fixed issue I3HZIK and removed some more SUPPORT_TRAIN ifdefs

mindspore/lite/src/lite_session.cc

@@ -395,9 +395,9 @@ int LiteSession::CompileGraph(Model *model) {
   }
   // scheduler kernels
 #if SUPPORT_NPU
-  Scheduler scheduler(context_, model, &tensors_, npu_manager_, npu_pass_manager_);
+  Scheduler scheduler(context_, model, &tensors_, is_train_session_, npu_manager_, npu_pass_manager_);
 #else
-  Scheduler scheduler(context_, model, &tensors_);
+  Scheduler scheduler(context_, model, &tensors_, is_train_session_);
 #endif
   ret = scheduler.Schedule(&kernels_);
   if (ret != RET_OK) {
@@ -599,7 +599,7 @@ LiteSession::~LiteSession() {
   npu_manager_->Reset();
   delete npu_manager_;
 #endif
-#if GPU_OPENCL && !SUPPORT_TRAIN
+#if GPU_OPENCL
   delete opencl_runtime_wrapper_;
 #endif
   delete (model_);
@@ -737,7 +737,7 @@ int LiteSession::Resize(const std::vector<mindspore::tensor::MSTensor *> &inputs
 }
 
 int LiteSession::InitGPURuntime() {
-#if GPU_OPENCL && !SUPPORT_TRAIN
+#if GPU_OPENCL
   if (this->context_->IsGpuEnabled()) {
     opencl_runtime_wrapper_ = new (std::nothrow) opencl::OpenCLRuntimeWrapper();
     if (opencl_runtime_wrapper_ == nullptr) {
@@ -754,7 +754,7 @@ int LiteSession::InitGPURuntime() {
       MS_LOG(INFO) << "Init OpenCL runtime success.";
     }
   }
-#elif GPU_VULKAN && !SUPPORT_TRAIN
+#elif GPU_VULKAN
   if (this->context_->IsGpuEnabled()) {
     auto gpu_device_info = this->context_->GetGpuInfo();
     vk_runtime_wrap_ = new (std::nothrow) gpu::GpuRuntimeWrapper<vulkan::VulkanRuntime>;

mindspore/lite/src/lite_session.h

@@ -134,13 +134,14 @@ class LiteSession : public session::LiteSession {
   Executor *executor_ = nullptr;
   Model *model_ = nullptr;
   std::atomic<bool> is_running_ = false;
+  bool is_train_session_ = false;
 #if SUPPORT_NPU
   NPUManager *npu_manager_ = nullptr;
   NPUPassManager *npu_pass_manager_ = nullptr;
 #endif
-#if GPU_OPENCL && !SUPPORT_TRAIN
+#if GPU_OPENCL
   opencl::OpenCLRuntimeWrapper *opencl_runtime_wrapper_{nullptr};
-#elif GPU_VULKAN && !SUPPORT_TRAIN
+#elif GPU_VULKAN
   gpu::GpuRuntimeWrapper<vulkan::VulkanRuntime> *vk_runtime_wrap_{nullptr};
 #endif
 };

mindspore/lite/src/scheduler.cc

@@ -215,7 +215,6 @@ int Scheduler::InferSubGraphShape(size_t subgraph_index, bool *infer_shape_inter
 }
 
 namespace {
-#ifndef SUPPORT_TRAIN
 int CastConstTensorData(Tensor *tensor, std::map<Tensor *, Tensor *> *restored_origin_tensors, TypeId dst_data_type) {
 #if defined(ENABLE_ARM) && defined(ENABLE_FP16)
   MS_ASSERT(tensor != nullptr);
@@ -319,7 +318,6 @@ int CopyConstTensorData(const std::vector<Tensor *> &tensors, int op_type) {
   }
   return RET_OK;
 }
-#endif
 
