!1593 Fix the bug that there is only return node in the forward graph

Merge pull request !1593 from yangzhenzhang/reshape-optimized

mindspore/ccsrc/parallel/step_parallel.cc

@@ -1683,7 +1683,10 @@ CNodePtr FindLossCNode(const FuncGraphPtr &func_graph) {
   MS_EXCEPTION_IF_NULL(pre_node);
 
   auto pre_cnode = pre_node->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(pre_cnode);
+  if (pre_cnode == nullptr) {
+    return nullptr;
+  }
+
   auto current_prim = GetValueNode<PrimitivePtr>(pre_cnode->input(0));
   // return -> cast
   if (current_prim->name() == CAST && pre_cnode->operator_info() == nullptr) {
@@ -1907,21 +1910,6 @@ void StepSplitSens(const std::pair<CNodePtr, CNodePtr> &sens_loss_pair) {
   }
 }
 
-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;
-}
-
 // Sens node satisfies the following conditions: cnode(sens)-->cnode(tuple_getitem)-->cnode-->cnode(J)
 std::vector<std::pair<CNodePtr, CNodePtr>> GetSensLossPairs(const FuncGraphPtr &root) {
   MS_EXCEPTION_IF_NULL(root);
@@ -1968,6 +1956,10 @@ std::vector<std::pair<CNodePtr, CNodePtr>> GetSensLossPairs(const FuncGraphPtr &
     }
     auto func_graph = GetValueNode<FuncGraphPtr>(expect_j_cnode->input(1));
     auto loss_cnode = FindLossCNode(func_graph);
+    if (loss_cnode == nullptr) {
+      MS_LOG(WARNING) << "Can not find the loss cnode";
+      continue;
+    }
     std::pair<CNodePtr, CNodePtr> sens_loss_pair = std::make_pair(sens_cnode, loss_cnode);
     sens_loss_pairs.push_back(sens_loss_pair);
   }
@@ -2158,10 +2150,14 @@ std::set<FuncGraphPtr> ForwardGraph(const FuncGraphPtr &root) {
 
 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;
+  auto loss_cnode = FindLossCNode(graph);
+  if (loss_cnode == nullptr) {
+    MS_LOG(WARNING) << "Can not find the loss cnode";
+    return root_forward_nodes;
+  }
+
+  auto loss_cnode_id = loss_cnode->UniqueIdThroughCopy();
   for (auto &node : all_nodes) {
     MS_EXCEPTION_IF_NULL(node);
     if (!node->isa<CNode>()) {

mindspore/ccsrc/parallel/step_parallel.h

@@ -144,8 +144,6 @@ bool StepParallel(const FuncGraphPtr &func_graph, const opt::OptimizerPtr &optim
 
 int32_t GetTupleGetItemIndex(const CNodePtr &cnode);
 
-std::vector<CNodePtr> FindLossCNodeFromRoot(const FuncGraphPtr &root);
-
 Status ParallelInit();
 
 std::vector<std::string> ExtractInputsTensorName(const CNodePtr &node);

tests/ut/python/parallel/test_reshape_optimized.py

@@ -0,0 +1,54 @@
+# 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
+from mindspore import context, Tensor, Parameter
+from mindspore.common.api import _executor
+from mindspore.nn import Cell, TrainOneStepCell, Momentum
+from mindspore.ops import operations as P
+
+
+class Net(Cell):
+    def __init__(self, mul_weight):
+        super().__init__()
+        self.reshape1 = P.Reshape()
+        self.reshape2 = P.Reshape()
+        self.mul_weight = Parameter(mul_weight, "w1")
+
+    def construct(self, x, b):
+        out = self.reshape1(self.mul_weight, (128, 64, 32))
+        out = self.reshape2(out, (128, 64, 32))
+        return out
+
+
+_x = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_w1 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+
+
+def compile_net(net):
+    context.set_context(save_graphs=True)
+    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, _x, _b)
+    context.reset_auto_parallel_context()
+
+
+def test_reshape_optimized():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
+    net = Net(_w1)
+    compile_net(net)