Clean building warnings for arm64 platform.

mindspore/core/utils/log_adapter.h

@@ -147,7 +147,7 @@ class LogWriter {
 
   LogWriter(const LocationInfo &location, MsLogLevel log_level, SubModuleId submodule,
             ExceptionType excp_type = NoExceptionType)
-      : location_(location), log_level_(log_level), submodule_(submodule), exception_type_(excp_type) {}
+      : location_(location), log_level_(log_level), exception_type_(excp_type) {}
   ~LogWriter() = default;
 
   void operator<(const LogStream &stream) const noexcept __attribute__((visibility("default")));
@@ -161,7 +161,6 @@ class LogWriter {
 
   LocationInfo location_;
   MsLogLevel log_level_;
-  SubModuleId submodule_;
   ExceptionType exception_type_;
 
   inline static ExceptionHandler exception_handler_ = nullptr;

mindspore/lite/CMakeLists.txt

@@ -65,22 +65,21 @@ set(CMAKE_VERBOSE_MAKEFILE on)
 add_compile_definitions(USE_ANDROID_LOG)
 add_compile_definitions(NO_DLIB)
 add_compile_options(-fPIC)
-if (NOT PLATFORM_ARM64 AND NOT PLATFORM_ARM32)
-    if ("${CMAKE_BUILD_TYPE}" STREQUAL "Debug")
-        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -DDebug -g")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DDebug -g")
-        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fvisibility=default")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fvisibility=default")
-    else ()
-        ## enable for binscope for release
-        set(CMAKE_C_FLAGS "-fPIC -fPIE -D_FORTIFY_SOURCE=2 -O2 -Wall -Werror -fstack-protector-strong -Wno-attributes -Wno-deprecated-declarations ${CMAKE_C_FLAGS}")
-        set(CMAKE_CXX_FLAGS "-fPIC -fPIE -D_FORTIFY_SOURCE=2 -O2 -Wall -Werror -fstack-protector-strong -Wno-attributes -Wno-deprecated-declarations ${CMAKE_CXX_FLAGS}")
-        if (NOT WIN32)
-            set(CMAKE_SHARED_LINKER_FLAGS "-Wl,-z,relro,-z,now -Wl,-z,noexecstack ${CMAKE_SHARED_LINKER_FLAGS}")
-            set(CMAKE_EXE_LINKER_FLAGS "-Wl,-z,relro,-z,now -Wl,-z,noexecstack ${CMAKE_EXE_LINKER_FLAGS}")
-        endif()
-        string(REPLACE    " -g " " " CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
-    endif ()
+
+if ("${CMAKE_BUILD_TYPE}" STREQUAL "Debug")
+    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -DDebug -g")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DDebug -g")
+    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fvisibility=default")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fvisibility=default")
+else ()
+    ## enable for binscope for release
+    set(CMAKE_C_FLAGS "-fPIC -fPIE -D_FORTIFY_SOURCE=2 -O2 -Wall -Werror -fstack-protector-strong -Wno-attributes -Wno-deprecated-declarations -Wno-missing-braces -Wno-overloaded-virtual ${CMAKE_C_FLAGS}")
+    set(CMAKE_CXX_FLAGS "-fPIC -fPIE -D_FORTIFY_SOURCE=2 -O2 -Wall -Werror -fstack-protector-strong -Wno-attributes -Wno-deprecated-declarations -Wno-missing-braces -Wno-overloaded-virtual ${CMAKE_CXX_FLAGS}")
+    if (NOT WIN32)
+        set(CMAKE_SHARED_LINKER_FLAGS "-Wl,-z,relro,-z,now -Wl,-z,noexecstack ${CMAKE_SHARED_LINKER_FLAGS}")
+        set(CMAKE_EXE_LINKER_FLAGS "-Wl,-z,relro,-z,now -Wl,-z,noexecstack ${CMAKE_EXE_LINKER_FLAGS}")
+    endif()
+    string(REPLACE    " -g " " " CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
 endif ()
 
 if (BUILD_DEVICE)

mindspore/lite/nnacl/arithmetic_common.h

@@ -51,6 +51,8 @@ void TileOneDimension(float *inData, float *outData, int dim, size_t ndim, int *
                       int *outStrides, int *multiple);
 void ComputeStrides(int *shape, int *strides, int ndim);
 
