support multiple subgraphs

mindspore/ccsrc/parallel/allreduce_fusion/allreduce_fusion.cc

@@ -399,7 +399,12 @@ Status AllreduceFusion::ProcessAllreduceFusion(const CNodePtr &ret) {
   ret_ = ret;
   root_graph_ = ret_->func_graph();
   MS_EXCEPTION_IF_NULL(root_graph_);
-  auto forward_graph = ForwardGraph(root_graph_);
+  auto graph_set = ForwardGraph(root_graph_);
+  if (graph_set.size() > 1) {
+    MS_LOG(WARNING) << "AllReduce fusion don't support multiple subgraphs now.";
+    return SUCCESS;
+  }
+  auto forward_graph = *(graph_set.begin());
   MS_EXCEPTION_IF_NULL(forward_graph);
   forward_ret_ = forward_graph->get_return();
   MS_EXCEPTION_IF_NULL(forward_ret_);

mindspore/ccsrc/parallel/step_parallel.cc

@@ -1607,72 +1607,79 @@ void ReshapeInit(const std::vector<AnfNodePtr> &all_nodes) {
   }
 }
 
-// Sens node satisfies the following conditions: cnode(sens)-->cnode(tuple_getitem)-->cnode-->cnode(J)
-bool IsGradSensNode(const AnfNodePtr &node) {
-  if (!node->isa<CNode>()) {
-    return false;
+CNodePtr FindLossCNode(const FuncGraphPtr &func_graph) {
+  MS_EXCEPTION_IF_NULL(func_graph);
+  CNodePtr return_node = func_graph->get_return();
+  MS_EXCEPTION_IF_NULL(return_node);
+  if (return_node->size() < 2) {
+    MS_LOG(EXCEPTION) << "Failure: " << return_node->ToString() << " size is smaller than 2";
   }
+  AnfNodePtr pre_node = return_node->input(1);
+  MS_EXCEPTION_IF_NULL(pre_node);
 
-  // cnode(sens)-->cnode(tuple_getitem)
-  auto cnode = node->cast<CNodePtr>();
-  AnfNodePtr expect_tuple_getitem = cnode->input(0);
-  MS_EXCEPTION_IF_NULL(expect_tuple_getitem);
-  if (!expect_tuple_getitem->isa<CNode>()) {
-    return false;
-  }
-  auto expect_tuple_getitem_cnode = expect_tuple_getitem->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(expect_tuple_getitem_cnode);
-  if (!IsValueNode<Primitive>(expect_tuple_getitem_cnode->input(0))) {
-    return false;
+  auto pre_cnode = pre_node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(pre_cnode);
+  auto current_prim = GetValueNode<PrimitivePtr>(pre_cnode->input(0));
+
+  // return -> cast
+  if (current_prim->name() == CAST && pre_cnode->operator_info() == nullptr) {
+    pre_cnode = pre_cnode->input(1)->cast<CNodePtr>();
+    MS_EXCEPTION_IF_NULL(pre_cnode);
+    current_prim = GetValueNode<PrimitivePtr>(pre_cnode->input(0));
   }
-  ValueNodePtr expect_tuple_getitem_value_node = expect_tuple_getitem_cnode->input(0)->cast<ValueNodePtr>();
-  MS_EXCEPTION_IF_NULL(expect_tuple_getitem_value_node);
-  PrimitivePtr expect_tuple_getitem_prim = expect_tuple_getitem_value_node->value()->cast<PrimitivePtr>();
-  MS_EXCEPTION_IF_NULL(expect_tuple_getitem_prim);
-  if (expect_tuple_getitem_prim->name() != TUPLE_GETITEM) {
-    return false;
+
+  // notice: the GetNext op has not input
+  if (INVALID_LOSS_OPS.find(current_prim->name()) != INVALID_LOSS_OPS.end()) {
+    MS_LOG(INFO) << "The loss is: " << current_prim->name();
+    return pre_cnode;
   }
 
