fix post training code

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

@@ -565,7 +565,7 @@ PostTrainingQuantizer::PostTrainingQuantizer(FuncGraphPtr graph, string path, in
 }
 
 STATUS PostTrainingQuantizer::DoQuantInput(double scale, int32_t zeropoint, struct MaxMin *max_min,
-                                           std::shared_ptr<PrimitiveC> lite_primitive, const size_t &index) {
+                                           std::shared_ptr<PrimitiveC> lite_primitive) {
   schema::QuantParamT quant_param;
   quant_param.scale = scale;
   quant_param.zeroPoint = zeropoint;
@@ -573,8 +573,9 @@ STATUS PostTrainingQuantizer::DoQuantInput(double scale, int32_t zeropoint, stru
   quant_param.min = max_min->min;
   quant_param.numBits = bit_num;
   quant_param.narrowRange = false;
+  quant_param.inited = true;
   std::vector<schema::QuantParamT> quant_params = {quant_param};
-  lite_primitive->SetInputQuantParam(index, quant_params);
+  lite_primitive->AddInputQuantParam(quant_params);
   return RET_OK;
 }
 
@@ -589,7 +590,7 @@ STATUS PostTrainingQuantizer::DoQuantOutput(double scale, int zeropoint, struct
   quant_param.narrowRange = false;
   quant_param.inited = true;
   std::vector<schema::QuantParamT> quant_params = {quant_param};
-  lite_primitive->SetOutputQuantParam(0, quant_params);
+  lite_primitive->AddOutputQuantParam(quant_params);
   return RET_OK;
 }
 
@@ -642,7 +643,7 @@ STATUS PostTrainingQuantizer::DoBiasQuant(AnfNodePtr bias, std::shared_ptr<Primi
   auto bias_param = std::dynamic_pointer_cast<ParamValueLite>(bias_default_param);
 
   auto active_weight_quant_params = primitive_c->GetInputQuantParams();
-  if (active_weight_quant_params.size() != 3) {
+  if (active_weight_quant_params.size() != 2) {
     MS_LOG(ERROR) << "unexpected active_weight_quant_params size: " << active_weight_quant_params.size();
     return RET_ERROR;
   }
@@ -721,7 +722,7 @@ STATUS PostTrainingQuantizer::DoBiasQuant(AnfNodePtr bias, std::shared_ptr<Primi
     auto quant_data = (int32_t)std::round(raw_datas[i] / bias_scale_tmp);
     quant_datas[i] = quant_data;
   }
-  primitive_c->SetInputQuantParam(2, quant_params);
+  primitive_c->AddInputQuantParam(quant_params);
   auto ret = memcpy_s(bias_param->tensor_addr(), bias_param->tensor_size(), quant_datas, shape_size * sizeof(int32_t));
   if (ret != EOK) {
     MS_LOG(ERROR) << "memcpy_s failed.";
@@ -832,19 +833,19 @@ STATUS PostTrainingQuantizer::QuantNode() {
         }
         if (input_cnode_primitive_c->IsOutputQuantParamsInited()) {
           auto quant_param = input_cnode_primitive_c->GetOutputQuantParams().front();
-          primitive_c->SetInputQuantParam(i - 1, quant_param);
+          primitive_c->AddInputQuantParam(quant_param);
         } else {
           // do input quant
           double scale = input_scale[cnode];
           int32_t zp = input_zero_point[cnode];
-          DoQuantInput(scale, zp, &input_min_max[cnode], primitive_c, i - 1);
+          DoQuantInput(scale, zp, &input_min_max[cnode], primitive_c);
         }
       }
     } else {
       // do input quant
       double scale = input_scale[cnode];
       int32_t convInputzeropoint = input_zero_point[cnode];
-      DoQuantInput(scale, convInputzeropoint, &input_min_max[cnode], primitive_c, 0);
+      DoQuantInput(scale, convInputzeropoint, &input_min_max[cnode], primitive_c);
       // do weight quant
       auto weight = cnode->input(2);
       bool perchannel = per_channel_;
@@ -916,6 +917,12 @@ STATUS PostTrainingQuantizer::PreProcess() {
     if (strategy.CanOpPostQuantized(anf)) {
       calibrator_->AddQuantizedOp(cnode);
     }
+    auto primitive_c = GetValueNode<std::shared_ptr<PrimitiveC>>(cnode->input(0));
+    if (primitive_c == nullptr) {
+      MS_LOG(ERROR) << cnode->fullname_with_scope() << " primitive is null";
+      continue;
+    }
+    primitive_c->ClearInputOutputQuantParam();
   }
   return RET_OK;
 }

mindspore/lite/tools/converter/quantizer/post_training_quantizer.h

@@ -107,7 +107,7 @@ class PostTrainingQuantizer : public Quantizer {
   STATUS QuantNode();
 
   STATUS DoQuantInput(double scale, int32_t zeropoint, struct MaxMin *max_min,
-                      std::shared_ptr<PrimitiveC> lite_primitive, const size_t &index);
+                      std::shared_ptr<PrimitiveC> lite_primitive);
   STATUS DoQuantOutput(double scale, int32_t zeropoint, struct MaxMin *max_min, std::shared_ptr<PrimitiveC>);
 
   STATUS DoWeightQuant(AnfNodePtr weight, std::shared_ptr<PrimitiveC> primitive_c, bool perchannel);

mindspore/lite/tools/converter/quantizer/quantize_util.h

@@ -283,7 +283,11 @@ STATUS QuantFilter(ParamValueLitePtr weight, std::shared_ptr<PrimitiveC> primiti
     MS_LOG(ERROR) << "quant_params empty";
     return RET_ERROR;
   }
-  primitive_c->SetInputQuantParam(WEIGHT_INDEX, quant_params);
+  if (quantType == QuantType_PostTraining) {
+    primitive_c->AddInputQuantParam(quant_params);
+  } else {
+    primitive_c->SetInputQuantParam(WEIGHT_INDEX, quant_params);
+  }
   return RET_OK;
 }