fix get nullptr when use graph manager

mindspore/ccsrc/pre_activate/mem_reuse/mem_swap_manager.cc

@@ -65,6 +65,7 @@ void MemSwapManager::Init(const mindspore::session::KernelGraph *kernel_graph) {
 void MemSwapManager::SaveUserKernelTopoOrder(const mindspore::session::KernelGraph *kernel_graph) {
   MS_EXCEPTION_IF_NULL(kernel_graph);
   FuncGraphManagerPtr manager = kernel_graph->manager();
+  MS_EXCEPTION_IF_NULL(manager);
   NodeUsersMap user_map = manager->node_users();
   for (const auto &kernel : execution_order_) {
     auto iter = user_map.find(kernel);

mindspore/ccsrc/session/session_basic.cc

@@ -532,6 +532,7 @@ KernelGraphPtr SessionBasic::ConstructKernelGraph(const AnfNodePtrList &lst, con
   graph->set_output(ConstructOutput(outputs, graph));
   MS_EXCEPTION_IF_NULL(context_);
   FuncGraphManagerPtr manager = MakeManager({graph});
+  context_->AddManager(manager);
   if (manager) {
     manager->AddFuncGraph(graph);
     graph->set_manager(manager);

mindspore/ccsrc/session/session_context.h

@@ -37,8 +37,10 @@ class Context : public pipeline::ResourceBase {
 
   uint32_t device_id() const { return device_id_; }
   static std::shared_ptr<Context> GetInstance();
+  void AddManager(const FuncGraphManagerPtr &m) { manager_list_.push_back(m); }
 
  private:
+  std::vector<FuncGraphManagerPtr> manager_list_;
   std::string target_;
   uint32_t device_id_;
 };

tests/st/networks/test_gpu_resnet.py

@@ -179,7 +179,8 @@ class ResidualBlockWithDown(Cell):
         self.relu = P.ReLU()
         self.downSample = down_sample
 
-        self.conv_down_sample = conv1x1(in_channels, out_channels, stride=stride, padding=0)
+        self.conv_down_sample = conv1x1(
+            in_channels, out_channels, stride=stride, padding=0)
         self.bn_down_sample = bn_with_initialize(out_channels)
         self.add = TensorAdd()
 
@@ -210,7 +211,8 @@ class MakeLayer0(Cell):
 
     def __init__(self, block, layer_num, in_channels, out_channels, stride):
         super(MakeLayer0, self).__init__()
-        self.a = ResidualBlockWithDown(in_channels, out_channels, stride=1, down_sample=True)
+        self.a = ResidualBlockWithDown(
+            in_channels, out_channels, stride=1, down_sample=True)
         self.b = block(out_channels, out_channels, stride=stride)
         self.c = block(out_channels, out_channels, stride=1)
 
@@ -226,7 +228,8 @@ class MakeLayer1(Cell):
 
     def __init__(self, block, layer_num, in_channels, out_channels, stride):
         super(MakeLayer1, self).__init__()
-        self.a = ResidualBlockWithDown(in_channels, out_channels, stride=stride, down_sample=True)
+        self.a = ResidualBlockWithDown(
+            in_channels, out_channels, stride=stride, down_sample=True)
         self.b = block(out_channels, out_channels, stride=1)
         self.c = block(out_channels, out_channels, stride=1)
         self.d = block(out_channels, out_channels, stride=1)
@@ -244,7 +247,8 @@ class MakeLayer2(Cell):
 
     def __init__(self, block, layer_num, in_channels, out_channels, stride):
         super(MakeLayer2, self).__init__()
-        self.a = ResidualBlockWithDown(in_channels, out_channels, stride=stride, down_sample=True)
+        self.a = ResidualBlockWithDown(
+            in_channels, out_channels, stride=stride, down_sample=True)
         self.b = block(out_channels, out_channels, stride=1)
         self.c = block(out_channels, out_channels, stride=1)
         self.d = block(out_channels, out_channels, stride=1)
@@ -266,7 +270,8 @@ class MakeLayer3(Cell):
 
     def __init__(self, block, layer_num, in_channels, out_channels, stride):
         super(MakeLayer3, self).__init__()
-        self.a = ResidualBlockWithDown(in_channels, out_channels, stride=stride, down_sample=True)
+        self.a = ResidualBlockWithDown(
+            in_channels, out_channels, stride=stride, down_sample=True)
         self.b = block(out_channels, out_channels, stride=1)
         self.c = block(out_channels, out_channels, stride=1)
 
@@ -330,14 +335,41 @@ def test_trainTensor(num_classes=10, epoch=8, batch_size=1):
     net = resnet50(num_classes)
     lr = 0.1
     momentum = 0.9
-    optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, momentum)
+    optimizer = Momentum(filter(lambda x: x.requires_grad,
+                                net.get_parameters()), lr, momentum)
     criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
     net_with_criterion = WithLossCell(net, criterion)
-    train_network = TrainOneStepCell(net_with_criterion, optimizer)  # optimizer
+    train_network = TrainOneStepCell(
+        net_with_criterion, optimizer)  # optimizer
     train_network.set_train()
     losses = []
     for i in range(0, epoch):
-        data = Tensor(np.ones([batch_size, 3, 224, 224]).astype(np.float32) * 0.01)
+        data = Tensor(np.ones([batch_size, 3, 224, 224]
+                              ).astype(np.float32) * 0.01)
+        label = Tensor(np.ones([batch_size]).astype(np.int32))
+        loss = train_network(data, label)
+        losses.append(loss)
+    assert (losses[-1].asnumpy() < 1)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_trainTensor_big_batchSize(num_classes=10, epoch=8, batch_size=170):
+    net = resnet50(num_classes)
+    lr = 0.1
+    momentum = 0.9
+    optimizer = Momentum(filter(lambda x: x.requires_grad,
+                                net.get_parameters()), lr, momentum)
+    criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
+    net_with_criterion = WithLossCell(net, criterion)
+    train_network = TrainOneStepCell(
+        net_with_criterion, optimizer)  # optimizer
+    train_network.set_train()
+    losses = []
+    for i in range(0, epoch):
+        data = Tensor(np.ones([batch_size, 3, 224, 224]
+                              ).astype(np.float32) * 0.01)
         label = Tensor(np.ones([batch_size]).astype(np.int32))
         loss = train_network(data, label)
         losses.append(loss)
@@ -351,13 +383,16 @@ def test_trainTensor_amp(num_classes=10, epoch=18, batch_size=16):
     net = resnet50(num_classes)
     lr = 0.1
     momentum = 0.9
-    optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, momentum)
+    optimizer = Momentum(filter(lambda x: x.requires_grad,
+                                net.get_parameters()), lr, momentum)
     criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
-    train_network = amp.build_train_network(net, optimizer, criterion, level="O2")
+    train_network = amp.build_train_network(
+        net, optimizer, criterion, level="O2")
     train_network.set_train()
     losses = []
     for i in range(0, epoch):
-        data = Tensor(np.ones([batch_size, 3, 224, 224]).astype(np.float32) * 0.01)
+        data = Tensor(np.ones([batch_size, 3, 224, 224]
+                              ).astype(np.float32) * 0.01)
         label = Tensor(np.ones([batch_size]).astype(np.int32))
         loss = train_network(data, label)
         losses.append(loss)