Revert "fix same node is used by two comm op"

This reverts commit b09d411dc4.

add AlltoAll and NeighborExchange as interface

mindspore/ccsrc/backend/optimizer/ascend/enhancer/insert_tensor_move_for_hccl_op.cc

@@ -41,18 +41,15 @@ bool IsParameterOrValueNode(const AnfNodePtr &node) {
 }
 
 // NodeUsersMap, for node B input i use node A, it will be one item in map with key: A, and value: (B, i)
-bool IsNodeOutPutUsedByOtherRealKernel(const AnfNodeIndexSet &node_users, const CNodePtr &known_user,
-                                       size_t known_index) {
+bool IsNodeOutPutUsedByOtherRealKernel(const AnfNodeIndexSet &node_users) {
   if (node_users.size() == 1) {
     MS_LOG(INFO) << "This node only used once, no need to insert tensormove node.";
     return false;
   }
   for (const auto &node_pair : node_users) {
-    auto &node = node_pair.first;
-    size_t idx = IntToSize(node_pair.second);
-    if (AnfAlgo::IsRealKernel(node) && !(known_user == node && known_index == idx)) {
-      MS_LOG(INFO) << "User " << node->DebugString() << " idx " << idx << " is real kernel and diff with known "
-                   << known_user->DebugString() << " idx " << known_index;
+    auto node = node_pair.first;
+    if (AnfAlgo::IsRealKernel(node) && !AnfAlgo::IsCommunicationOp(node)) {
+      MS_LOG(INFO) << "This node only used other real kernel: " << node->fullname_with_scope();
       return true;
     }
   }
@@ -61,13 +58,11 @@ bool IsNodeOutPutUsedByOtherRealKernel(const AnfNodeIndexSet &node_users, const
 }
 }  // namespace
 
-bool InsertTensorMoveForHcclOp::NeedInsertTensorMove(const FuncGraphPtr &graph, const CNodePtr &cur_node,
-                                                     size_t input_idx) const {
+bool InsertTensorMoveForHcclOp::NeedInsertTensorMove(const FuncGraphPtr &graph, const AnfNodePtr &input,
+                                                     const CNodePtr &cur_node) const {
   MS_EXCEPTION_IF_NULL(graph);
-  MS_EXCEPTION_IF_NULL(cur_node);
-  auto input = cur_node->input(input_idx);
   MS_EXCEPTION_IF_NULL(input);
-
+  MS_EXCEPTION_IF_NULL(cur_node);
   if (IsPrimitiveCNode(cur_node, prim::kPrimReceive)) {
     return false;
   }
@@ -99,7 +94,7 @@ bool InsertTensorMoveForHcclOp::NeedInsertTensorMove(const FuncGraphPtr &graph,
       MS_LOG(EXCEPTION) << "node has no output in manager"
                         << " trace: " << trace::DumpSourceLines(input);
     }
-    if (IsNodeOutPutUsedByOtherRealKernel(iter->second, cur_node, input_idx)) {
+    if (IsNodeOutPutUsedByOtherRealKernel(iter->second)) {
       return true;
     }
   }
@@ -113,7 +108,7 @@ void InsertTensorMoveForHcclOp::InsertTensorMove(const FuncGraphPtr &graph, cons
   std::vector<AnfNodePtr> new_inputs = {hccl_node->input(0)};
   for (size_t i = 1; i < hccl_node->size(); ++i) {
     auto input = hccl_node->input(i);
-    if (NeedInsertTensorMove(graph, hccl_node, i)) {
+    if (NeedInsertTensorMove(graph, input, hccl_node)) {
       auto tensor_move = CreateTensorMoveOp(graph, input);
       if (tensor_move == nullptr) {
         MS_LOG(EXCEPTION) << "Create tensor_move op failed.";

mindspore/ccsrc/backend/optimizer/ascend/enhancer/insert_tensor_move_for_hccl_op.h