-  // cnode(sens)-->cnode(tuple_getitem)-->cnode
-  AnfNodePtr expect_anonymous = expect_tuple_getitem_cnode->input(1);
-  MS_EXCEPTION_IF_NULL(expect_anonymous);
-  if (!expect_anonymous->isa<CNode>()) {
-    return false;
+  // size of common cnode is larger than 1
+  if (pre_cnode->size() < 2) {
+    MS_LOG(EXCEPTION) << pre_cnode->ToString() << " size( " << pre_cnode->inputs().size() << " ) is smaller than 2";
   }
 
-  // cnode(sens)-->cnode(tuple_getitem)-->cnode-->cnode(J)
-  auto expect_anonymous_cnode = expect_anonymous->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(expect_anonymous_cnode);
-  AnfNodePtr expect_j = expect_anonymous_cnode->input(0);
-  MS_EXCEPTION_IF_NULL(expect_j);
-  if (!expect_j->isa<CNode>()) {
-    return false;
+  // return -> tuple_getitem -> loss
+  if (current_prim->name() == TUPLE_GETITEM) {
+    AnfNodePtr pre_pre_node = pre_cnode->input(1);
+    MS_EXCEPTION_IF_NULL(pre_pre_node);
+
+    auto pre_pre_cnode = pre_pre_node->cast<CNodePtr>();
+    auto value = pre_pre_cnode->input(0)->cast<ValueNodePtr>();
+    MS_EXCEPTION_IF_NULL(value);
+    PrimitivePtr prim = value->value()->cast<PrimitivePtr>();
+    MS_EXCEPTION_IF_NULL(prim);
+    MS_LOG(DEBUG) << "The loss name is " << prim->name();
+    return pre_pre_cnode;
   }
-  auto expect_j_cnode = expect_j->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(expect_j_cnode);
-  if (!IsValueNode<Primitive>(expect_j_cnode->input(0))) {
-    return false;
+
+  // return -> make_tuple
+  if (current_prim->name() == MAKE_TUPLE) {
+    MS_LOG(EXCEPTION) << "The loss have make_tuple, it is not supported";
   }
-  ValueNodePtr expect_j_value_node = expect_j_cnode->input(0)->cast<ValueNodePtr>();
-  MS_EXCEPTION_IF_NULL(expect_j_value_node);
-  PrimitivePtr expect_j_prim = expect_j_value_node->value()->cast<PrimitivePtr>();
-  MS_EXCEPTION_IF_NULL(expect_j_prim);
-  return (expect_j_prim->name() == J);
+
+  // return -> loss
+  MS_LOG(DEBUG) << "The loss name is " << current_prim->name();
+  return pre_cnode;
 }
 
-TensorLayouts GetLossNodeGradOutputLayout(const CNodePtr &loss_cnode) {
+TensorLayouts GetLossNodeGradOutputLayout(const CNodePtr &cnode) {
+  MS_EXCEPTION_IF_NULL(cnode);
+  TensorLayouts ret;
+  if (!IsValueNode<FuncGraph>(cnode->input(1))) {
+    MS_LOG(EXCEPTION) << "Sens can't find the corresponding graph.";
+  }
+  auto func_graph = GetValueNode<FuncGraphPtr>(cnode->input(1));
+  auto loss_cnode = FindLossCNode(func_graph);
   MS_EXCEPTION_IF_NULL(loss_cnode);
   AnfNodePtr node = loss_cnode->cast<AnfNodePtr>();
   MS_EXCEPTION_IF_NULL(node);
 
   LossNodeInfo node_info = GetLossNodeInfo(node);
-
   ValueNodePtr prim_anf_node = loss_cnode->input(0)->cast<ValueNodePtr>();
   MS_EXCEPTION_IF_NULL(prim_anf_node);
   PrimitivePtr prim = prim_anf_node->value()->cast<PrimitivePtr>();
   MS_EXCEPTION_IF_NULL(prim);
-
-  TensorLayouts ret;
   if (INVALID_LOSS_OPS.find(prim->name()) != INVALID_LOSS_OPS.end()) {
     MS_LOG(WARNING) << "The loss name is: " << prim->name() << ", do nothing for split sens now";
     return ret;
@@ -1680,7 +1687,6 @@ TensorLayouts GetLossNodeGradOutputLayout(const CNodePtr &loss_cnode) {
 
