!10450 [MSLITE][DEVELOP] modify npu transpose pass

From: @yangruoqi713
Reviewed-by: @zhang_xue_tong,@zhang_xue_tong,@hangangqiang
Signed-off-by: @zhang_xue_tong,@zhang_xue_tong

mindspore/lite/src/runtime/agent/npu/optimizer/npu_fusion_pass.cc

@@ -21,20 +21,22 @@
 namespace mindspore::lite {
 bool CheckFusion(kernel::LiteKernel *kernel) {
   auto pre_flag =
-    std::all_of(kernel->in_kernels().begin(), kernel->in_kernels().end(), [](const kernel::LiteKernel *kernel) {
-      return kernel->Type() == schema::PrimitiveType_Nchw2Nhwc && kernel->out_kernels().size() == 1;
+    std::all_of(kernel->in_kernels().begin(), kernel->in_kernels().end(), [](const kernel::LiteKernel *in_kernel) {
+      return in_kernel->Type() == schema::PrimitiveType_Nchw2Nhwc && in_kernel->out_kernels().size() == 1;
     });
   if (!pre_flag) {
     return false;
   }
-  auto post_flag =
-    std::all_of(kernel->out_kernels().begin(), kernel->out_kernels().end(), [](const kernel::LiteKernel *kernel) {
-      return kernel->Type() == schema::PrimitiveType_Nhwc2Nchw && kernel->in_kernels().size() == 1;
-    });
+  auto post_flag = std::all_of(
+    kernel->out_kernels().begin(), kernel->out_kernels().end(),
+    [](const kernel::LiteKernel *out_kernel) { return out_kernel->Type() == schema::PrimitiveType_Nhwc2Nchw; });
   return post_flag;
 }
 
 bool CheckFormatFusion(kernel::LiteKernel *kernel) {
+  if (kernel->out_kernels().empty()) {
+    return false;
+  }
   if (kernel->Type() == schema::PrimitiveType_Nhwc2Nchw) {
     return std::all_of(
       kernel->out_kernels().begin(), kernel->out_kernels().end(),
@@ -159,38 +161,26 @@ int TransFormAxis(int axis) {
   }
 }
 
-int NPUFusionPass::AddFusion(kernel::LiteKernel *kernel) {
-  if (!CheckFusion(kernel)) {
-    return RET_OK;
-  }
+void NPUFusionPass::UpdateKernel(kernel::LiteKernel *kernel) {
   UpdatePreTensors(kernel);
   UpdatePostTensors(kernel);
   UpdatePreKernels(kernel);
   UpdatePostKernels(kernel);
+}
+
+int NPUFusionPass::CommonFusion(kernel::LiteKernel *kernel) {
+  UpdateKernel(kernel);
   return RET_OK;
 }
 
 int NPUFusionPass::ConcatFusion(kernel::LiteKernel *kernel) {
-  if (!CheckFusion(kernel)) {
-    return RET_OK;
-  }
-  UpdatePreTensors(kernel);
-  UpdatePostTensors(kernel);
-  UpdatePreKernels(kernel);
-  UpdatePostKernels(kernel);
+  UpdateKernel(kernel);
   auto concat_param = reinterpret_cast<ConcatParameter *>(kernel->op_parameter());
   concat_param->axis_ = TransFormAxis(concat_param->axis_);
   return RET_OK;
 }
 
 int NPUFusionPass::FormatFusion(kernel::LiteKernel *kernel) {
-  if (kernel->out_kernels().empty()) {
-    return RET_OK;
-  }
-  if (!CheckFormatFusion(kernel)) {
-    return RET_OK;
-  }
-
   auto pre_kernel = kernel->in_kernels()[0];
   auto in_tensor = kernel->in_tensors()[0];
   auto out_tensor = kernel->out_tensors()[0];
@@ -237,17 +227,28 @@ int NPUFusionPass::FormatFusion(kernel::LiteKernel *kernel) {
 }
 