+void CalcMultiplesAndStrides(ArithmeticParameter *param);
+
 void TileDimensions(float *data0, float *data1, float *tile_data0, float *tile_data1, ArithmeticParameter *param);
 void TileDimensionsUint8(uint8_t *data0, uint8_t *data1, uint8_t *tile_data0, uint8_t *tile_data1,
                          ArithmeticParameter *param);

mindspore/lite/nnacl/fp16/conv_fp16.c

@@ -395,7 +395,6 @@ void Conv3x3Fp16(float16_t *input_data, float16_t *transed_weight, const float16
 
   int input_batch = conv_param->input_batch_;
   for (int batch = 0; batch < input_batch; batch++) {
-    int in_batch_offset = batch * ic4 * C4NUM * conv_param->input_h_ * conv_param->input_w_;
     int tmp_out_batch_offset = batch * oc8 * C8NUM * out_w_block * out_h_block * output_unit * output_unit;
     for (int thread_id = task_id; thread_id < output_tile_count; thread_id += thread_count) {
       int start_index = thread_id * tile_num;

mindspore/lite/nnacl/fp16/pack_fp16.c

@@ -55,7 +55,6 @@ void Im2ColPackUnitFp16(float16_t *input_data, ConvParameter *conv_param, float1
   int in_w = conv_param->input_w_;
   int out_w = conv_param->output_w_;
   int channel_block = UP_DIV(in_channel, 4);
-  int kernel_plane = kernel_h * kernel_w;
 
   for (int i = 0; i < real_cal_num; i++) {
     int block_start = block_index + i;

mindspore/lite/nnacl/fp16/winograd_transform_fp16.c

@@ -607,7 +607,7 @@ void WinogradInputTransformFp16(const float16_t *input_data, float16_t *trans_in
         for (int j = 0; j < (interval_x_e - interval_x_s); j++) {
           int src_x_offset = src_y_offset + j * ic8 * C8NUM;
           int dst_x_offset = dst_y_offset + j * C8NUM;
-          float16_t *src_addr = input_data + src_x_offset;
+          const float16_t *src_addr = input_data + src_x_offset;
           float16_t *dst_addr = tmp_data + dst_x_offset;
 #ifdef ENABLE_NEON
           vst1q_f16(dst_addr, vld1q_f16(src_addr));

mindspore/lite/nnacl/int8/conv_int8.c

@@ -28,7 +28,7 @@ void IndirectGemmInt8(int8_t *dst, int32_t *tmp_dst, const int8_t *src, const in
   int32_t out_zp = conv_param->conv_quant_arg_.output_quant_args_[0].zp_;
   int32_t act_min = conv_param->conv_quant_arg_.out_act_min_[0];
   int32_t act_max = conv_param->conv_quant_arg_.out_act_max_[0];
-  int oc4 = UP_DIV(output_channel, C4NUM);
+
 #ifdef ENABLE_ARM64
   size_t asymmetric = conv_param->conv_quant_arg_.asymmetric_ & FILTER_ASYMMETRIC;
   size_t per_channel = conv_param->conv_quant_arg_.per_channel_ & FILTER_PER_CHANNEL;
@@ -36,6 +36,7 @@ void IndirectGemmInt8(int8_t *dst, int32_t *tmp_dst, const int8_t *src, const in
                        output_channel * sizeof(int8_t), input_sum, act_min, act_max, out_zp, out_multiplier,
                        shift_before, shift_after, asymmetric, per_channel);
 #else
+  int oc4 = UP_DIV(output_channel, C4NUM);
   int tile_num = conv_param->tile_num_;
   int plane_c4 = UP_DIV(kernel_plane, C4NUM);
   for (int oc = 0; oc < output_channel; oc++) {

mindspore/lite/nnacl/int8/matmul_int8.c

@@ -63,16 +63,17 @@ void RowMajor2Row16x4MajorInt8(void *src_ptr, void *dst_ptr, int row, int col) {
   for (int ri = 0; ri < row_4div; ri += C4NUM) {
     for (int ci = 0; ci < col_16div; ci += C16NUM) {
 #ifdef ENABLE_ARM64
+      size_t col_offset = col;
       int8_t *src_c = src_r + ci;
       int8_t *dst_c = dst_r + ci * C4NUM;
       asm volatile(
         "mov x10, %[src_c] \n"
         "mov x11, %[dst_c] \n"
 