   OperatorInfoPtr operator_info = loss_cnode->operator_info();
   MS_EXCEPTION_IF_NULL(operator_info);
-
   TensorInfo loss_grad_tensor_info;
   size_t op_output_size = operator_info->outputs_tensor_info().size();
   MS_LOG(INFO) << "The loss name is " << operator_info->name() << ", the has tuple item is  "
@@ -1805,6 +1811,100 @@ void HandleSpecialNode(const OperatorInfoPtr &distribute_operator, const CNodePt
   HandleDropoutNode(distribute_operator, cnode);
 }
 
+std::set<FuncGraphPtr> FindForwardGraphByRootNodes(const AnfNodeSet &root_all_nodes) {
+  // J->CNode->Graph
+  std::set<FuncGraphPtr> graph_set;
+  for (auto &node : root_all_nodes) {
+    MS_EXCEPTION_IF_NULL(node);
+    if (!node->isa<CNode>()) {
+      continue;
+    }
+
+    auto cnode = node->cast<CNodePtr>();
+    MS_EXCEPTION_IF_NULL(cnode);
+    if ((cnode->size() < 2) || !IsValueNode<Primitive>(cnode->input(0))) {
+      continue;
+    }
+    auto expect_j_prim = GetValueNode<PrimitivePtr>(cnode->input(0));
+    if (expect_j_prim->name() != J) {
+      continue;
+    }
+    if (IsValueNode<FuncGraph>(cnode->input(1))) {
+      auto graph = GetValueNode<FuncGraphPtr>(cnode->input(1));
+      MS_LOG(DEBUG) << "Find the forward graph success";
+      graph_set.insert(graph);
+    }
+  }
+  return graph_set;
+}
+
+// Sens node satisfies the following conditions: cnode(sens)-->cnode(tuple_getitem)-->cnode-->cnode(J)
+void StepSplitSens(const AnfNodePtr &node) {
+  if (!node->isa<CNode>()) {
+    return;
+  }
+
+  // cnode(sens)-->cnode(tuple_getitem)
+  auto cnode = node->cast<CNodePtr>();
+  AnfNodePtr expect_tuple_getitem = cnode->input(0);
+  MS_EXCEPTION_IF_NULL(expect_tuple_getitem);
+  if (!expect_tuple_getitem->isa<CNode>()) {
+    return;
+  }
+  auto expect_tuple_getitem_cnode = expect_tuple_getitem->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(expect_tuple_getitem_cnode);
+  if (!IsValueNode<Primitive>(expect_tuple_getitem_cnode->input(0))) {
+    return;
+  }
+  auto expect_tuple_getitem_prim = GetValueNode<PrimitivePtr>(expect_tuple_getitem_cnode->input(0));
+  if (expect_tuple_getitem_prim->name() != TUPLE_GETITEM) {
+    return;
+  }
+
+  // cnode(sens)-->cnode(tuple_getitem)-->cnode
+  AnfNodePtr expect_anonymous = expect_tuple_getitem_cnode->input(1);
+  MS_EXCEPTION_IF_NULL(expect_anonymous);
+  if (!expect_anonymous->isa<CNode>()) {
+    return;
+  }
+
+  // cnode(sens)-->cnode(tuple_getitem)-->cnode-->cnode(J)
+  auto expect_anonymous_cnode = expect_anonymous->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(expect_anonymous_cnode);
+  AnfNodePtr expect_j = expect_anonymous_cnode->input(0);
+  MS_EXCEPTION_IF_NULL(expect_j);
+  if (!expect_j->isa<CNode>()) {
+    return;
+  }
+  auto expect_j_cnode = expect_j->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(expect_j_cnode);
+  if (!IsValueNode<Primitive>(expect_j_cnode->input(0))) {
+    return;
+  }
+  auto expect_j_prim = GetValueNode<PrimitivePtr>(expect_j_cnode->input(0));
+  if (expect_j_prim->name() == J) {
+    auto loss_grad_layout = GetLossNodeGradOutputLayout(expect_j_cnode);
+    if (!loss_grad_layout.empty()) {
+      SplitSens(node, loss_grad_layout[0]);
+    }
+  }
+}
+
+std::vector<CNodePtr> FindLossCNodeFromRoot(const FuncGraphPtr &root) {
+  MS_EXCEPTION_IF_NULL(root);
+  AnfNodePtr root_return_node = root->get_return();
+  MS_EXCEPTION_IF_NULL(root_return_node);
+  std::vector<CNodePtr> loss_node;
+  const auto &all_nodes = root->nodes();
+  std::set<FuncGraphPtr> graph_set = FindForwardGraphByRootNodes(all_nodes);
+  if (graph_set.empty()) {
+    loss_node.push_back(FindLossCNode(root));
+  }
+  (void)std::transform(graph_set.begin(), graph_set.end(), std::back_inserter(loss_node),
+                       [](const FuncGraphPtr &graph) { return FindLossCNode(graph); });
+  return loss_node;
+}
+
 void ParallelCommunication(const FuncGraphPtr &root, const std::vector<AnfNodePtr> &all_nodes,
                            const FuncGraphManagerPtr &manager) {
   MS_EXCEPTION_IF_NULL(root);
@@ -1812,18 +1912,15 @@ void ParallelCommunication(const FuncGraphPtr &root, const std::vector<AnfNodePt
   TensorRedistribution tensor_redistribution;
   AnfNodePtr grad_sens_node = nullptr;
 
