), &converted_ret);
           ASSERT_EQ(converted_ret->ToString(), "0");
+        } else if (name == "no_elimilate") {
+          parse::ConvertData(py::cast<py::object>(item.second), &converted_ret);
+          ASSERT_EQ(converted_ret->ToString(), "true");
         } else {
           MS_LOG(EXCEPTION) << "Test failed";
         }