-        "ld1 {v0.16b}, [x10], %[col]\n"
-        "ld1 {v1.16b}, [x10], %[col]\n"
-        "ld1 {v2.16b}, [x10], %[col]\n"
-        "ld1 {v3.16b}, [x10], %[col]\n"
+        "ld1 {v0.16b}, [x10], %[col_offset]\n"
+        "ld1 {v1.16b}, [x10], %[col_offset]\n"
+        "ld1 {v2.16b}, [x10], %[col_offset]\n"
+        "ld1 {v3.16b}, [x10], %[col_offset]\n"
 
         "st1 {v0.16b}, [x11], #16\n"
         "st1 {v1.16b}, [x11], #16\n"
@@ -80,7 +81,7 @@ void RowMajor2Row16x4MajorInt8(void *src_ptr, void *dst_ptr, int row, int col) {
         "st1 {v3.16b}, [x11], #16\n"
 
         :
-        : [ dst_c ] "r"(dst_c), [ src_c ] "r"(src_c), [ col ] "r"(col)
+        : [ dst_c ] "r"(dst_c), [ src_c ] "r"(src_c), [ col_offset ] "r"(col_offset)
         : "x10", "x11", "v0", "v1", "v2", "v3");
 #else
       MatrixPack4x16UnitInt8(src_r + ci, dst_r + ci * C4NUM, C4NUM, C16NUM, col);

mindspore/lite/nnacl/winograd_transform.c

@@ -1225,9 +1225,9 @@ void Conv3x3Uint8OutputUnit(const int32_t *gemm_out, const int32_t *bias_data, i
     ls = vld1q_s32(left_shift);
     rs = vld1q_s32(right_shift);
   } else {
-    out_multiplier = vdupq_n_s32(quant_multiplier);
-    ls = vdupq_n_s32(left_shift);
-    rs = vdupq_n_s32(right_shift);
+    out_multiplier = vdupq_n_s32(quant_multiplier[0]);
+    ls = vdupq_n_s32(left_shift[0]);
+    rs = vdupq_n_s32(right_shift[0]);
   }
   int32x4_t out_zp = vdupq_n_s32(output_zp);
   int32x4_t output_min = vdupq_n_s32(out_min);

mindspore/lite/src/common/graph_util.cc

@@ -43,7 +43,7 @@ std::vector<size_t> GetGraphInputNodes(const schema::MetaGraph *meta_graph) {
       }
     }
   }
-  return std::move(ret);
+  return ret;
 }
 
 std::vector<size_t> GetGraphOutputNodes(const schema::MetaGraph *meta_graph) {
@@ -64,7 +64,7 @@ std::vector<size_t> GetGraphOutputNodes(const schema::MetaGraph *meta_graph) {
       }
     }
   }
-  return std::move(ret);
+  return ret;
 }
 
 // NODE_ID OpNode::ID() { return id; }

mindspore/lite/src/ops/resize.cc

@@ -54,7 +54,10 @@ int Resize::InferShape(std::vector<lite::tensor::Tensor *> inputs_, std::vector<
   if (input == nullptr) {
     return 1;
   }
-  MS_ASSERT(input->shape().size() == kInputRank);
+  if (input->shape().size() != kInputRank) {
+    MS_LOG(ERROR) << "Size of input shape is wrong.";
+    return RET_ERROR;
+  }
 
   auto output = outputs_.front();
   if (output == nullptr) {

mindspore/lite/src/runtime/kernel/arm/base/prior_box.cc

@@ -40,8 +40,14 @@ int PriorBoxCPUKernel::Init() {
     return RET_NULL_PTR;
   }
 