-  CNodePtr loss_cnode = FindLossCNodeFromRoot(root);
-  MS_EXCEPTION_IF_NULL(loss_cnode);
-  // get output layout of loss must before inserting the operators below
-  TensorLayouts loss_layout = GetLossNodeGradOutputLayout(loss_cnode);
-
+  std::vector<CNodePtr> loss_cnode = FindLossCNodeFromRoot(root);
+  // split sens must before inserting the operators.
   for (auto &node : all_nodes) {
-    // find sens node
-    if ((grad_sens_node == nullptr) && IsGradSensNode(node)) {
-      grad_sens_node = node;
-      MS_LOG(INFO) << "Find the sens node success";
-    }
+    // If the shape of grad-sens tensor is not [] or [1], use get tensor slice to handel it.
+    // If the type of sens node is not Tensor, it is unsupported now, do nothing default.
+    StepSplitSens(node);
+  }
 
+  for (auto &node : all_nodes) {
     MS_EXCEPTION_IF_NULL(node);
     if (node->isa<CNode>()) {
       auto cnode = node->cast<CNodePtr>();
@@ -1837,7 +1934,8 @@ void ParallelCommunication(const FuncGraphPtr &root, const std::vector<AnfNodePt
       }
 
       bool is_loss_cnode = false;
-      if (cnode == loss_cnode) {
+      auto iter = std::find(loss_cnode.begin(), loss_cnode.end(), cnode);
+      if (iter != loss_cnode.end()) {
         is_loss_cnode = true;
       }
       // insert forward ops
@@ -1857,12 +1955,6 @@ void ParallelCommunication(const FuncGraphPtr &root, const std::vector<AnfNodePt
       StepSplitTensor(node, manager);
     }
   }
-
-  // If the shape of grad-sens tensor is not [] or [1], use get tensor slice to handel it.
-  // If the type of sens node is not Tensor, it is unsupported now, do nothing default.
-  if (grad_sens_node && !loss_layout.empty()) {
-    SplitSens(grad_sens_node, loss_layout[0]);
-  }
 }
 
 namespace {
@@ -2003,134 +2095,57 @@ void SetForwardFlag(const AnfNodeSet &all_nodes) {
   }
 }
 