 int NPUFusionPass::Run() {
-  for (auto kernel : *kernels) {
+  for (size_t i = 0; i < kernels->size(); i++) {
+    auto kernel = (*kernels)[i];
+    if (kernel->Type() == schema::PrimitiveType_Nchw2Nhwc || kernel->Type() == schema::PrimitiveType_Nchw2Nhwc) {
+      if (CheckFormatFusion(kernel)) {
+        i--;
+        FormatFusion(kernel);
+      }
+      continue;
+    }
+    if (!CheckFusion(kernel)) {
+      continue;
+    }
     switch (kernel->Type()) {
       case schema::PrimitiveType_Concat:
+        i -= kernel->in_kernels().size();
         ConcatFusion(kernel);
         continue;
       case schema::PrimitiveType_Add:
       case schema::PrimitiveType_Activation:
-        AddFusion(kernel);
-        continue;
-      case schema::PrimitiveType_Nchw2Nhwc:
-        FormatFusion(kernel);
+      case schema::PrimitiveType_Eltwise:
+        i -= kernel->in_kernels().size();
+        CommonFusion(kernel);
         continue;
       default:
         continue;

mindspore/lite/src/runtime/agent/npu/optimizer/npu_fusion_pass.h

@@ -33,11 +33,12 @@ class NPUFusionPass : public NPUBasePass {
   int Run() override;
 
  protected:
-  void RemoveAndFreeKernel(kernel::LiteKernel *cur_kernel);
   void UpdatePreKernels(kernel::LiteKernel *kernel);
   void UpdatePostKernels(kernel::LiteKernel *kernel);
+  void RemoveAndFreeKernel(kernel::LiteKernel *cur_kernel);
+  void UpdateKernel(kernel::LiteKernel *kernel);
+  int CommonFusion(kernel::LiteKernel *kernel);
   int ConcatFusion(kernel::LiteKernel *kernel);
-  int AddFusion(kernel::LiteKernel *kernel);
   int FormatFusion(kernel::LiteKernel *kernel);
 
  private:

mindspore/lite/src/runtime/agent/npu/optimizer/npu_insert_transform_pass.cc

@@ -21,7 +21,9 @@ namespace mindspore::lite {
 using kernel::KERNEL_ARCH::kNPU;
 enum InsertState { InsertNone, PreInsert, PostInsert };
 
-std::set<mindspore::schema::PrimitiveType> npu_insert_nodes = {schema::PrimitiveType_Concat, schema::PrimitiveType_Add};
+std::set<mindspore::schema::PrimitiveType> npu_insert_nodes = {schema::PrimitiveType_Concat, schema::PrimitiveType_Add,
+                                                               schema::PrimitiveType_Eltwise,
+                                                               schema::PrimitiveType_Activation};
 
 int GetInsertState(kernel::LiteKernel *kernel) {
   if (npu_insert_nodes.find(kernel->Type()) == npu_insert_nodes.end()) {
@@ -42,16 +44,18 @@ int GetInsertState(kernel::LiteKernel *kernel) {
   return InsertNone;
 }
 
-int NPUInsertTransformPass::InsertPreNode(const InnerContext *context, kernel::LiteKernel *cur_kernel,
-                                          std::vector<kernel::LiteKernel *> *all_kernels,
+int NPUInsertTransformPass::InsertPreNode(const InnerContext *context, kernel::LiteKernel *kernel,
+                                          std::vector<kernel::LiteKernel *> *trans_kernels,
                                           std::vector<Tensor *> *all_tensors) {
-  for (auto kernel : cur_kernel->in_kernels()) {
-    if (kernel->Type() == schema::PrimitiveType_Nchw2Nhwc) {
+  for (auto in_kernel : kernel->in_kernels()) {
+    if (in_kernel->Type() == schema::PrimitiveType_Nchw2Nhwc) {
       continue;
     }
-    auto nhwc_shape = cur_kernel->out_tensors()[0]->shape();
+    auto nhwc_shape = in_kernel->out_tensors()[0]->shape();
     std::vector<int> nchw_shape = {nhwc_shape[0], nhwc_shape[3], nhwc_shape[1], nhwc_shape[2]};
-    auto nh2nc_tensor = new Tensor(kernel->out_tensors()[0]->data_type(), nchw_shape, schema::Format_NHWC, Tensor::VAR);
+
+    auto nh2nc_tensor =
+      new Tensor(in_kernel->out_tensors()[0]->data_type(), nchw_shape, schema::Format_NHWC, Tensor::VAR);
     std::vector<Tensor *> nh2nc_tensors = {nh2nc_tensor};
     all_tensors->push_back(nh2nc_tensors[0]);
 