-  MS_ASSERT(in_tensors_.size() == kInputNum);
-  MS_ASSERT(out_tensors_.size() == kOutputNum);
+  if (in_tensors_.size() != kInputNum) {
+    MS_LOG(ERROR) << "Size of input tensors is wrong.";
+    return RET_ERROR;
+  }
+  if (in_tensors_.size() != kOutputNum) {
+    MS_LOG(ERROR) << "Size of input tensors is wrong.";
+    return RET_ERROR;
+  }
 
   if (!InferShapeDone()) {
     return RET_OK;

mindspore/lite/src/runtime/kernel/arm/fp16/arithmetic_fp16.h

@@ -46,7 +46,6 @@ class ArithmeticFP16CPUKernel : public LiteKernel {
  private:
   void FreeTmpBuffer();
   int break_pos_;
-  int outside_;
   int out_thread_stride_;
   int out_count_;
   float16_t *tile_data0_ = nullptr;

mindspore/lite/src/runtime/kernel/arm/fp16/reduce_fp16.h

@@ -44,7 +44,6 @@ class ReduceFp16CPUKernel : public ReduceBaseCPUKernel {
  private:
   Reducer reducer_ = nullptr;
   std::vector<float16_t *> data_buffers_;
-  const float *src_data_ = nullptr;
   float *dst_data_ = nullptr;
   float16_t *fp16_input_ = nullptr;
   const float16_t *fp16_src_data_ = nullptr;

mindspore/lite/src/runtime/kernel/arm/fp16/split_fp16.cc

@@ -111,8 +111,8 @@ int SplitFp16CPUKernel::Run() {
       context_->allocator->Free(output_ptr_[i]);
       output_ptr_[i] = nullptr;
     }
-    return RET_OK;
   }
+  return RET_OK;
 }
 
 kernel::LiteKernel *CpuSplitFp16KernelCreator(const std::vector<lite::tensor::Tensor *> &inputs,

mindspore/lite/src/runtime/kernel/arm/fp16/transpose_fp16.cc

@@ -30,10 +30,6 @@ using mindspore::lite::RET_OP_EXECUTE_FAILURE;
 using mindspore::schema::PrimitiveType_Transpose;
 
 namespace mindspore::kernel {
-namespace {
-constexpr int kTransposeInputNum = 1;
-constexpr int kTransposeOutputNum = 1;
-}  // namespace
 int TransposeFp16CPUKernel::Init() {
   TransposeParameter *param = reinterpret_cast<TransposeParameter *>(this->op_parameter_);
   num_unit_ = static_cast<int>(in_tensors_[kInputIndex]->shape().at(param->perm_[kNHWC_H]));

mindspore/lite/src/runtime/kernel/arm/fp32/arithmetic_self.h

@@ -46,7 +46,7 @@ class ArithmeticSelfCPUKernel : public LiteKernel {
   explicit ArithmeticSelfCPUKernel(OpParameter *parameter, const std::vector<lite::tensor::Tensor *> &inputs,
                                    const std::vector<lite::tensor::Tensor *> &outputs, const lite::Context *ctx,
                                    const mindspore::lite::PrimitiveC *primitive)
-      : LiteKernel(parameter, inputs, outputs, ctx, primitive), ctx_(ctx), thread_count_(ctx->thread_num_) {
+      : LiteKernel(parameter, inputs, outputs, ctx, primitive), thread_count_(ctx->thread_num_) {
     switch (parameter->type_) {
       case PrimitiveType_Abs:
         arithmeticSelf_run_ = ElementAbs;
@@ -102,7 +102,6 @@ class ArithmeticSelfCPUKernel : public LiteKernel {
   size_t data_size_;
   ArithmeticSelfParameter *arithmeticSelfParameter_;
   ArithmeticSelfRun arithmeticSelf_run_;
-  const Context *ctx_;
   int thread_count_;
   float *in_ptr_;
   float *out_ptr_;

mindspore/lite/src/runtime/kernel/arm/fp32/constant_of_shape.cc

@@ -28,12 +28,6 @@ using mindspore::lite::RET_OK;
 using mindspore::schema::PrimitiveType_ConstantOfShape;
 
 namespace mindspore::kernel {
-
-namespace {
-constexpr int kInputNum = 1;
-constexpr int kOutputNum = 1;
-}  // namespace
-
 int ConstantOfShapeCPUKernel::Init() { return RET_OK; }
 