@@ -32,7 +32,7 @@ class InsertTensorMoveForHcclOp : public PatternProcessPass {
 
  private:
   void InsertTensorMove(const FuncGraphPtr &graph, const CNodePtr &hccl_node) const;
-  bool NeedInsertTensorMove(const FuncGraphPtr &graph, const CNodePtr &cur_node, size_t input_idx) const;
+  bool NeedInsertTensorMove(const FuncGraphPtr &graph, const AnfNodePtr &input, const CNodePtr &cur_node) const;
   KernelQueryPtr kernel_query_;
 };
 }  // namespace opt

mindspore/ccsrc/backend/optimizer/ascend/mindir/all_to_all_unify_mindir.cc

@@ -119,6 +119,7 @@ CNodePtr CreateAllToAllvNode(const FuncGraphPtr &graph, const CNodePtr &all_to_a
   AnfAlgo::SetNodeAttr(kAttrSendRankIds, MakeValue<std::vector<int64_t>>(rank_ids), all_to_all_v);
   AnfAlgo::SetNodeAttr(kAttrRecvRankIds, MakeValue<std::vector<int64_t>>(rank_ids), all_to_all_v);
   AnfAlgo::SetNodeAttr(kAttrGroup, MakeValue<std::string>(group), all_to_all_v);
+  MS_LOG(INFO) << "Create AllToAllv success, split count " << split_count << ", rank size " << rank_size;
   return all_to_all_v;
 }
 

mindspore/ccsrc/frontend/parallel/ops_info/ops_utils.h

@@ -217,7 +217,7 @@ constexpr char DEVICE_NUM[] = "device_num";
 constexpr char VIRTUAL_DIV[] = "_VirtualDiv";
 constexpr char GET_TENSOR_SLICE[] = "_GetTensorSlice";
 constexpr char SPLIT[] = "Split";
-constexpr char ALL_TO_ALL[] = "_AlltoAll";
+constexpr char ALL_TO_ALL[] = "AlltoAll";
 constexpr char NEIGHBOREXCHANGE[] = "NeighborExchange";
 constexpr char PERMUTE_BY_AXIS[] = "PermuteByAxis";
 constexpr char CONCAT_BY_AXIS[] = "ConcatByAxis";

mindspore/core/base/core_ops.h

@@ -407,7 +407,7 @@ inline const PrimitivePtr kPrimSend = std::make_shared<Primitive>("Send");
 inline const PrimitivePtr kPrimReceive = std::make_shared<Primitive>("Receive");
 inline const PrimitivePtr kPrimAllReduce = std::make_shared<Primitive>("AllReduce");
 inline const PrimitivePtr kPrimNeighborExchange = std::make_shared<Primitive>("NeighborExchange");
-inline const PrimitivePtr kPrimAllToAll = std::make_shared<Primitive>("_AlltoAll");
+inline const PrimitivePtr kPrimAllToAll = std::make_shared<Primitive>("AlltoAll");
 inline const PrimitivePtr kPrimAllToAllv = std::make_shared<Primitive>("AllToAllv");
 inline const PrimitivePtr kPrimAllSwap = std::make_shared<Primitive>("AllSwap");
 inline const PrimitivePtr kPrimBroadcast = std::make_shared<Primitive>("Broadcast");

mindspore/ops/_grad/grad_comm_ops.py

@@ -22,8 +22,8 @@ from mindspore.parallel._utils import _get_enable_parallel_optimizer
 from .. import operations as P
 from ...common.tensor import RowTensor
 from ..composite.multitype_ops.zeros_like_impl import zeros_like
-from ..operations.comm_ops import (AllGather, _MiniStepAllGather, _HostAllGather, AllReduce, _AlltoAll, Broadcast,
-                                   _GetTensorSlice, _MirrorOperator, _MirrorMiniStepOperator, ReduceOp,
+from ..operations.comm_ops import (AllGather, _MiniStepAllGather, _HostAllGather, AllReduce, NeighborExchange, AlltoAll,
+                                   Broadcast, _GetTensorSlice, _MirrorOperator, _MirrorMiniStepOperator, ReduceOp,
                                    ReduceScatter, _HostReduceScatter, _VirtualDiv, _VirtualAdd, AllSwap,
                                    _VirtualAssignAdd, _VirtualAccuGrad, _MirrorMicroStepOperator, _MicroStepAllGather)
 from .grad_base import bprop_getters
@@ -357,10 +357,27 @@ def get_bprop_host_reduce_scatter(self):
     return bprop
 