@@ -59,34 +63,36 @@ int NPUInsertTransformPass::InsertPreNode(const InnerContext *context, kernel::L
     std::vector<Tensor *> nc2nh_tensors = {nc2nh_tensor};
     all_tensors->push_back(nc2nh_tensors[0]);
 
-    auto nh2nc_name = kernel->name() + "_nh2nc_" + std::to_string(total++);
-    auto *nh2nc_kernel = NPUPassUtils::CreateNhwc2NchwKernel(kernel->out_tensors(), nh2nc_tensors, context, nh2nc_name);
-    all_kernels->push_back(nh2nc_kernel);
+    auto nh2nc_name = in_kernel->name() + "_nh2nc_" + std::to_string(total++);
+    auto *nh2nc_kernel =
+      NPUPassUtils::CreateNhwc2NchwKernel(in_kernel->out_tensors(), nh2nc_tensors, context, nh2nc_name);
+    trans_kernels->push_back(nh2nc_kernel);
     insert_primitive_.push_back(nh2nc_kernel->GetPrimitive());
-    auto nc2nh_name = kernel->name() + "_nc2nh_" + std::to_string(total++);
+
+    auto nc2nh_name = in_kernel->name() + "_nc2nh_" + std::to_string(total++);
     auto *nc2nh_kernel = NPUPassUtils::CreateNchw2NhwcKernel(nh2nc_tensors, nc2nh_tensors, context, nc2nh_name);
-    all_kernels->push_back(nc2nh_kernel);
+    trans_kernels->push_back(nc2nh_kernel);
     insert_primitive_.push_back(nc2nh_kernel->GetPrimitive());
-    NPUPassUtils::UpdateKernel(nh2nc_kernel, {kernel}, {nc2nh_kernel}, kernel->out_tensors(), nh2nc_tensors);
-    NPUPassUtils::UpdateKernel(nc2nh_kernel, {nh2nc_kernel}, {cur_kernel}, nh2nc_tensors, nc2nh_tensors);
-    NPUPassUtils::UpdateNH2NCTransNodePreKernel(kernel, nh2nc_kernel, cur_kernel);
-    NPUPassUtils::UpdateNC2NHTransNodeAfterKernel(kernel, nc2nh_kernel, cur_kernel);
+
+    NPUPassUtils::UpdateKernel(nh2nc_kernel, {in_kernel}, {nc2nh_kernel}, in_kernel->out_tensors(), nh2nc_tensors);
+    NPUPassUtils::UpdateKernel(nc2nh_kernel, {nh2nc_kernel}, {kernel}, nh2nc_tensors, nc2nh_tensors);
+    NPUPassUtils::UpdateNH2NCTransNodePreKernel(in_kernel, nh2nc_kernel, kernel);
+    NPUPassUtils::UpdateNC2NHTransNodeAfterKernel(in_kernel, nc2nh_kernel, kernel);
   }
   return RET_OK;
 }
 
-int NPUInsertTransformPass::InsertPostNode(const InnerContext *context, kernel::LiteKernel *cur_kernel,
-                                           std::vector<kernel::LiteKernel *> *all_kernels,
+int NPUInsertTransformPass::InsertPostNode(const InnerContext *context, kernel::LiteKernel *kernel,
+                                           std::vector<kernel::LiteKernel *> *trans_kernels,
                                            std::vector<Tensor *> *all_tensors) {
-  for (auto out_kernel : cur_kernel->out_kernels()) {
+  for (auto out_kernel : kernel->out_kernels()) {
     if (out_kernel->Type() == schema::PrimitiveType_Nhwc2Nchw) {
       continue;
     }
-    auto nhwc_shape = cur_kernel->out_tensors()[0]->shape();
+    auto nhwc_shape = kernel->out_tensors()[0]->shape();
     std::vector<int> nchw_shape = {nhwc_shape[0], nhwc_shape[3], nhwc_shape[1], nhwc_shape[2]};
 