 inline void FreeRestoreTensors(std::map<Tensor *, Tensor *> *restored_origin_tensors) {
   MS_ASSERT(restored_origin_tensors != nullptr);
@@ -368,7 +366,8 @@ kernel::LiteKernel *Scheduler::FindCpuKernel(const std::vector<Tensor *> &in_ten
     return nullptr;
   }
   std::map<Tensor *, Tensor *> restored_origin_tensors;
-#ifndef SUPPORT_TRAIN
+
+  if (!is_train_session_) {
     ret = CastConstTensorsData(in_tensors, &restored_origin_tensors, kernel_data_type);
     if (ret != RET_OK) {
       MS_LOG(DEBUG) << "CastConstTensorsData failed: " << ret;
@@ -380,7 +379,7 @@ kernel::LiteKernel *Scheduler::FindCpuKernel(const std::vector<Tensor *> &in_ten
       MS_LOG(DEBUG) << "CopyConstTensorsData failed: " << ret;
       return nullptr;
     }
-#endif
+  }
   auto *kernel = KernelRegistry::GetInstance()->GetKernel(in_tensors, out_tensors, context_, cpu_desc, op_parameter);
   if (kernel != nullptr) {
     MS_LOG(DEBUG) << "Get TypeId(" << kernel_data_type << ") op success: " << PrimitiveCurVersionTypeName(op_type);

mindspore/lite/src/scheduler.h

@@ -30,16 +30,17 @@
 namespace mindspore::lite {
 class Scheduler {
  public:
-  Scheduler(const InnerContext *ctx, Model *src_model, std::vector<Tensor *> *src_tensors)
-      : context_(ctx), src_model_(src_model), src_tensors_(src_tensors) {}
+  Scheduler(const InnerContext *ctx, Model *src_model, std::vector<Tensor *> *src_tensors, bool is_train_session)
+      : context_(ctx), src_model_(src_model), src_tensors_(src_tensors), is_train_session_(is_train_session) {}
 #if SUPPORT_NPU
-  Scheduler(const InnerContext *ctx, Model *src_model, std::vector<Tensor *> *src_tensors,
+  Scheduler(const InnerContext *ctx, Model *src_model, std::vector<Tensor *> *src_tensors, bool is_train_session,
             NPUManager *npu_manager = nullptr, NPUPassManager *npu_pass_manager = nullptr)
       : context_(ctx),
         src_model_(src_model),
         src_tensors_(src_tensors),
         npu_manager_(npu_manager),
-        npu_pass_manager_(npu_pass_manager) {}
+        npu_pass_manager_(npu_pass_manager),
+        is_train_session_(is_train_session) {}
 #endif
   ~Scheduler() = default;
 
@@ -113,6 +114,7 @@ class Scheduler {
 #endif
   std::vector<size_t> graph_output_node_indexes_;
   std::map<int, OpParameter *> op_parameters_;
+  bool is_train_session_ = false;
 };
 }  // namespace mindspore::lite
 

mindspore/lite/src/sub_graph_kernel.cc

@@ -166,11 +166,6 @@ int CpuSubGraph::Run(const KernelCallBack &before, const KernelCallBack &after)
     }
   }
 
-#ifdef SUPPORT_TRAIN
-  for (auto out_tensor : out_tensors_) {  // increase RefCount of output tensors, such that Run will not free them
-    out_tensor->set_ref_count(out_tensor->ref_count() + 1);
-  }
-#endif
 #ifdef SUPPORT_GPU
   // In heterogeneous scenarios of CPU and GPU, call MutableData to MapBuffer(synchronize data).
   if (static_cast<const lite::InnerContext *>(context_)->IsGpuEnabled()) {

mindspore/lite/src/train/train_session.cc

@@ -57,6 +57,7 @@ static kernel::LiteKernel *TSFindKernel(const std::vector<kernel::LiteKernel *>
   return *it;
 }
 TrainSession::TrainSession() {
+  is_train_session_ = true;
 #ifdef ENABLE_V0
   if (VersionManager::GetInstance()->CheckV0Schema()) {
     kernel::PopulateTrainV0Parameters();