 
-@bprop_getters.register(_AlltoAll)
+@bprop_getters.register(NeighborExchange)
+def get_bprop_neighborexchange(self):
+    """Generate bprop for NeighborExchange."""
+    group = self.group
+    send_rank_ids = self.recv_rank_ids
+    recv_rank_ids = self.send_rank_ids
+    recv_shapes = self.send_shapes
+    send_shapes = self.recv_shapes
+    recv_type = self.recv_type
+    neighborexchange_grad = NeighborExchange(send_rank_ids, recv_rank_ids, recv_shapes, send_shapes, recv_type, group)
+
+    def bprop(x, out, dout):
+        return (neighborexchange_grad(dout),)
+
+    return bprop
+
+
+@bprop_getters.register(AlltoAll)
 def get_bprop_all_to_all(self):
     """Generate bprop for AlltoAll."""
-    all_to_all_grad = _AlltoAll(self.split_count, self.concat_dim, self.split_dim, self.group)
+    all_to_all_grad = AlltoAll(self.split_count, self.concat_dim, self.split_dim, self.group)
     if self.instance_name:
         instance_name = "grad" + self.instance_name
         all_to_all_grad.set_prim_instance_name(instance_name)

mindspore/ops/_grad_experimental/__init__.py

@@ -18,7 +18,6 @@ from .._grad.grad_base import get_bprop_fn
 from . import grad_array_ops
 from . import grad_inner_ops
 from . import grad_nn_ops
-from . import grad_comm_ops
 from . import grad_math_ops
 
 __all__ = ['get_bprop_fn']

mindspore/ops/_grad_experimental/grad_comm_ops.py

@@ -1,35 +0,0 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-
-"""Generate bprop for comm ops"""
-from .._grad.grad_base import bprop_getters
-from ..operations._inner_ops import NeighborExchange
-
-
-@bprop_getters.register(NeighborExchange)
-def get_bprop_neighborexchange(self):
-    """Generate bprop for NeighborExchange."""
-    group = self.group
-    send_rank_ids = self.recv_rank_ids
-    recv_rank_ids = self.send_rank_ids
-    recv_shapes = self.send_shapes
-    send_shapes = self.recv_shapes
-    recv_type = self.recv_type
-    neighborexchange_grad = NeighborExchange(send_rank_ids, recv_rank_ids, recv_shapes, send_shapes, recv_type, group)
-
-    def bprop(x, out, dout):
-        return (neighborexchange_grad(dout),)
-
-    return bprop

mindspore/ops/bprop_mindir/ApproximateEqual_bprop.mindir

@@ -1,18 +1,19 @@
 
-0.1.0	MindSpore*1.4.1:ø	
-‡

+0.1.0	MindSpore*1.4.1:Š
 
-bprop.32:xbprop.32:[CNode]33bprop.32:[CNode]33"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op22
-‡

+‹

 
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+‹

+
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mindspore/ops/bprop_mindir/Argmax_bprop.mindir

@@ -1,12 +1,12 @@
 
-0.1.0	MindSpore*1.4.1:Ê
-†

+0.1.0	MindSpore*1.4.1:Ö
+Š

 
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mindspore/ops/bprop_mindir/Argmin_bprop.mindir

@@ -1,12 +1,12 @@
 
-0.1.0	MindSpore*1.4.1:Ë
-‡

+0.1.0	MindSpore*1.4.1:×
+‹

 
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mindspore/ops/bprop_mindir/AssignAdd_bprop.mindir

@@ -1,18 +1,19 @@
 
-0.1.0	MindSpore*1.4.1:ø	
-‡

+0.1.0	MindSpore*1.4.1:Š
 
-bprop.21:xbprop.21:[CNode]22bprop.21:[CNode]22"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op14
-‡

+‹

 
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mindspore/ops/bprop_mindir/AssignSub_bprop.mindir

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-0.1.0	MindSpore*1.4.1:ø	
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mindspore/ops/bprop_mindir/Assign_bprop.mindir

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-0.1.0	MindSpore*1.4.1:è
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mindspore/ops/bprop_mindir/BNTrainingReduce_bprop.mindir

@@ -1,12 +1,12 @@
 
-0.1.0	MindSpore*1.4.1:Ë
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+0.1.0	MindSpore*1.4.1:×
+‹

 
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mindspore/ops/bprop_mindir/Broadcast_bprop.mindir

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mindspore/ops/bprop_mindir/DropoutDoMask_bprop.mindir