-    auto nh2nc_tensor =
-      new Tensor(cur_kernel->out_tensors()[0]->data_type(), nchw_shape, schema::Format_NHWC, Tensor::VAR);
+    auto nh2nc_tensor = new Tensor(kernel->out_tensors()[0]->data_type(), nchw_shape, schema::Format_NHWC, Tensor::VAR);
     std::vector<Tensor *> nh2nc_tensors = {nh2nc_tensor};
     all_tensors->push_back(nh2nc_tensors[0]);
 
@@ -94,19 +100,20 @@ int NPUInsertTransformPass::InsertPostNode(const InnerContext *context, kernel::
     std::vector<Tensor *> nc2nh_tensors = {nc2nh_tensor};
     all_tensors->push_back(nc2nh_tensors[0]);
 
-    auto nh2nc_name = cur_kernel->name() + "_nh2nc_" + std::to_string(total++);
-    auto *nh2nc_kernel =
-      NPUPassUtils::CreateNhwc2NchwKernel(cur_kernel->out_tensors(), nh2nc_tensors, context, nh2nc_name);
-    all_kernels->push_back(nh2nc_kernel);
+    auto nh2nc_name = kernel->name() + "_nh2nc_" + std::to_string(total++);
+    auto *nh2nc_kernel = NPUPassUtils::CreateNhwc2NchwKernel(kernel->out_tensors(), nh2nc_tensors, context, nh2nc_name);
+    trans_kernels->push_back(nh2nc_kernel);
     insert_primitive_.push_back(nh2nc_kernel->GetPrimitive());
-    auto nc2nh_name = cur_kernel->name() + "_nc2nh_" + std::to_string(total++);
+
+    auto nc2nh_name = kernel->name() + "_nc2nh_" + std::to_string(total++);
     auto *nc2nh_kernel = NPUPassUtils::CreateNchw2NhwcKernel(nh2nc_tensors, nc2nh_tensors, context, nc2nh_name);
-    all_kernels->push_back(nc2nh_kernel);
+    trans_kernels->push_back(nc2nh_kernel);
     insert_primitive_.push_back(nc2nh_kernel->GetPrimitive());
-    NPUPassUtils::UpdateKernel(nh2nc_kernel, {cur_kernel}, {nc2nh_kernel}, cur_kernel->out_tensors(), nh2nc_tensors);
+
+    NPUPassUtils::UpdateKernel(nh2nc_kernel, {kernel}, {nc2nh_kernel}, kernel->out_tensors(), nh2nc_tensors);
     NPUPassUtils::UpdateKernel(nc2nh_kernel, {nh2nc_kernel}, {out_kernel}, nh2nc_tensors, nc2nh_tensors);
-    NPUPassUtils::UpdateNH2NCTransNodePreKernel(cur_kernel, nh2nc_kernel, out_kernel);
-    NPUPassUtils::UpdateNC2NHTransNodeAfterKernel(cur_kernel, nc2nh_kernel, out_kernel);
+    NPUPassUtils::UpdateNH2NCTransNodePreKernel(kernel, nh2nc_kernel, out_kernel);
+    NPUPassUtils::UpdateNC2NHTransNodeAfterKernel(kernel, nc2nh_kernel, out_kernel);
   }
   return RET_OK;
 }

mindspore/lite/src/runtime/agent/npu/optimizer/npu_insert_transform_pass.h

@@ -41,11 +41,11 @@ class NPUInsertTransformPass : public NPUBasePass {
   int Run() override;
 
  private:
-  int InsertPreNode(const InnerContext *context, kernel::LiteKernel *cur_kernel,
-                    std::vector<kernel::LiteKernel *> *all_kernels, std::vector<Tensor *> *all_tensors);
+  int InsertPreNode(const InnerContext *context, kernel::LiteKernel *kernel,
+                    std::vector<kernel::LiteKernel *> *trans_kernels, std::vector<Tensor *> *all_tensors);
 
