Fix the bug of matmul_int8's pre-process

mindspore/lite/nnacl/int8/matmul_int8.c

@@ -225,12 +225,15 @@ void RowMajor2Col16x4Major(int8_t *src, int row, int col, int8_t *dst, int row_1
 }
 
 // dst: weight_zp * input_row_sums
-void CalcInputSums(int8_t *input, int row, int col, int weight_zp, int *dst) {
+void CalcInputSums(int8_t *input, int row, int col, int weight_zp, int *dst, DataOrder order) {
   for (int r = 0; r < row; ++r) {
     int sum = 0;
     for (int c = 0; c < col; ++c) {
-      int src_idx = r * col + c;
-      sum += input[src_idx];
+      if (order == RowMajor) {
+        sum += input[r * col + c];
+      } else {
+        sum += input[c * row + r];
+      }
     }
     sum *= weight_zp;
     dst[r] = sum;
@@ -238,12 +241,16 @@ void CalcInputSums(int8_t *input, int row, int col, int weight_zp, int *dst) {
 }
 
 // dst: bias + depth*input_zp*weight_zp - input_zp*weight_col_sums
-void CalcWeightBiasSums(int8_t *weight, int row, int col, int input_zp, int weight_zp, int *bias, int *dst) {
+void CalcWeightBiasSums(int8_t *weight, int row, int col, int input_zp, int weight_zp, int *bias, int *dst,
+                        DataOrder order) {
   for (int c = 0; c < col; ++c) {
     int sum = 0;
     for (int r = 0; r < row; ++r) {
-      int src_idx = r * col + c;
-      sum += weight[src_idx];
+      if (order == RowMajor) {
+        sum += weight[r * col + c];
+      } else {
+        sum += weight[c * row + r];
+      }
     }
     dst[c] = row * input_zp * weight_zp - input_zp * sum;
     if (bias) {

mindspore/lite/nnacl/int8/matmul_int8.h

@@ -37,8 +37,9 @@ void RowMajor2Row16x4MajorInt8(void *src_ptr, void *dst_ptr, int row, int col);
 
 void RowMajor2Row4x16Major(int8_t *src, int row, int col, int8_t *dst, int col_16);
 void RowMajor2Col16x4Major(int8_t *src, int row, int col, int8_t *dst, int row_16);
-void CalcInputSums(int8_t *a, int row, int col, int b_zp, int *dst);
-void CalcWeightBiasSums(int8_t *b, int row, int col, int a_zp, int b_zp, int *bias, int *dst);
+void CalcInputSums(int8_t *input, int row, int col, int weight_zp, int *dst, DataOrder order);
+void CalcWeightBiasSums(int8_t *weight, int row, int col, int input_zp, int weight_zp, int *bias, int *dst,
+                        DataOrder order);
 void MatmulInt8(const int8_t *a, const int8_t *b, int8_t *dst, const int *a_sums, const int *bias, int act_min,
                 int act_max, int out_zp, int multiplier, int left_shift, int right_shift, int row, int col, int deep16,
                 int stride);

mindspore/lite/nnacl/op_base.h

@@ -55,6 +55,11 @@ typedef enum LiteDataType {
   kDataTypeInt8,
 } LiteDataType;
 
+typedef enum DataOrder {
+  RowMajor,
+  ColMajor,
+} DataOrder;
+
 typedef struct OpParameter {
   char name_[100];
   int type_;

mindspore/lite/src/runtime/kernel/arm/int8/fullconnection_int8.cc

@@ -90,7 +90,7 @@ int FullconnectionInt8CPUKernel::ReSize() {
                                     quant_params_.output.zp_, quant_params_.output.scale_, &quant_params_.out_act_min,
                                     &quant_params_.out_act_max);
   CalcWeightBiasSums(weight_data, fc_param_->deep_, fc_param_->col_, quant_params_.input.zp_, quant_params_.weight.zp_,
-                     bias_ptr_, weight_bias_sums_);
+                     bias_ptr_, weight_bias_sums_, ColMajor);
   return RET_OK;
 }
 
@@ -136,7 +136,7 @@ int FullconnectionInt8CPUKernel::Run() {
   }
   auto input_ptr = reinterpret_cast<int8_t *>(in_tensors_[0]->Data());
   RowMajor2Row4x16Major(input_ptr, fc_param_->row_, fc_param_->deep_, a_r4x16_ptr_, d16_);
-  CalcInputSums(input_ptr, fc_param_->row_, fc_param_->deep_, quant_params_.weight.zp_, input_sums_);
+  CalcInputSums(input_ptr, fc_param_->row_, fc_param_->deep_, quant_params_.weight.zp_, input_sums_, RowMajor);
   ParallelLaunch(THREAD_POOL_DEFAULT, FcInt8Run, this, thread_count_);
   return RET_OK;
 }

mindspore/lite/src/runtime/kernel/arm/int8/matmul_int8.cc

@@ -140,18 +140,21 @@ int MatmulInt8CPUKernel::Run() {
 
     if (params_->a_transpose_) {
       RowMajor2Col16x4Major(cur_a_ptr, params_->deep_, params_->row_, a_r4x16_ptr_, d16_);
+      CalcInputSums(cur_a_ptr, params_->row_, params_->deep_, quant_params_.weight.zp_, input_sums_, ColMajor);
     } else {
       RowMajor2Row4x16Major(cur_a_ptr, params_->row_, params_->deep_, a_r4x16_ptr_, d16_);
+      CalcInputSums(cur_a_ptr, params_->row_, params_->deep_, quant_params_.weight.zp_, input_sums_, RowMajor);
     }
     if (params_->b_transpose_) {
       RowMajor2Row4x16Major(cur_b_ptr, params_->col_, params_->deep_, b_c16x4_ptr_, d16_);
+      CalcWeightBiasSums(cur_b_ptr, params_->deep_, params_->col_, quant_params_.input.zp_, quant_params_.weight.zp_,
+                         NULL, weight_bias_sums_, ColMajor);
     } else {
       RowMajor2Col16x4Major(cur_b_ptr, params_->deep_, params_->col_, b_c16x4_ptr_, d16_);
+      CalcWeightBiasSums(cur_b_ptr, params_->deep_, params_->col_, quant_params_.input.zp_, quant_params_.weight.zp_,
+                         NULL, weight_bias_sums_, RowMajor);
     }
     c_ptr_ = c_ptr + i * c_stride;
-    auto &q = quant_params_;
-    CalcInputSums(cur_a_ptr, params_->row_, params_->deep_, q.weight.zp_, input_sums_);
-    CalcWeightBiasSums(cur_b_ptr, params_->deep_, params_->col_, q.input.zp_, q.weight.zp_, NULL, weight_bias_sums_);
     ret = ParallelLaunch(THREAD_POOL_DEFAULT, MatmulInt8Run, this, thread_count_);
     if (ret != RET_OK) {
       MS_LOG(ERROR) << "MatmulInt8Run error: [" << ret << "]";

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

@@ -226,7 +226,8 @@ STATUS AwareQuantizer::DoQuantize() {
     }
     STATUS status;
     if (GetCNodeTType(*node) == schema::PrimitiveType_Conv2D ||
-        GetCNodeTType(*node) == schema::PrimitiveType_DepthwiseConv2D) {
+        GetCNodeTType(*node) == schema::PrimitiveType_DepthwiseConv2D ||
+        GetCNodeTType(*node) == schema::PrimitiveType_FullConnection) {
       auto inputIndexes = node->inputIndex;
       if (inputIndexes.size() < 2) {
         MS_LOG(ERROR) << node->name.c_str()