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mindspore/ops/bprop_mindir/DropoutGenMask_bprop.mindir

@@ -1,15 +1,15 @@
 
-0.1.0	MindSpore*1.4.1:�
+0.1.0	MindSpore*1.4.1:¢
 
-‹

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+“

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mindspore/ops/bprop_mindir/Equal_bprop.mindir

@@ -1,18 +1,19 @@
 
-0.1.0	MindSpore*1.4.1:ø	
-‡

+0.1.0	MindSpore*1.4.1:Š
 
-bprop.70:xbprop.70:[CNode]71bprop.70:[CNode]71"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op50
-‡

+‹

 
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+‹

+
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+‰

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bprop.70:dout2
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mindspore/ops/bprop_mindir/GreaterEqual_bprop.mindir

@@ -1,18 +1,19 @@
 
-0.1.0	MindSpore*1.4.1:ø	
-‡

+0.1.0	MindSpore*1.4.1:Š
 
-bprop.82:xbprop.82:[CNode]83bprop.82:[CNode]83"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op59
-‡

+‹

 
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+‹

+
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+‰

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mindspore/ops/bprop_mindir/Greater_bprop.mindir

@@ -1,18 +1,19 @@
 
-0.1.0	MindSpore*1.4.1:ø	
-‡

+0.1.0	MindSpore*1.4.1:Š
 
-bprop.78:xbprop.78:[CNode]79bprop.78:[CNode]79"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op56
-‡

+‹

 
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+‹

+
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+‰

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bprop.78:dout2
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mindspore/ops/bprop_mindir/IOU_bprop.mindir

@@ -1,18 +1,19 @@
 
-0.1.0	MindSpore*1.4.1:ø	
-‡

+0.1.0	MindSpore*1.4.1:Š
 
-bprop.63:xbprop.63:[CNode]64bprop.63:[CNode]64"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op45
-‡

+‹

 
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+‹

+
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+‰

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bprop.63:dout2
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bprop.63:dout2
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mindspore/ops/bprop_mindir/Identity_bprop.mindir

@@ -1,8 +1,8 @@
 
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mindspore/ops/bprop_mindir/IsFinite_bprop.mindir

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-0.1.0	MindSpore*1.4.1:Ë
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+0.1.0	MindSpore*1.4.1:×
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mindspore/ops/bprop_mindir/LessEqual_bprop.mindir

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-0.1.0	MindSpore*1.4.1:ø	
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+‹

 
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bprop.90:dout2
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mindspore/ops/bprop_mindir/Less_bprop.mindir

@@ -1,18 +1,19 @@
 
-0.1.0	MindSpore*1.4.1:ø	
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+‹

 
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mindspore/ops/bprop_mindir/LinSpace_bprop.mindir

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mindspore/ops/bprop_mindir/LogicalAnd_bprop.mindir

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-0.1.0	MindSpore*1.4.1:ø	
-‡

+0.1.0	MindSpore*1.4.1:Š
 
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+‹

 
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mindspore/ops/bprop_mindir/LogicalNot_bprop.mindir

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mindspore/ops/bprop_mindir/LogicalOr_bprop.mindir

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mindspore/ops/bprop_mindir/NotEqual_bprop.mindir

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mindspore/ops/bprop_mindir/OneHot_bprop.mindir

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mindspore/ops/bprop_mindir/OnesLike_bprop.mindir

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mindspore/ops/bprop_mindir/Range_bprop.mindir

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mindspore/ops/bprop_mindir/ReLU_bprop.mindir

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mindspore/ops/bprop_mindir/ReduceAll_bprop.mindir

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mindspore/ops/bprop_mindir/ReduceAny_bprop.mindir

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mindspore/ops/bprop_mindir/Round_bprop.mindir

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mindspore/ops/bprop_mindir/Sign_bprop.mindir

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-bprop.36:[CNode]37bprop.36:[CNode]38bprop.36:[CNode]38"S-Prim-MakeTuple:Default/S-Prim-MakeTuple-op26bprop.36*
+bprop.36:xbprop.36:[CNode]37:1bprop.36:[CNode]37:1"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op25
+s
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+
bprop.36:dout2
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mindspore/ops/bprop_mindir/ZerosLike_bprop.mindir