-  int InsertPostNode(const InnerContext *context, kernel::LiteKernel *cur_kernel,
-                     std::vector<kernel::LiteKernel *> *all_kernels, std::vector<Tensor *> *all_tensors);
+  int InsertPostNode(const InnerContext *context, kernel::LiteKernel *kernel,
+                     std::vector<kernel::LiteKernel *> *trans_kernels, std::vector<Tensor *> *all_tensors);
 
  private:
   int total = 0;

mindspore/lite/src/runtime/agent/npu/optimizer/npu_pass_utils.cc

@@ -100,25 +100,25 @@ void NPUPassUtils::UpdateKernel(kernel::LiteKernel *kernel, const std::vector<ke
   kernel->set_out_kernels(out_kernels);
 }
 
-void NPUPassUtils::UpdateNH2NCTransNodePreKernel(kernel::LiteKernel *kernel, kernel::LiteKernel *trans_kernel,
-                                                 kernel::LiteKernel *after_kernel) {
+void NPUPassUtils::UpdateNH2NCTransNodePreKernel(kernel::LiteKernel *pre_kernel, kernel::LiteKernel *trans_kernel,
+                                                 kernel::LiteKernel *kernel) {
   std::vector<kernel::LiteKernel *> out_kernels;
 
-  for (auto out_kernel : kernel->out_kernels()) {
-    if (out_kernel == after_kernel) {
+  for (auto out_kernel : pre_kernel->out_kernels()) {
+    if (out_kernel == kernel) {
       out_kernels.push_back(trans_kernel);
     } else {
       out_kernels.push_back(out_kernel);
     }
   }
-  UpdateKernel(kernel, kernel->in_kernels(), out_kernels, kernel->in_tensors(), kernel->out_tensors());
+  pre_kernel->set_out_kernels(out_kernels);
 }
 
 void NPUPassUtils::UpdateNC2NHTransNodePreKernel(kernel::LiteKernel *kernel, kernel::LiteKernel *trans_kernel,
-                                                 kernel::LiteKernel *next_kernel) {
+                                                 kernel::LiteKernel *post_kernel) {
   std::vector<kernel::LiteKernel *> cur_out_kernels;
   for (auto out_kernel : kernel->out_kernels()) {
-    if (out_kernel == next_kernel) {
+    if (out_kernel == post_kernel) {
       cur_out_kernels.push_back(trans_kernel);
     } else {
       cur_out_kernels.push_back(out_kernel);
@@ -130,45 +130,47 @@ void NPUPassUtils::UpdateNC2NHTransNodePreKernel(kernel::LiteKernel *kernel, ker
   std::vector<int> nchw_shape = {nhwc_shape[0], nhwc_shape[3], nhwc_shape[1], nhwc_shape[2]};
   kernel_out_tensor->set_format(schema::Format_NCHW);
   kernel_out_tensor->set_shape(nchw_shape);
-  UpdateKernel(kernel, kernel->in_kernels(), cur_out_kernels, kernel->in_tensors(), {kernel_out_tensor});
+  kernel->set_out_kernels(cur_out_kernels);
+  kernel->set_out_tensors({kernel_out_tensor});
 }
 
 void NPUPassUtils::UpdateNH2NCTransNodeAfterKernel(kernel::LiteKernel *kernel, kernel::LiteKernel *trans_kernel,
-                                                   kernel::LiteKernel *before_kernel) {
+                                                   kernel::LiteKernel *pre_kernel) {
   std::vector<lite::Tensor *> cur_kernel_in_tensors = {trans_kernel->out_tensors()[0]};
   for (int i = 1; i < kernel->in_tensors().size(); i++) {
     cur_kernel_in_tensors.push_back(kernel->in_tensors()[i]);
   }
   std::vector<kernel::LiteKernel *> cur_in_kernels = {trans_kernel};
-  for (int i = 0; i < kernel->in_kernels().size(); i++) {
+  for (int i = 1; i < kernel->in_kernels().size(); i++) {
     auto in_kernel = kernel->in_kernels()[i];
     if (in_kernel != kernel) {
       cur_in_kernels.push_back(in_kernel);
     }
   }
-  UpdateKernel(kernel, cur_in_kernels, kernel->out_kernels(), cur_kernel_in_tensors, kernel->out_tensors());
+  kernel->set_in_kernels(cur_in_kernels);
+  kernel->set_in_tensors({cur_kernel_in_tensors});
 }
 