 int ConstantOfShapeCPUKernel::ReSize() { return RET_OK; }

mindspore/lite/src/runtime/kernel/arm/fp32/expandDims.h

@@ -32,7 +32,7 @@ class ExpandDimsCPUKernel : public LiteKernel {
   ExpandDimsCPUKernel(OpParameter *parameter, const std::vector<lite::tensor::Tensor *> &inputs,
                       const std::vector<lite::tensor::Tensor *> &outputs, const lite::Context *ctx,
                       const mindspore::lite::PrimitiveC *primitive)
-      : LiteKernel(parameter, inputs, outputs, ctx, primitive), ctx_(ctx), thread_count_(ctx->thread_num_) {}
+      : LiteKernel(parameter, inputs, outputs, ctx, primitive), thread_count_(ctx->thread_num_) {}
   ~ExpandDimsCPUKernel() override = default;
 
   int Init() override;
@@ -46,7 +46,6 @@ class ExpandDimsCPUKernel : public LiteKernel {
   size_t data_size_;
   float *in_ptr_;
   float *out_ptr_;
-  const Context *ctx_;
   int thread_count_;
 };
 }  // namespace mindspore::kernel

mindspore/lite/src/runtime/kernel/arm/fp32/fill.cc

@@ -28,12 +28,6 @@ using mindspore::lite::RET_OK;
 using mindspore::schema::PrimitiveType_Fill;
 
 namespace mindspore::kernel {
-
-namespace {
-constexpr int kInputNum = 1;
-constexpr int kOutputNum = 1;
-}  // namespace
-
 int FillCPUKernel::Init() {
   if (!InferShapeDone()) {
     return RET_OK;

mindspore/lite/src/runtime/kernel/arm/fp32/fill.h

@@ -30,7 +30,7 @@ class FillCPUKernel : public LiteKernel {
   FillCPUKernel(OpParameter *parameter, const std::vector<lite::tensor::Tensor *> &inputs,
                 const std::vector<lite::tensor::Tensor *> &outputs, const lite::Context *ctx,
                 const mindspore::lite::PrimitiveC *primitive)
-      : LiteKernel(parameter, inputs, outputs, ctx, primitive), ctx_(ctx), thread_count_(ctx->thread_num_) {}
+      : LiteKernel(parameter, inputs, outputs, ctx, primitive), thread_count_(ctx->thread_num_) {}
   ~FillCPUKernel() override = default;
 
   int Init() override;
@@ -44,7 +44,6 @@ class FillCPUKernel : public LiteKernel {
   int data_size_;
   float src_data_;
   float *out_ptr_;
-  const Context *ctx_;
   int thread_count_;
 };
 }  // namespace mindspore::kernel

mindspore/lite/src/runtime/kernel/arm/fp32/gatherNd.h

@@ -32,7 +32,7 @@ class GatherNdCPUKernel : public LiteKernel {
   GatherNdCPUKernel(OpParameter *parameter, const std::vector<lite::tensor::Tensor *> &inputs,
                     const std::vector<lite::tensor::Tensor *> &outputs, const Context *ctx,
                     const mindspore::lite::PrimitiveC *primitive)
-      : LiteKernel(parameter, inputs, outputs, ctx, primitive), ctx_(ctx), thread_count_(ctx->thread_num_) {}
+      : LiteKernel(parameter, inputs, outputs, ctx, primitive), thread_count_(ctx->thread_num_) {}
   ~GatherNdCPUKernel() override;
 
   int Init() override;
@@ -48,7 +48,6 @@ class GatherNdCPUKernel : public LiteKernel {
   int *in_offset_ = nullptr;
   float *in_ptr_;
   float *out_ptr_;
-  const Context *ctx_;
   int thread_count_;
 };
 }  // namespace mindspore::kernel

mindspore/lite/src/runtime/kernel/arm/fp32/power.h

@@ -30,7 +30,6 @@ class PowerCPUKernel : public PowerBaseCPUKernel {
                  const std::vector<lite::tensor::Tensor *> &outputs, const lite::Context *ctx,
                  const mindspore::lite::PrimitiveC *primitive)
       : PowerBaseCPUKernel(param, inputs, outputs, ctx, primitive),
-        ctx_(ctx),
         thread_count_(ctx->thread_num_),
         power_(reinterpret_cast<PowerParameter *>(op_parameter_)->power_),
         scale_(reinterpret_cast<PowerParameter *>(op_parameter_)->scale_),
@@ -43,7 +42,6 @@ class PowerCPUKernel : public PowerBaseCPUKernel {
   int RunImpl(int task_id);
 