@@ -1,12 +1,12 @@
 
-0.1.0	MindSpore*1.4.1:É
-†

+0.1.0	MindSpore*1.4.1:Õ
+Š

 
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-l
-bprop.10:[CNode]11bprop.10:[CNode]12bprop.10:[CNode]12"S-Prim-MakeTuple:Default/S-Prim-MakeTuple-op8bprop.10*
+bprop.10:xbprop.10:[CNode]11:1bprop.10:[CNode]11:1"!S-Prim-hyper_map[zeros_like_leaf]:-Default/S-Prim-hyper_map[zeros_like_leaf]-op7
+r
+bprop.10:[CNode]11:1bprop.10:[CNode]12:2bprop.10:[CNode]12:2"S-Prim-MakeTuple:Default/S-Prim-MakeTuple-op8bprop.10*
 
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+
bprop.10:dout2
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mindspore/ops/operations/__init__.py

@@ -35,7 +35,7 @@ from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Stack, Unpack, Unsta
                         BatchToSpace, SpaceToBatchND, BatchToSpaceND, BroadcastTo, InplaceUpdate, ReverseSequence,
                         EmbeddingLookup, Unique, GatherD, Identity, Range, MaskedFill, MaskedSelect, SearchSorted,
                         TensorScatterMax, TensorScatterMin, TensorScatterSub)
-from .comm_ops import (AllGather, AllReduce, _AlltoAll, AllSwap, ReduceScatter, Broadcast,
+from .comm_ops import (AllGather, AllReduce, NeighborExchange, AlltoAll, AllSwap, ReduceScatter, Broadcast,
                        _MirrorOperator, _MirrorMiniStepOperator, _MiniStepAllGather, ReduceOp, _VirtualDataset,
                        _VirtualOutput, _VirtualDiv, _GetTensorSlice, _VirtualAdd, _VirtualAssignAdd, _VirtualAccuGrad,
                        _HostAllGather, _HostReduceScatter, _MirrorMicroStepOperator, _MicroStepAllGather)

mindspore/ops/operations/_inner_ops.py

@@ -492,36 +492,6 @@ class Receive(PrimitiveWithInfer):
         return self.dtype
 
 
-class NeighborExchange(Primitive):
-    """
-    NeighborExchange is a collective operation.
-
-    NeighborExchange sends data from the local rank to ranks in the send_rank_ids,
-    as while receive data from recv_rank_ids.
-
-    Args:
-        send_rank_ids (list(int)): Ranks which the data is sent to.
-        recv_rank_ids (list(int)): Ranks which the data is received from.
-        recv_shapes (tuple(list(int))): Data shape which received from recv_rank_ids.
-        send_shapes (tuple(list(int))): Data shape which send to the send_rank_ids.
-        recv_type (type): Data type which received from recv_rank_ids
-        group (str):
-    """
-
-    @prim_attr_register
-    def __init__(self, send_rank_ids, recv_rank_ids, recv_shapes, send_shapes, recv_type,
-                 group=GlobalComm.WORLD_COMM_GROUP):
-        self.init_prim_io_names(inputs=['x'], outputs=['output'])
-        self.send_rank_ids = send_rank_ids
-        self.recv_rank_ids = recv_rank_ids
-        self.recv_shapes = recv_shapes
-        self.send_shapes = send_shapes
-        self.recv_type = recv_type
-
-    def __call__(self, tensor):
-        raise NotImplementedError
-
-
 class MatrixSetDiag(PrimitiveWithInfer):
     r"""
     Modifies the batched diagonal part of a batched tensor.

mindspore/ops/operations/comm_ops.py

@@ -20,7 +20,7 @@ from ..._checkparam import Validator as validator
 from ..._checkparam import Rel
 from ...communication.management import get_rank, get_group_size, GlobalComm, _get_group
 from ...common import dtype as mstype
-from ..primitive import PrimitiveWithInfer, PrimitiveWithCheck, prim_attr_register
+from ..primitive import PrimitiveWithInfer, PrimitiveWithCheck, Primitive, prim_attr_register
 from ...common.api import context
 
