!48644 [MS][LITE] code clean

Merge pull request !48644 from jianghui58/codex_fuzz_master

mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/intrinsics/avx/common_utils.h

@@ -34,6 +34,9 @@ extern "C" {
 #endif
 #endif
 
+#define AVX_ACT_RELU 1
+#define AVX_ACT_RELU6 3
+
 // Signed saturating Add
 __m128i _mm_adds_epi32(__m128i a, __m128i b);
 
@@ -89,7 +92,7 @@ static inline void ActBlock8Avx(__m256 *v1, __m256 *v2, __m256 *v3, __m256 *v4,
   __m256 relu6 = _mm256_set1_ps(6.0);
   __m256 zero = _mm256_setzero_ps();
   switch (relu_type) {
-    case 3:
+    case AVX_ACT_RELU6:
       *v1 = _mm256_min_ps(*v1, relu6);
       *v2 = _mm256_min_ps(*v2, relu6);
       *v3 = _mm256_min_ps(*v3, relu6);
@@ -98,7 +101,7 @@ static inline void ActBlock8Avx(__m256 *v1, __m256 *v2, __m256 *v3, __m256 *v4,
       *v6 = _mm256_min_ps(*v6, relu6);
       *v7 = _mm256_min_ps(*v7, relu6);
       *v8 = _mm256_min_ps(*v8, relu6);
-    case 1:
+    case AVX_ACT_RELU:
       *v1 = _mm256_max_ps(*v1, zero);
       *v2 = _mm256_max_ps(*v2, zero);
       *v3 = _mm256_max_ps(*v3, zero);

mindspore/lite/src/litert/huffman_decode.cc

@@ -76,7 +76,9 @@ STATUS HuffmanDecode::DoHuffmanDecode(const std::string &input_str, void *decode
 }
 
 STATUS HuffmanDecode::RebuildHuffmanTree(std::string keys, std::string codes, const HuffmanNodePtr &root) {
-  HuffmanNodePtr cur_node, tmp_node, new_node;
+  HuffmanNodePtr cur_node;
+  HuffmanNodePtr tmp_node;
+  HuffmanNodePtr new_node;
 
   auto huffman_keys = Str2Vec(std::move(keys));
   auto huffman_codes = Str2Vec(std::move(codes));

mindspore/lite/src/litert/kernel/cpu/base/strided_slice.cc

@@ -166,7 +166,8 @@ int StridedSliceCPUKernel::FastRunImpl(int task_id) {
     if (cal_axis_num > cal_num_per_thread_) {
       cal_axis_num = cal_num_per_thread_;
     }
-    FastStride(cur_in_ptr, cur_out_ptr, cal_axis_num, param_->strides_[split_axis_], 1, inner_size_, 0);
+    FastStride(cur_in_ptr, cur_out_ptr, static_cast<uint32_t>(cal_axis_num), param_->strides_[split_axis_], 1,
+               inner_size_, 0);
   }
   return RET_OK;
 }
@@ -271,7 +272,7 @@ int StridedSliceCPUKernel::SoftCopyInputToOutput() {
       auto out_start = output_data + task_id * block_size;
       auto copy_size = block_size;
       if (task_id == (thread_num_ - 1)) {
-        copy_size = size - task_id * block_size;
+        copy_size = size - static_cast<size_t>(task_id) * block_size;
       }
       (void)memcpy(out_start, in_start, copy_size);
       return RET_OK;

mindspore/lite/src/litert/kernel/cpu/fp16_grad/convolution_fp16_grad_input.cc

@@ -85,7 +85,8 @@ int ConvolutionGradInputCPUKernelFp16::DoExecute(int task_id) {
   auto w_addr = reinterpret_cast<float16_t *>(input_w->data());
   auto dx_addr = reinterpret_cast<float16_t *>(out_dx->data());
 