  private:
-  const lite::Context *ctx_;
   int thread_count_;
   float power_;
   float scale_;

mindspore/lite/src/runtime/kernel/arm/fp32/range.cc

@@ -27,12 +27,6 @@ using mindspore::lite::RET_OK;
 using mindspore::schema::PrimitiveType_Range;
 
 namespace mindspore::kernel {
-
-namespace {
-constexpr int kInputNum = 0;
-constexpr int kOutputNum = 1;
-}  // namespace
-
 int RangeCPUKernel::Init() { return RET_OK; }
 
 int RangeCPUKernel::ReSize() { return RET_OK; }

mindspore/lite/src/runtime/kernel/arm/fp32/rank.cc

@@ -27,12 +27,6 @@ using mindspore::lite::RET_OK;
 using mindspore::schema::PrimitiveType_Rank;
 
 namespace mindspore::kernel {
-
-namespace {
-constexpr int kInputNum = 1;
-constexpr int kOutputNum = 1;
-}  // namespace
-
 int RankCPUKernel::Init() { return RET_OK; }
 
 int RankCPUKernel::ReSize() { return RET_OK; }

mindspore/lite/src/runtime/kernel/arm/fp32/reverse.h

@@ -31,7 +31,7 @@ class ReverseCPUKernel : public LiteKernel {
   ReverseCPUKernel(OpParameter *parameter, const std::vector<lite::tensor::Tensor *> &inputs,
                    const std::vector<lite::tensor::Tensor *> &outputs, const lite::Context *ctx,
                    const mindspore::lite::PrimitiveC *primitive)
-      : LiteKernel(parameter, inputs, outputs, ctx, primitive), ctx_(ctx), thread_count_(ctx->thread_num_) {}
+      : LiteKernel(parameter, inputs, outputs, ctx, primitive), thread_count_(ctx->thread_num_) {}
   ~ReverseCPUKernel() {
     if (tmp_ != nullptr) {
       free(tmp_);
@@ -52,7 +52,6 @@ class ReverseCPUKernel : public LiteKernel {
   int strides_[REVERSE_STRIDE_MAX_SIZE];
   int inCount_[REVERSE_STRIDE_MAX_SIZE];
   int outCount_[REVERSE_STRIDE_MAX_SIZE];
-  const Context *ctx_;
   int thread_count_;
   int *tmp_ = nullptr;
   float *in_ptr_;

mindspore/lite/src/runtime/kernel/arm/fp32/scatter_nd.cc

@@ -30,8 +30,6 @@ using mindspore::schema::PrimitiveType_ScatterND;
 
 namespace mindspore::kernel {
 namespace {
-constexpr int kScatterNDInputNum = 3;
-constexpr int kScatterNDOutputNum = 1;
 constexpr int kScatterShapeIndex = 0;
 constexpr int kScatterIndicesIndex = 1;
 constexpr int kScatterUpdateIndex = 2;

mindspore/lite/src/runtime/kernel/arm/fp32/shape.cc

@@ -26,10 +26,6 @@ using mindspore::lite::RET_OK;
 using mindspore::schema::PrimitiveType_Shape;
 
 namespace mindspore::kernel {
-namespace {
-constexpr int kShapeInputNum = 1;
-constexpr int kShapeOutputNum = 1;
-}  // namespace
 int ShapeCPUKernel::Init() { return RET_OK; }
 
 int ShapeCPUKernel::ReSize() { return RET_OK; }

mindspore/lite/src/runtime/kernel/arm/fp32/squeeze.cc

@@ -27,11 +27,6 @@ using mindspore::lite::RET_OK;
 using mindspore::schema::PrimitiveType_Squeeze;
 
 namespace mindspore::kernel {
-namespace {
-constexpr int kSqueezeInputNum = 1;
-constexpr int kSqueezeOutputNum = 1;
-}  // namespace
-
 int SqueezeCPUKernel::Init() { return RET_OK; }
 
 int SqueezeCPUKernel::ReSize() { return RET_OK; }

mindspore/lite/src/runtime/kernel/arm/fp32/transpose.cc

@@ -29,10 +29,6 @@ using mindspore::lite::RET_OP_EXECUTE_FAILURE;
 using mindspore::schema::PrimitiveType_Transpose;
 