-CNodePtr FindLossCNode(const FuncGraphPtr &func_graph) {
-  MS_EXCEPTION_IF_NULL(func_graph);
-  CNodePtr return_node = func_graph->get_return();
-  MS_EXCEPTION_IF_NULL(return_node);
-  if (return_node->inputs().size() < 2) {
-    MS_LOG(EXCEPTION) << "Failure: " << return_node->ToString() << " size is smaller than 2";
-  }
-  AnfNodePtr pre_node = return_node->input(1);
-  MS_EXCEPTION_IF_NULL(pre_node);
-
-  auto pre_cnode = pre_node->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(pre_cnode);
-  auto current_value = pre_cnode->input(0)->cast<ValueNodePtr>();
-  MS_EXCEPTION_IF_NULL(current_value);
-  PrimitivePtr current_prim = current_value->value()->cast<PrimitivePtr>();
-  MS_EXCEPTION_IF_NULL(current_prim);
-
-  // return -> cast
-  if (current_prim->name() == CAST && pre_cnode->operator_info() == nullptr) {
-    pre_cnode = pre_cnode->input(1)->cast<CNodePtr>();
-    MS_EXCEPTION_IF_NULL(pre_cnode);
-    current_prim = GetValueNode<PrimitivePtr>(pre_cnode->input(0));
-  }
-
-  // notice: the GetNext op has not input
-  if (INVALID_LOSS_OPS.find(current_prim->name()) != INVALID_LOSS_OPS.end()) {
-    MS_LOG(INFO) << "The loss is: " << current_prim->name();
-    return pre_cnode;
-  }
-
-  // size of common cnode is larger than 1
-  if (pre_cnode->inputs().size() < 2) {
-    MS_LOG(EXCEPTION) << pre_cnode->ToString() << " size( " << pre_cnode->inputs().size() << " ) is smaller than 2";
-  }
-
-  // return -> tuple_getitem -> loss
-  if (current_prim->name() == TUPLE_GETITEM) {
-    AnfNodePtr pre_pre_node = pre_cnode->input(1);
-    MS_EXCEPTION_IF_NULL(pre_pre_node);
-
-    auto pre_pre_cnode = pre_pre_node->cast<CNodePtr>();
-    auto value = pre_pre_cnode->input(0)->cast<ValueNodePtr>();
-    MS_EXCEPTION_IF_NULL(value);
-    PrimitivePtr prim = value->value()->cast<PrimitivePtr>();
-    MS_EXCEPTION_IF_NULL(prim);
-    MS_LOG(INFO) << "The loss name is " << prim->name();
-    return pre_pre_cnode;
-  } else if (current_prim->name() == MAKE_TUPLE) {
-    MS_LOG(EXCEPTION) << "The loss have make_tuple, it is not supported";
-  }
-
-  // return -> loss
-  MS_LOG(INFO) << "The loss name is " << current_prim->name();
-  return pre_cnode;
+std::set<FuncGraphPtr> ForwardGraph(const FuncGraphPtr &root) {
+  MS_EXCEPTION_IF_NULL(root);
+  const auto &all_nodes = root->nodes();
+  std::set<FuncGraphPtr> graph_set = FindForwardGraphByRootNodes(all_nodes);
+  return graph_set;
 }
 
-FuncGraphPtr FindForwardGraphByRootNodes(const AnfNodeSet &root_all_nodes) {
-  for (auto &node : root_all_nodes) {
+std::vector<AnfNodePtr> FindRootForwardCNode(const FuncGraphPtr &graph, const AnfNodeSet &all_nodes) {
+  MS_EXCEPTION_IF_NULL(graph);
+  auto loss_cnode = FindLossCNode(graph);
+  MS_EXCEPTION_IF_NULL(loss_cnode);
+  auto loss_cnode_id = loss_cnode->UniqueIdThroughCopy();
+  std::vector<AnfNodePtr> root_forward_nodes;
+  for (auto &node : all_nodes) {
     MS_EXCEPTION_IF_NULL(node);
     if (!node->isa<CNode>()) {
       continue;
     }
-
     auto cnode = node->cast<CNodePtr>();
     MS_EXCEPTION_IF_NULL(cnode);
-    if ((cnode->inputs().size() < 2) || !IsValueNode<Primitive>(cnode->input(0))) {
-      continue;
-    }
-    ValueNodePtr expect_j_value_node = cnode->input(0)->cast<ValueNodePtr>();
-    MS_EXCEPTION_IF_NULL(expect_j_value_node);
-    PrimitivePtr expect_j_prim = expect_j_value_node->value()->cast<PrimitivePtr>();
-    MS_EXCEPTION_IF_NULL(expect_j_prim);
-    if (expect_j_prim->name() != J) {
-      continue;
-    }
-    MS_LOG(DEBUG) << "Find J prim: " << expect_j_value_node->DebugString() << ".";
-    if (IsValueNode<FuncGraph>(cnode->input(1))) {
-      auto graph = GetValueNode<FuncGraphPtr>(cnode->input(1));
-      MS_LOG(INFO) << "Find the forward graph success";
-      return graph;
+    auto root_node_id = node->UniqueIdThroughCopy();
+    if (loss_cnode_id == root_node_id) {
+      root_forward_nodes = DeepLinkedGraphSearch(cnode);
+      break;
     }
   }
-  return nullptr;
-}
-
-CNodePtr FindLossCNodeFromRoot(const FuncGraphPtr &root) {
-  MS_EXCEPTION_IF_NULL(root);
-  AnfNodePtr root_return_node = root->get_return();
-  MS_EXCEPTION_IF_NULL(root_return_node);
-  const auto &all_nodes = root->nodes();
-  FuncGraphPtr func_graph = FindForwardGraphByRootNodes(all_nodes);
-  if (func_graph == nullptr) {
-    return FindLossCNode(root);
-  } else {
-    return FindLossCNode(func_graph);
-  }
-}
-
-FuncGraphPtr ForwardGraph(const FuncGraphPtr &root) {
-  FuncGraphPtr forward_graph = root;
-  MS_EXCEPTION_IF_NULL(root);
-  AnfNodePtr root_return_node = root->get_return();
-  MS_EXCEPTION_IF_NULL(root_return_node);
-  const auto &all_nodes = root->nodes();
-  FuncGraphPtr func_graph = FindForwardGraphByRootNodes(all_nodes);
-  if (func_graph != nullptr) {
-    forward_graph = func_graph;
-  }
-  return forward_graph;
+  return root_forward_nodes;
 }
 