 
@@ -613,7 +613,37 @@ class AllSwap(PrimitiveWithCheck):
         return out
 
 
-class _AlltoAll(PrimitiveWithInfer):
+class NeighborExchange(Primitive):
+    """
+    NeighborExchange is a collective operation.
+
+    NeighborExchange sends data from the local rank to ranks in the send_rank_ids,
+    as while receive data from recv_rank_ids.
+
+    Args:
+        send_rank_ids (list(int)): Ranks which the data is sent to.
+        recv_rank_ids (list(int)): Ranks which the data is received from.
+        recv_shapes (tuple(list(int))): Data shape which received from recv_rank_ids.
+        send_shapes (tuple(list(int))): Data shape which send to the send_rank_ids.
+        recv_type (type): Data type which received from recv_rank_ids
+        group (str):
+    """
+
+    @prim_attr_register
+    def __init__(self, send_rank_ids, recv_rank_ids, recv_shapes, send_shapes, recv_type,
+                 group=GlobalComm.WORLD_COMM_GROUP):
+        self.init_prim_io_names(inputs=['x'], outputs=['output'])
+        self.send_rank_ids = send_rank_ids
+        self.recv_rank_ids = recv_rank_ids
+        self.recv_shapes = recv_shapes
+        self.send_shapes = send_shapes
+        self.recv_type = recv_type
+
+    def __call__(self, tensor):
+        raise NotImplementedError
+
+
+class AlltoAll(PrimitiveWithInfer):
     """
     AlltoAll is a collective operation.
 

tests/ut/cpp/python_input/gtest_input/pre_activate/all_to_all_unify_mindir_test.py

@@ -14,8 +14,7 @@
 # ============================================================================
 import mindspore as ms
 from mindspore import context
-from mindspore.ops.operations._inner_ops import NeighborExchange
-from mindspore.ops.operations.comm_ops import _AlltoAll
+from mindspore.ops.operations.comm_ops import AlltoAll, NeighborExchange
 from mindspore.communication.management import GlobalComm, init
 
 context.set_context(device_target="Ascend")
@@ -44,9 +43,9 @@ def test_neighbor_exchange(tag):
     return fns[tag]
 
 def test_all_to_all(tag):
-    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(device_num=2, global_rank=0)
     fns = FnDict()
-    altoall = _AlltoAll(split_count=8, split_dim=2, concat_dim=3)
+    altoall = AlltoAll(split_count=2, split_dim=2, concat_dim=3)
     @fns
     def before(x):
         return altoall(x)

tests/ut/python/communication/test_comm.py

@@ -25,7 +25,7 @@ from mindspore.nn import Dense
 from mindspore.nn import Momentum
 from mindspore.nn import ReLU
 from mindspore.nn import TrainOneStepCell, WithLossCell
-from mindspore.ops.operations.comm_ops import AllReduce, AllGather, _AlltoAll, ReduceOp, ReduceScatter
+from mindspore.ops.operations.comm_ops import AllReduce, AllGather, AlltoAll, ReduceOp, ReduceScatter
 from mindspore.ops.operations.comm_ops import Broadcast, AllSwap
 from mindspore.ops.operations.array_ops import Gather
 import mindspore
@@ -113,7 +113,7 @@ class AlltoAllNet(nn.Cell):
     def __init__(self, input_channel, out_channel):
         super(AlltoAllNet, self).__init__()
         self.dense = Dense(input_channel, out_channel)
-        self.alltoall = _AlltoAll(1, 0, 1)
+        self.alltoall = AlltoAll(1, 0, 1)
         self.relu = ReLU()
 
     def construct(self, x):

tests/ut/python/parallel/test_alltoall.py

@@ -25,7 +25,7 @@ from mindspore.common.parameter import Parameter
 from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
 from mindspore.nn.optim.momentum import Momentum
 from mindspore.ops import operations as P
-from mindspore.ops.operations.comm_ops import _AlltoAll
+from mindspore.ops.operations.comm_ops import AlltoAll
 from mindspore.parallel._utils import _reset_op_id
 from mindspore.train import Model
 from mindspore.context import ParallelMode
@@ -130,7 +130,7 @@ def test_all_to_all_success():
     class Net(nn.Cell):
         def __init__(self):
             super(Net, self).__init__()
-            self.alltoallv = _AlltoAll(split_count=8, split_dim=2, concat_dim=3)
+            self.alltoallv = AlltoAll(split_count=8, split_dim=2, concat_dim=3)
 