 namespace mindspore::kernel {
-namespace {
-constexpr int kTransposeInputNum = 1;
-constexpr int kTransposeOutputNum = 1;
-}  // namespace
 int TransposeCPUKernel::Init() {
   TransposeParameter *param = reinterpret_cast<TransposeParameter *>(this->op_parameter_);
   num_unit_ = static_cast<int>(in_tensors_[kInputIndex]->shape().at(param->perm_[kNHWC_H]));

mindspore/lite/src/runtime/kernel/arm/fp32/zeroslike.cc

@@ -27,9 +27,6 @@ using mindspore::lite::RET_OK;
 using mindspore::schema::PrimitiveType_ZerosLike;
 
 namespace mindspore::kernel {
-constexpr int kInputNum = 1;
-constexpr int kOutputNum = 1;
-
 int ZerosLikeCPUKernel::Init() { return RET_OK; }
 
 int ZerosLikeCPUKernel::Run() {

mindspore/lite/src/runtime/kernel/arm/int8/add_int8.cc

@@ -92,7 +92,7 @@ int QuantizedAddCPUKernel::Run() {
     input0_data_ = static_cast<int8_t *>(ctx_->allocator->Malloc(out_tensors_.at(0)->Size()));
     input1_data_ = static_cast<int8_t *>(ctx_->allocator->Malloc(out_tensors_.at(0)->Size()));
 
-    ArithmeticParameter tile_para = {0};
+    ArithmeticParameter tile_para;
     tile_para.ndim_ = out_tensors_.at(0)->shape().size();
     for (size_t i = 0; i < tile_para.ndim_; i++) {
       tile_para.in_shape0_[i] = in_tensors_.at(0)->DimensionSize(i);

mindspore/lite/src/runtime/kernel/arm/int8/arithmetic_self_int8.h

@@ -45,7 +45,7 @@ class ArithmeticSelfInt8CPUKernel : public LiteKernel {
   explicit ArithmeticSelfInt8CPUKernel(OpParameter *parameter, const std::vector<lite::tensor::Tensor *> &inputs,
                                        const std::vector<lite::tensor::Tensor *> &outputs, const Context *ctx,
                                        const mindspore::lite::PrimitiveC *primitive)
-      : LiteKernel(parameter, inputs, outputs, ctx, primitive), ctx_(ctx), thread_count_(ctx->thread_num_) {
+      : LiteKernel(parameter, inputs, outputs, ctx, primitive), thread_count_(ctx->thread_num_) {
     switch (parameter->type_) {
       case PrimitiveType_Round:
         arithmeticSelf_run_ = Int8ElementRound;
@@ -98,7 +98,6 @@ class ArithmeticSelfInt8CPUKernel : public LiteKernel {
   size_t data_size_;
   ArithmeticSelfParameter *para_;
   ArithmeticSelfInt8Run arithmeticSelf_run_;
-  const Context *ctx_;
   int thread_count_;
   int8_t *in_ptr_;
   int8_t *out_ptr_;

mindspore/lite/src/runtime/kernel/arm/int8/div_int8.cc

@@ -104,7 +104,7 @@ int DivInt8CPUKernel::Run() {
   }
 
   if (broadcast_) {
-    ArithmeticParameter tile_para = {0};
+    ArithmeticParameter tile_para;
     tile_para.ndim_ = out_tensors_.at(0)->shape().size();
     for (size_t i = 0; i < tile_para.ndim_; i++) {
       tile_para.in_shape0_[i] = in_tensors_.at(0)->DimensionSize(i);

mindspore/lite/src/runtime/kernel/arm/int8/mul_int8.cc

@@ -77,7 +77,7 @@ int MulInt8CPUKernel::Run() {
     input0_data_ = static_cast<int8_t *>(ctx_->allocator->Malloc(out_tensors_.at(0)->Size()));
     input1_data_ = static_cast<int8_t *>(ctx_->allocator->Malloc(out_tensors_.at(0)->Size()));
 