 void MarkForwardCNode(const FuncGraphPtr &root) {
   MS_EXCEPTION_IF_NULL(root);
-  AnfNodePtr root_return_node = root->get_return();
-  MS_EXCEPTION_IF_NULL(root_return_node);
-  auto &all_nodes = root->nodes();
-  FuncGraphPtr func_graph = FindForwardGraphByRootNodes(all_nodes);
+  auto all_nodes = root->nodes();
+  std::set<FuncGraphPtr> graph_set = FindForwardGraphByRootNodes(all_nodes);
 
-  if (func_graph == nullptr) {
-    // Can not find the forward graph, so the ops in root graph are forward.
+  if (graph_set.empty()) {
     MS_LOG(INFO) << "Can not find the forward graph, so mark the ops in root graph";
     SetForwardFlag(all_nodes);
   } else {
-    MS_LOG(INFO) << "The sub graph size of root is  " << root->func_graphs_used().size();
-    AnfNodePtr return_node = func_graph->get_return();
-    MS_EXCEPTION_IF_NULL(return_node);
-    std::vector<AnfNodePtr> all_dfs_nodes = DeepLinkedGraphSearch(return_node);
-    SetForwardFlag(all_dfs_nodes);
+    for (auto &func_graph : graph_set) {
+      MS_LOG(INFO) << "The sub graph size of root is " << root->func_graphs_used().size();
+      auto return_node = func_graph->get_return();
+      MS_EXCEPTION_IF_NULL(return_node);
+      auto all_dfs_nodes = DeepLinkedGraphSearch(return_node);
+      SetForwardFlag(all_dfs_nodes);
+      auto root_forward_nodes = FindRootForwardCNode(func_graph, all_nodes);
+      if (root_forward_nodes.empty()) {
+        continue;
+      }
+      // Mark forward flag for the nodes in root graph.
+      SetForwardFlag(root_forward_nodes);
+    }
   }
 }
 

mindspore/ccsrc/parallel/step_parallel.h

@@ -24,6 +24,7 @@
 #include <string>
 #include <unordered_map>
 #include <utility>
+#include <set>
 
 #include "./common.h"
 #include "optimizer/opt.h"
@@ -142,13 +143,13 @@ bool StepParallel(const FuncGraphPtr &func_graph, const opt::OptimizerPtr &optim
 
 int32_t GetTupleGetItemIndex(const CNodePtr &cnode);
 
-CNodePtr FindLossCNodeFromRoot(const FuncGraphPtr &root);
+std::vector<CNodePtr> FindLossCNodeFromRoot(const FuncGraphPtr &root);
 
 Status ParallelInit();
 
 std::vector<std::string> ExtractInputsTensorName(const CNodePtr &node);
 