         def construct(self, x1):
             out = self.alltoallv(x1)
@@ -151,7 +151,7 @@ def test_all_to_all_invalid_split_count_value_failed():
     class Net(nn.Cell):
         def __init__(self):
             super(Net, self).__init__()
-            self.alltoallv = _AlltoAll(split_count=7, split_dim=2, concat_dim=3)
+            self.alltoallv = AlltoAll(split_count=7, split_dim=2, concat_dim=3)
 
         def construct(self, x1):
             out = self.alltoallv(x1)
@@ -173,7 +173,7 @@ def test_all_to_all_invalid_split_count_type_failed():
     class Net(nn.Cell):
         def __init__(self):
             super(Net, self).__init__()
-            self.alltoallv = _AlltoAll(split_count=[8], split_dim=2, concat_dim=3)
+            self.alltoallv = AlltoAll(split_count=[8], split_dim=2, concat_dim=3)
 
         def construct(self, x1):
             out = self.alltoallv(x1)
@@ -195,7 +195,7 @@ def test_all_to_all_invalid_split_dim_value_failed():
     class Net(nn.Cell):
         def __init__(self):
             super(Net, self).__init__()
-            self.alltoallv = _AlltoAll(split_count=8, split_dim=4, concat_dim=3)
+            self.alltoallv = AlltoAll(split_count=8, split_dim=4, concat_dim=3)
 
         def construct(self, x1):
             out = self.alltoallv(x1)
@@ -217,7 +217,7 @@ def test_all_to_all_invalid_split_dim_type_failed():
     class Net(nn.Cell):
         def __init__(self):
             super(Net, self).__init__()
-            self.alltoallv = _AlltoAll(split_count=8, split_dim=(3,), concat_dim=3)
+            self.alltoallv = AlltoAll(split_count=8, split_dim=(3,), concat_dim=3)
 
         def construct(self, x1):
             out = self.alltoallv(x1)
@@ -239,7 +239,7 @@ def test_all_to_all_invalid_concat_dim_value_failed():
     class Net(nn.Cell):
         def __init__(self):
             super(Net, self).__init__()
-            self.alltoallv = _AlltoAll(split_count=8, split_dim=3, concat_dim=4)
+            self.alltoallv = AlltoAll(split_count=8, split_dim=3, concat_dim=4)
 
         def construct(self, x1):
             out = self.alltoallv(x1)
@@ -261,7 +261,7 @@ def test_all_to_all_invalid_concat_dim_type_failed():
     class Net(nn.Cell):
         def __init__(self):
             super(Net, self).__init__()
-            self.alltoallv = _AlltoAll(split_count=8, split_dim=3, concat_dim=([3],))
+            self.alltoallv = AlltoAll(split_count=8, split_dim=3, concat_dim=([3],))
 
         def construct(self, x1):
             out = self.alltoallv(x1)
@@ -283,7 +283,7 @@ def test_all_to_all_invalid_split_count_cannot_be_divisible_failed():
     class Net(nn.Cell):
         def __init__(self):
             super(Net, self).__init__()
-            self.alltoallv = _AlltoAll(split_count=3, split_dim=3, concat_dim=3)
+            self.alltoallv = AlltoAll(split_count=3, split_dim=3, concat_dim=3)
 
         def construct(self, x1):
             out = self.alltoallv(x1)
@@ -305,7 +305,7 @@ def test_all_to_all_invalid_group_type_failed():
     class Net(nn.Cell):
         def __init__(self):
             super(Net, self).__init__()
-            self.alltoallv = _AlltoAll(split_count=8, split_dim=3, concat_dim=3, group=3)
+            self.alltoallv = AlltoAll(split_count=8, split_dim=3, concat_dim=3, group=3)
 
         def construct(self, x1):
             out = self.alltoallv(x1)

tests/ut/python/parallel/test_neighborexchange.py

@@ -21,7 +21,7 @@ import mindspore.nn as nn
 from mindspore.common.api import _cell_graph_executor
 from mindspore.nn import TrainOneStepCell, Momentum
 from mindspore.ops import operations as P
-from mindspore.ops.operations._inner_ops import NeighborExchange
+from mindspore.ops.operations.comm_ops import NeighborExchange
 
 _w1 = Tensor(np.ones([32, 32]), dtype=ms.float32)
 _x1 = Tensor(np.ones([32, 16]), dtype=ms.float32)