-    ArithmeticParameter tile_para = {0};
+    ArithmeticParameter tile_para;
     tile_para.ndim_ = out_tensors_.at(0)->shape().size();
     for (size_t i = 0; i < tile_para.ndim_; i++) {
       tile_para.in_shape0_[i] = in_tensors_.at(0)->DimensionSize(i);

mindspore/lite/src/runtime/kernel/arm/int8/resize_int8.cc

@@ -30,12 +30,6 @@ using mindspore::lite::RET_NULL_PTR;
 using mindspore::lite::RET_OK;
 
 namespace mindspore::kernel {
-namespace {
-constexpr int kInputNum = 1;
-constexpr int kOutputNum = 1;
-constexpr size_t kRank = 4;
-}  // namespace
-
 int ResizeInt8CPUKernel::Init() {
   auto ret = ResizeBaseCPUKernel::Init();
   if (ret != RET_OK) {

mindspore/lite/src/runtime/kernel/arm/int8/sub_int8.cc

@@ -128,7 +128,7 @@ int SubInt8CPUKernel::Run() {
   }
 
   if (broadcast_) {
-    ArithmeticParameter tile_para = {0};
+    ArithmeticParameter tile_para;
     tile_para.ndim_ = out_tensors_.at(0)->shape().size();
     for (size_t i = 0; i < tile_para.ndim_; i++) {
       tile_para.in_shape0_[i] = in_tensors_.at(0)->DimensionSize(i);

mindspore/lite/src/runtime/kernel/arm/int8/unsqueeze_int8.h

@@ -30,7 +30,7 @@ class Unsqueezeint8CPUKernel : public LiteKernel {
   Unsqueezeint8CPUKernel(OpParameter *parameter, const std::vector<lite::tensor::Tensor *> &inputs,
                          const std::vector<lite::tensor::Tensor *> &outputs, const Context *ctx,
                          const mindspore::lite::PrimitiveC *primitive)
-      : LiteKernel(parameter, inputs, outputs, ctx, primitive), ctx_(ctx), thread_count_(ctx->thread_num_) {
+      : LiteKernel(parameter, inputs, outputs, ctx, primitive), thread_count_(ctx->thread_num_) {
     Unsq_para_ = reinterpret_cast<UnSqueezeParameter *>(op_parameter_);
     Unsq_para_->thread_count_ = op_parameter_->thread_num_;
   }
@@ -42,14 +42,12 @@ class Unsqueezeint8CPUKernel : public LiteKernel {
   int DoUnsqueeze(int task_id);
 
  private:
-  UnSqueezeQuantArg *quant_Unsqueeze_parm_;
   UnSqueezeParameter *Unsq_para_;
   int thread_sz_count_;
   int thread_sz_stride_;
   int data_size_;
   float *in_ptr_;
   float *out_ptr_;
-  const Context *ctx_;
   int thread_count_;
 };
 }  // namespace mindspore::kernel

mindspore/lite/src/runtime/kernel/opencl/subgraph_opencl_kernel.h

@@ -57,7 +57,6 @@ class SubGraphOpenCLKernel : public SubGraphKernel {
                             std::vector<std::vector<kernel::LiteKernel *>> *out_kernels, bool is_from);
 
  private:
-  SubGraphOpenCLParameter *subgraph_ocl_parameter_;
   lite::opencl::OpenCLAllocator *allocator_;
   std::vector<lite::tensor::Tensor *> in_convert_tensors_;
   std::vector<lite::tensor::Tensor *> out_convert_tensors_;

mindspore/lite/tools/time_profile/CMakeLists.txt

@@ -17,5 +17,10 @@ else()
     target_link_libraries(timeprofile mindspore-lite pthread)
 endif()
 
-install(TARGETS timeprofile
-        RUNTIME DESTINATION ${MAIN_DIR}/time_profile COMPONENT ${COMPONENT_NAME})
\ No newline at end of file
+if (PLATFORM_ARM32 OR PLATFORM_ARM64)
+    install(TARGETS timeprofile
+            RUNTIME DESTINATION ${MAIN_DIR}/time_profile COMPONENT ${COMPONENT_NAME})
+else()
+    install(TARGETS timeprofile
+            RUNTIME DESTINATION ${MAIN_DIR}/time_profile COMPONENT ${RUN_X86_COMPONENT_NAME})
+endif()