 void NPUPassUtils::UpdateNC2NHTransNodeAfterKernel(kernel::LiteKernel *kernel, kernel::LiteKernel *trans_kernel,
-                                                   kernel::LiteKernel *next_kernel) {
-  std::vector<Tensor *> next_in_tensors;
-  for (auto next_in_tensor : next_kernel->in_tensors()) {
-    if (next_in_tensor != kernel->out_tensors()[0]) {
-      next_in_tensors.push_back(next_in_tensor);
+                                                   kernel::LiteKernel *post_kernel) {
+  std::vector<Tensor *> post_in_tensors;
+  for (auto post_in_tensor : post_kernel->in_tensors()) {
+    if (post_in_tensor != kernel->out_tensors()[0]) {
+      post_in_tensors.push_back(post_in_tensor);
     } else {
-      next_in_tensors.push_back(trans_kernel->out_tensors()[0]);
+      post_in_tensors.push_back(trans_kernel->out_tensors()[0]);
     }
   }
-  next_kernel->set_in_tensors(next_in_tensors);
-  std::vector<kernel::LiteKernel *> next_in_kernels;
-  for (auto in_kernel : next_kernel->in_kernels()) {
+  post_kernel->set_in_tensors(post_in_tensors);
+  std::vector<kernel::LiteKernel *> post_in_kernels;
+  for (auto in_kernel : post_kernel->in_kernels()) {
     if (in_kernel == kernel) {
-      next_in_kernels.push_back(trans_kernel);
+      post_in_kernels.push_back(trans_kernel);
     } else {
-      next_in_kernels.push_back(in_kernel);
+      post_in_kernels.push_back(in_kernel);
     }
   }
-  NPUPassUtils::UpdateKernel(next_kernel, next_in_kernels, next_kernel->out_kernels(), next_in_tensors,
-                             next_kernel->out_tensors());
+  post_kernel->set_in_kernels(post_in_kernels);
+  post_kernel->set_in_tensors({post_in_tensors});
 }
 }  // namespace mindspore::lite

mindspore/lite/src/runtime/agent/npu/optimizer/npu_pass_utils.h

@@ -35,17 +35,17 @@ class NPUPassUtils {
                            const std::vector<kernel::LiteKernel *> &out_kernels,
                            const std::vector<Tensor *> &in_tensors, const std::vector<Tensor *> &out_tensors);
 
-  static void UpdateNH2NCTransNodePreKernel(kernel::LiteKernel *kernel, kernel::LiteKernel *trans_kernel,
-                                            kernel::LiteKernel *after_kernel);
+  static void UpdateNH2NCTransNodePreKernel(kernel::LiteKernel *pre_kernel, kernel::LiteKernel *trans_kernel,
+                                            kernel::LiteKernel *kernel);
 
   static void UpdateNC2NHTransNodePreKernel(kernel::LiteKernel *kernel, kernel::LiteKernel *trans_kernel,
-                                            kernel::LiteKernel *next_kernel);
+                                            kernel::LiteKernel *post_kernel);
 
   static void UpdateNH2NCTransNodeAfterKernel(kernel::LiteKernel *kernel, kernel::LiteKernel *trans_kernel,
-                                              kernel::LiteKernel *before_kernel);
+                                              kernel::LiteKernel *pre_kernel);
 
   static void UpdateNC2NHTransNodeAfterKernel(kernel::LiteKernel *kernel, kernel::LiteKernel *trans_kernel,
-                                              kernel::LiteKernel *next_kernel);
+                                              kernel::LiteKernel *post_kernel);
 
  private:
   static PrimitiveC *CreateNchw2NhwcPrimitive();