-FuncGraphPtr ForwardGraph(const FuncGraphPtr &root);
+std::set<FuncGraphPtr> ForwardGraph(const FuncGraphPtr &root);
 }  // namespace parallel
 }  // namespace mindspore
 

tests/ut/python/parallel/test_semi_auto_two_subgraphs.py

@@ -0,0 +1,108 @@
+# Copyright 2020 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
+
+import mindspore as ms
+from mindspore import Tensor, Parameter, ParameterTuple, context
+from mindspore import nn
+from mindspore.common.api import _executor
+from mindspore.nn.optim import Adam, FTRL
+from mindspore.ops import operations as P
+from mindspore.ops import composite as C
+from mindspore.ops import functional as F
+import numpy as np
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.mul = P.Mul()
+        self.relu = P.ReLU()
+        self.param1 = Parameter(Tensor(np.ones([8, 8, 8, 8]).astype(np.float32)), name="wide")
+        self.param2 = Parameter(Tensor(np.ones([8, 8, 8, 8]).astype(np.float32)), name="deep")
+
+    def construct(self, x):
+        out = self.mul(x, self.param1)
+        out = self.mul(out, self.param2)
+        out = self.relu(out)
+        return out
+
+
+class NetWithLoss(nn.Cell):
+    def __init__(self, network):
+        super(NetWithLoss, self).__init__()
+        self.sum = P.ReduceSum(keep_dims=False).set_strategy(strategy=((4, 1, 1, 1),))
+        self.mean = P.ReduceMean(keep_dims=False).set_strategy(strategy=((8, 1, 1, 1),))
+        self.net = network
+
+    def construct(self, x):
+        net_out = self.net(x)
+        loss1 = self.sum(net_out, -1)
+        loss2 = self.mean(net_out, -1)
+        return loss1, loss2
+
+
+class IthOutputCell(nn.Cell):
+    def __init__(self, network, output_index):
+        super(IthOutputCell, self).__init__()
+        self.network = network
+        self.output_index = output_index
+
+    def construct(self, x1):
+        predict = self.network(x1)[self.output_index]
+        return predict
+
+
+class TrainStepWrap(nn.Cell):
+    def __init__(self, network, sens=1000.0):
+        super(TrainStepWrap, self).__init__()
+        self.network = network
+        self.network.set_train()
+        self.trainable_params = network.trainable_params()
+        weights_w = []
+        weights_d = []
+        for params in self.trainable_params:
+            weights_w.append(params)
+            weights_d.append(params)
+        
+        self.weights_w = ParameterTuple(weights_w)
+        self.weights_d = ParameterTuple(weights_d)
+        self.optimizer_w = FTRL(learning_rate=1e-2, params=self.weights_w,
+                                l1=1e-8, l2=1e-8, initial_accum=1.0)
+        self.optimizer_d = Adam(self.weights_d, learning_rate=3.5e-4, eps=1e-8,
+                                loss_scale=sens)
+        self.hyper_map = C.HyperMap()
+        self.grad_w = C.GradOperation('grad_w', get_by_list=True,
+                                      sens_param=True)
+        self.grad_d = C.GradOperation('grad_d', get_by_list=True,
+                                      sens_param=True)
+        self.sens = sens
+        self.loss_net_w = IthOutputCell(network, output_index=0)
+        self.loss_net_d = IthOutputCell(network, output_index=1)
+
+    def construct(self, x):
+        weights_w = self.weights_w
+        weights_d = self.weights_d
+        loss_w, loss_d = self.network(x)
+        sens_w = P.Fill()(P.DType()(loss_w), P.Shape()(loss_w), self.sens)
+        sens_d = P.Fill()(P.DType()(loss_d), P.Shape()(loss_d), self.sens)
+        grads_w = self.grad_w(self.loss_net_w, weights_w)(x, sens_w)
+        grads_d = self.grad_d(self.loss_net_d, weights_d)(x, sens_d)
+        return F.depend(loss_w, self.optimizer_w(grads_w)), F.depend(loss_d, self.optimizer_d(grads_d))
+
+
+def test_two_subgraphs():
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
+    net = TrainStepWrap(NetWithLoss(Net()))
+    input_x = Tensor(np.ones([8, 8, 8, 8]), dtype=ms.float32)
+    _executor.compile(net, input_x)