1. if Switch/SwitchLayer, do not replace Load or remove UpdateState; 2. add control flow testcases; 3. fix codedex problem

mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc

@@ -880,7 +880,7 @@ static std::vector<std::pair<CNodePtr, CNodePtr>> FindPrimalJPair(const FuncGrap
     } else if (IsPrimitive(cnode->inputs().at(0), prim::kPrimJ)) {
       // To find J user.
       auto j_user = GetJUser(node_user_map, cnode, index);
-      primal_j_pair.emplace_back(std::pair<CNodePtr, CNodePtr>(nullptr, j_user));
+      (void)primal_j_pair.emplace_back(std::pair<CNodePtr, CNodePtr>(nullptr, j_user));
     }
   }
 

mindspore/ccsrc/frontend/optimizer/auto_monad_eliminate.cc

@@ -85,6 +85,11 @@ std::vector<std::vector<size_t>> SplitGroup(const std::vector<AnfNodePtr> &topos
         if (IsPrimitiveCNode(node, prim::kPrimLoad)) {
           return false;
         }
+        // if Call/Switch/SwitchLayer, do not replace load.
+        if (IsPrimitiveCNode(node, prim::kPrimCall) || IsPrimitiveCNode(node, prim::kPrimSwitch) ||
+            IsPrimitiveCNode(node, prim::kPrimSwitchLayer)) {
+          return true;
+        }
         auto cnode = node->cast<CNodePtr>();
         auto &inputs = cnode->inputs();
         return std::any_of(inputs.begin(), inputs.end(),

mindspore/ccsrc/frontend/optimizer/irpass/updatestate_eliminate.cc

@@ -28,6 +28,7 @@ namespace {
 // data = Load(input, attach)
 // data = Depend(input, attach)
 // monad = UpdateState(input, attach)
+constexpr size_t kFirstInputIndex = 0;
 constexpr size_t kInputIndex = 1;
 constexpr size_t kAttachIndex = 2;
 constexpr size_t kMakeTupleSize = 3;
@@ -120,6 +121,13 @@ AnfNodePtr EliminateUpdateStateForPureNode(const CNodePtr &update_state, const A
       return nullptr;
     }
   }
+  // Skip Call/Switch/SwitchLayer.
+  auto first_input_node = cnode->input(kFirstInputIndex);
+  if (IsPrimitiveCNode(first_input_node, prim::kPrimCall) || IsPrimitiveCNode(first_input_node, prim::kPrimSwitch) ||
+      IsPrimitiveCNode(first_input_node, prim::kPrimSwitchLayer)) {
+    return nullptr;
+  }
+
   // Remove UpdateState by replace it with its input monad.
   return update_state->input(kInputIndex);
 }

mindspore/ccsrc/frontend/optimizer/opt.cc

@@ -246,7 +246,7 @@ void SubstitutionList::DisplayStatusOfSubstitution(const std::unordered_map<std:
      << std::endl;
   for (size_t i = 0; i < list_.size(); i++) {
     auto name = list_[i]->name_;
-    ss << std::left << std::setw(space + 4) << name << "\t";
+    ss << std::left << std::setw(SizeToInt(space) + 4) << name << "\t";
     for (auto change : status.at(name + std::to_string(i))) {
       ss << change << " ";
     }

mindspore/core/ir/tensor.cc

@@ -393,7 +393,7 @@ class TensorDataImpl : public TensorData {
         pos++;
       }
       size_t len = pos - index;
-      std::string space(max_width - len, ' ');
+      std::string space(max_width - SizeToInt(len), ' ');
       str = str.replace(index, len, space);
       index = str.find('#', index);
     }

tests/st/control/inner/test_030_for_in_if.py

@@ -170,3 +170,52 @@ def test_for_in_if_03():
 
     assert graph_forward_res == pynative_forward_res
     assert graph_backward_res == pynative_backward_res
+
+
+def test_for_in_if_04():
+    class ForInIfNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.param_a = Parameter(Tensor(5, mstype.int32), name='a')
+            self.param_b = Parameter(Tensor(4, mstype.int32), name='b')
+
+        def construct(self, x):
+            out = self.param_a
+            x = self.func(x)
+            out *= x
+            return out
+
+        def func(self, x):
+            if self.param_a > self.param_b:
+                for _ in range(0, 4):
+                    self.param_a += 1
+                    self.param_b -= 3
+            self.param_b += 10
+            return x
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor(5, mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    for_in_if_net = ForInIfNet()
+    net = GradNet(for_in_if_net)
+    graph_forward_res = for_in_if_net(x)
+    graph_backward_res = net(x)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    for_in_if_net = ForInIfNet()
+    net = GradNet(for_in_if_net)
+    pynative_forward_res = for_in_if_net(x)
+    pynative_backward_res = net(x)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_100_if_after_if.py

@@ -74,6 +74,25 @@ class IfAfterIfNet2(nn.Cell):
         return y
 
 
+class IfAfterIfNet3(nn.Cell):
+    def __init__(self):
+        super().__init__()
+        self.param_a = Parameter(Tensor(5, mstype.int32), name='a')
+        self.param_b = Parameter(Tensor(4, mstype.int32), name='b')
+
+    def construct(self, x, y):
+        out = x * y + self.func(self.param_b)
+        if self.param_a > self.param_b:
+            out += 5
+        return out
+
+    def func(self, x):
+        if self.param_a > self.param_b:
+            x += 5
+        self.param_b += 4
+        return x
+
+
 class GradNet(nn.Cell):
     def __init__(self, net):
         super(GradNet, self).__init__()
@@ -118,3 +137,9 @@ def test_if_after_if_02():
     x = Tensor(2, mstype.int32)
     y = Tensor(5, mstype.int32)
     control_flow_if_after_if(IfAfterIfNet2, x, y)
+
+
+def test_if_after_if_03():
+    x = Tensor(2, mstype.int32)
+    y = Tensor(5, mstype.int32)
+    control_flow_if_after_if(IfAfterIfNet3, x, y)