mindspore/lite/src/runtime/agent/npu/optimizer/npu_transform_pass.cc

@@ -19,51 +19,53 @@
 #include "src/runtime/agent/npu/npu_manager.h"
 #include "src/runtime/agent/npu/optimizer/npu_pass_utils.h"
 namespace mindspore::lite {
-using kernel::KERNEL_ARCH::kCPU;
 using kernel::KERNEL_ARCH::kNPU;
 int NPUTransformPass::InsertPreNode(const InnerContext *context, kernel::LiteKernel *kernel,
-                                    std::vector<kernel::LiteKernel *> *all_kernels,
+                                    std::vector<kernel::LiteKernel *> *trans_kernels,
                                     std::vector<Tensor *> *all_tensors) {
   bool is_input_kernel = kernel->in_kernels().empty();
   if (is_input_kernel || kernel->in_kernels()[0]->desc().arch != kNPU ||
       npu_trans_nodes.find(kernel->in_kernels()[0]->Type()) == npu_trans_nodes.end()) {
-    kernel::LiteKernel *before_kernel = nullptr;
+    kernel::LiteKernel *pre_kernel = nullptr;
     if (!is_input_kernel) {
-      before_kernel = kernel->in_kernels()[0];
+      pre_kernel = kernel->in_kernels()[0];
     }
-    // Create pre transform kernel out tensors.
+
+    // Create pre transform kernel's out tensor.
     auto nhwc_shape = kernel->in_tensors()[0]->shape();
     std::vector<int> nchw_shape = {nhwc_shape[0], nhwc_shape[3], nhwc_shape[1], nhwc_shape[2]};
     auto tensor = new Tensor(kernel->in_tensors()[0]->data_type(), nchw_shape, schema::Format_NCHW, Tensor::VAR);
     std::vector<Tensor *> pre_trans_out_tensors = {tensor};
     all_tensors->push_back(pre_trans_out_tensors[0]);
-    // Replace the output tensor of the previous node
+
+    // Create pre transform kernel: Nhwc2Nchw
     auto name = kernel->name() + "_pre_trans" + "_Nhwc2Nchw_" + std::to_string(total++);
-    auto *pre_trans_kernel =
+    auto *trans_kernel =
       NPUPassUtils::CreateNhwc2NchwKernel({kernel->in_tensors()[0]}, pre_trans_out_tensors, context, name);
-    // Insert Nhwc2Nchw into the front of the current queue
-    all_kernels->push_back(pre_trans_kernel);
-    insert_primitive_.push_back(pre_trans_kernel->GetPrimitive());
-    // Replace the output kernel of the previous node
+
+    trans_kernels->push_back(trans_kernel);
+    insert_primitive_.push_back(trans_kernel->GetPrimitive());
+
+    // Set in_kernels, out_kernels, in_tensors,out_tensors for transform kernel
     std::vector<kernel::LiteKernel *> pre_trans_in_kernel;
     if (is_input_kernel) {
       pre_trans_in_kernel = {};
     } else {
-      pre_trans_in_kernel = {before_kernel};
+      pre_trans_in_kernel = {pre_kernel};
     }
-    NPUPassUtils::UpdateKernel(pre_trans_kernel, pre_trans_in_kernel, {kernel}, {kernel->in_tensors()[0]},
+    NPUPassUtils::UpdateKernel(trans_kernel, pre_trans_in_kernel, {kernel}, {kernel->in_tensors()[0]},
                                pre_trans_out_tensors);
 
-    if (before_kernel != nullptr) {
-      NPUPassUtils::UpdateNH2NCTransNodePreKernel(before_kernel, pre_trans_kernel, kernel);
+    if (pre_kernel != nullptr) {
+      NPUPassUtils::UpdateNH2NCTransNodePreKernel(pre_kernel, trans_kernel, kernel);
     }
-    NPUPassUtils::UpdateNH2NCTransNodeAfterKernel(kernel, pre_trans_kernel, before_kernel);
+    NPUPassUtils::UpdateNH2NCTransNodeAfterKernel(kernel, trans_kernel, pre_kernel);
   }
   return RET_OK;
 }
 
