fix auto parallel mutigrpah bug

mindspore/ccsrc/frontend/parallel/step_parallel.cc

@@ -1269,15 +1269,17 @@ std::pair<AnfNodePtr, int> FindSubGraph(const FuncGraphPtr &graph, const AnfNode
   } else {
     AnfNodeIndexSet param_sub_set = manager->node_users()[parameter];
     for (auto &param_pair : param_sub_set) {
-      CNodePtr graph_cnode = param_pair.first->cast<CNodePtr>();
-      if ((graph_cnode == nullptr) || !graph_cnode->input(0)->isa<CNode>()) {
+      CNodePtr param_cnode = param_pair.first->cast<CNodePtr>();
+      AnfNodePtr graph_value_node;
+      if (param_cnode->input(0)->isa<CNode>()) {
+        graph_value_node = param_cnode->input(0)->cast<CNodePtr>()->input(1);
+      } else {
+        graph_value_node = param_cnode->input(0);
+      }
+      if (!IsValueNode<FuncGraph>(graph_value_node)) {
         continue;
       }
-      CNodePtr graph_cnode_inp0 = graph_cnode->input(0)->cast<CNodePtr>();
-      if (!IsValueNode<FuncGraph>(graph_cnode_inp0->input(1))) {
-        continue;
-      }
-      FuncGraphPtr graph_sub = GetValueNode<FuncGraphPtr>(graph_cnode_inp0->input(1));
+      FuncGraphPtr graph_sub = GetValueNode<FuncGraphPtr>(graph_value_node);
       auto parameters = graph_sub->parameters();
       if (IntToSize(param_pair.second - 1) >= parameters.size()) {
         MS_LOG(EXCEPTION) << "The index is out of range, index is " << param_pair.second - 1 << ", vector size is "
@@ -1864,7 +1866,8 @@ CNodePtr FindLossCNode(const FuncGraphPtr &func_graph) {
 
   // return -> make_tuple
   if (current_prim->name() == MAKE_TUPLE) {
-    MS_LOG(EXCEPTION) << "The loss have make_tuple, it is not supported";
+    MS_LOG(WARNING) << "The loss have make_tuple, it is not supported";
+    return nullptr;
   }
 
   // return -> loss
@@ -2069,6 +2072,12 @@ std::set<FuncGraphPtr> FindForwardGraphByRootNodes(const AnfNodeSet &root_all_no
       auto graph = GetValueNode<FuncGraphPtr>(cnode->input(1));
       MS_LOG(DEBUG) << "Find the forward graph success";
       graph_set.insert(graph);
+      auto manager = graph->manager();
+      MS_EXCEPTION_IF_NULL(manager);
+      auto graph_used = manager->func_graphs_used_total(graph);
+      for (auto &sub_graph : graph_used) {
+        graph_set.insert(sub_graph);
+      }
     }
   }
   return graph_set;
@@ -2423,7 +2432,7 @@ void HandleRootReshape(const std::vector<AnfNodePtr> &all_nodes) {
 void MarkForwardCNode(const FuncGraphPtr &root) {
   MS_EXCEPTION_IF_NULL(root);
   auto all_nodes = root->nodes();
-  std::set<FuncGraphPtr> graph_set = FindForwardGraphByRootNodes(all_nodes);
+  auto graph_set = FindForwardGraphByRootNodes(all_nodes);
 
   if (graph_set.empty()) {
     MS_LOG(INFO) << "Can not find the forward graph, so mark the ops in root graph";

mindspore/ccsrc/frontend/parallel/step_parallel.h

@@ -145,10 +145,10 @@ int32_t GetTupleGetItemIndex(const CNodePtr &cnode);
 
 Status ParallelInit();
 
-std::vector<std::string> ExtractInputsTensorName(const CNodePtr &node);
-
 std::set<FuncGraphPtr> ForwardGraph(const FuncGraphPtr &root);
 
+std::vector<std::string> ExtractInputsTensorName(const CNodePtr &node);
+
 bool AnfNodeIsPrimitive(const AnfNodePtr &anf_node, const std::string &prim_name);
 
 using RefKeyPair = std::pair<AnfNodePtr, std::vector<AnfNodePtr>>;

tests/ut/python/parallel/test_auto_parallel_multi_graph.py

@@ -0,0 +1,69 @@
+# 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 numpy as np
+import mindspore as ms
+import mindspore.context as context
+from mindspore.common.api import _executor
+from mindspore import Tensor, Parameter
+from mindspore.nn import Cell, TrainOneStepCell, Momentum
+from mindspore.ops import operations as P
+
+
+class TwoInputBprop(Cell):
+    def __init__(self):
+        super().__init__()
+        self.op = P.Mul()
+
+    def construct(self, x, y):
+        return self.op(x, y)
+
+    def bprop(self, x, y, out, dout):
+        return x * 5, y * 8
+
+
+class ParallelFloorDivBpropNet(Cell):
+    def __init__(self, mul_size, test_size, strategy=None, strategy2=None):
+        super().__init__()
+        mul_np = np.full(mul_size, 0.5, dtype=np.float32)
+        floordiv_np = np.full(test_size, 0.1, dtype=np.float32)
+        self.mul_weight = Parameter(Tensor(mul_np), name="mul_weight")
+        self.floordiv_weight = Parameter(Tensor(floordiv_np), name="floordiv_weight")
+        self.mul = TwoInputBprop()
+        self.floor_div = P.FloorDiv()
+        if strategy is not None:
+            self.mul.op.shard(strategy2)
+            self.floor_div.shard(strategy)
+
+    def construct(self, inputs, label):
+        x = self.mul(inputs, self.mul_weight)
+        x = self.floor_div(x, self.floordiv_weight)
+        return x
+
+
+inputs_ = Tensor(np.random.randn(128, 96).astype(np.float32), dtype=ms.float32)
+label_ = Tensor(np.random.randn(128, 96).astype(np.float32), dtype=ms.float32)
+def compile_net(net):
+    optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
+    train_net = TrainOneStepCell(net, optimizer)
+    train_net.set_auto_parallel()
+    _executor.compile(train_net, inputs_, label_)
+    context.reset_auto_parallel_context()
+
+
+def test_net():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=4, global_rank=0)
+    strategy = ((4, 1), (4, 1))
+    net = ParallelFloorDivBpropNet(mul_size=(128, 96), test_size=(128, 96), strategy=strategy, strategy2=strategy)
+    compile_net(net)