-  int i, j;
+  int i;
+  int j;
   int in_ch = conv_param->input_channel_;
   int in_h = conv_param->input_h_;
   int nweights = input_w->ElementsNum();

mindspore/lite/src/litert/kernel/cpu/fp32/resize_fp32.cc

@@ -206,7 +206,8 @@ int ResizeCPUKernel::RunImpl(int task_id) {
                                    calculate_, coordinate_transform_mode_, task_id, op_parameter_->thread_num_);
     }
     case static_cast<int>(schema::ResizeMethod_CUBIC): {
-      float *line_buffer = static_cast<float *>(line_buffer_) + new_width_ * c * sizeof(float) * task_id;
+      float *line_buffer = static_cast<float *>(line_buffer_) +
+                           static_cast<size_t>(new_width_ * c) * sizeof(float) * static_cast<size_t>(task_id);
       return ResizeBicubic(input_data, output_data, input_shape.data(), out_tensors_.at(0)->shape().data(),
                            coordinate_.y_tops_, coordinate_.x_lefts_, static_cast<float *>(y_weights_),
                            static_cast<float *>(x_weights_), line_buffer, h_begin, h_end);

mindspore/lite/src/litert/kernel/cpu/fp32_grad/convolution_grad_input.cc

@@ -86,7 +86,8 @@ int ConvolutionGradInputCPUKernel::DoExecute(int task_id) {
   auto w_addr = reinterpret_cast<float *>(input_w->MutableData());
   auto dx_addr = reinterpret_cast<float *>(out_dx->MutableData());
 
-  int i, j;
+  int i;
+  int j;
   int batch = conv_param->output_batch_;
   int groups = conv_param->group_;
   int in_ch = conv_param->input_channel_;

mindspore/lite/src/litert/kernel/cpu/fp32_grad/deconvolution_grad_filter.cc

@@ -83,7 +83,8 @@ int DeConvolutionGradFilterCPUKernel::DoExecute(int task_id) {
   auto dw_addr = reinterpret_cast<float *>(out_dw->data());
   CHECK_NULL_RETURN(dw_addr);
 
-  int i, j;
+  int i;
+  int j;
   int out_w = conv_param->output_w_;
   int out_h = conv_param->output_h_;
   int out_ch = conv_param->output_channel_;

mindspore/lite/src/litert/lite_session.cc

@@ -1745,6 +1745,8 @@ const char *lite::LiteSession::LoadModelByPath(const std::string &file, mindspor
   char *lite_buf = nullptr;
   auto buf_model_type = LoadModelByBuff(model_buf, buf_size, &lite_buf, size, model_type);
   if (buf_model_type == mindspore::ModelType::kUnknownType || lite_buf == nullptr) {
+    delete[] model_buf;
+    model_buf = nullptr;
     return nullptr;
   }
 

mindspore/lite/tools/common/string_util.cc

@@ -84,7 +84,9 @@ std::vector<std::string> SplitStringToVector(const std::string &raw_str, const c
 }
 
 std::vector<std::string> SplitStringToVector(const std::string &raw_str, const std::string &delimiter) {
-  size_t pos_start = 0, pos_end, delim_len = delimiter.length();
+  size_t pos_start = 0;
+  size_t pos_end = 0;
+  size_t delim_len = delimiter.length();
   std::string token;
   std::vector<std::string> res;
 

mindspore/lite/tools/converter/quantizer/bias_correction_strategy.cc

@@ -179,7 +179,7 @@ MSKernelCallBack BiasCorrectionStrategy::GetCPUFloatBeforeCallBack() {
       MS_LOG(INFO) << "tensor type is " << tensor.DataType();
       return true;
     }
-    size_t elem_count = tensor.ElementNum();
+    size_t elem_count = static_cast<size_t>(tensor.ElementNum());
     MS_CHECK_GT(elem_count, 0, false);
     std::vector<float> fp32_op_input(elem_count);
     auto ret =

mindspore/lite/tools/optimizer/fisson/fisson_util.cc

@@ -60,7 +60,10 @@ std::vector<int64_t> GetSplitPadList(const api::SharedPtr<ops::Conv2DFusion> &or
     return {};
   }
   std::vector<int64_t> new_pad_list;
-  int64_t pad_up = 0, pad_down = 0, pad_left = 0, pad_right = 0;
+  int64_t pad_up = 0;
+  int64_t pad_down = 0;
+  int64_t pad_left = 0;
+  int64_t pad_right = 0;
   int64_t pad_h_all =
     (output_h - 1) * ori_conv_prim->get_stride().at(kIndexH) + (kernel_h - 1) * dilation_h + 1 - input_h;
   int64_t pad_w_all =