 int NPUTransformPass::InsertPostNode(const InnerContext *context, kernel::LiteKernel *kernel,
-                                     std::vector<kernel::LiteKernel *> *all_kernels,
+                                     std::vector<kernel::LiteKernel *> *trans_kernels,
                                      std::vector<Tensor *> *all_tensors) {
   // Model output does not insert operator
   if (kernel->out_kernels().empty()) {
@@ -71,27 +73,30 @@ int NPUTransformPass::InsertPostNode(const InnerContext *context, kernel::LiteKe
   }
   // Single output multiple references
   for (int i = 0; i < kernel->out_kernels().size(); i++) {
-    auto next_kernel = kernel->out_kernels().at(i);
-    if (next_kernel->desc().arch == kNPU && npu_trans_nodes.find(next_kernel->Type()) != npu_trans_nodes.end()) {
+    auto post_kernel = kernel->out_kernels().at(i);
+    if (post_kernel->desc().arch == kNPU && npu_trans_nodes.find(post_kernel->Type()) != npu_trans_nodes.end()) {
       continue;
     }
-    // Change format the output of the current kernel nhwc->nchw
+
+    // Create post transform kernel's out tensor.
     auto tensor = new Tensor(kernel->out_tensors()[0]->data_type(), kernel->out_tensors()[0]->shape(),
                              schema::Format_NHWC, Tensor::VAR);
     std::vector<Tensor *> post_trans_out_tensors = {tensor};
     all_tensors->push_back(post_trans_out_tensors[0]);
-    // Use the output tensor of the current node as the input tensor of the post-conversion operator
+
+    // Create post transform kernel: Nchw2Nhwc
     auto name = kernel->name() + "_post_trans" + "_Nchw2Nhwc" + std::to_string(total++);
     auto *post_trans_kernel =
       NPUPassUtils::CreateNchw2NhwcKernel(kernel->out_tensors(), post_trans_out_tensors, context, name);
-    // Replace the input tensor of the next node
-    NPUPassUtils::UpdateKernel(post_trans_kernel, {kernel}, {next_kernel}, kernel->out_tensors(),
+
+    // Set in_kernels, out_kernels, in_tensors,out_tensors for transform kernel
+    NPUPassUtils::UpdateKernel(post_trans_kernel, {kernel}, {post_kernel}, kernel->out_tensors(),
                                post_trans_out_tensors);
     insert_primitive_.push_back(post_trans_kernel->GetPrimitive());
-    // Directly insert in the back, will not affect the topological sort
-    all_kernels->push_back(post_trans_kernel);
-    NPUPassUtils::UpdateNC2NHTransNodePreKernel(kernel, post_trans_kernel, next_kernel);
-    NPUPassUtils::UpdateNC2NHTransNodeAfterKernel(kernel, post_trans_kernel, next_kernel);
+
+    trans_kernels->push_back(post_trans_kernel);
+    NPUPassUtils::UpdateNC2NHTransNodePreKernel(kernel, post_trans_kernel, post_kernel);
+    NPUPassUtils::UpdateNC2NHTransNodeAfterKernel(kernel, post_trans_kernel, post_kernel);
   }
   return RET_OK;
 }

mindspore/lite/src/runtime/agent/npu/optimizer/npu_transform_pass.h

@@ -43,10 +43,10 @@ class NPUTransformPass : public NPUBasePass {
 
  private:
   int InsertPreNode(const InnerContext *context, kernel::LiteKernel *kernel,
-                    std::vector<kernel::LiteKernel *> *all_kernels, std::vector<Tensor *> *all_tensors);
+                    std::vector<kernel::LiteKernel *> *trans_kernels, std::vector<Tensor *> *all_tensors);
 
   int InsertPostNode(const InnerContext *context, kernel::LiteKernel *kernel,
-                     std::vector<kernel::LiteKernel *> *all_kernels, std::vector<Tensor *> *all_tensors);
+                     std::vector<kernel::LiteKernel *> *trans_kernels, std::vector<Tensor *> *all_tensors);
 
  private:
   int total = 0;