fix matmul quantizationt

mindspore/lite/src/dequant.cc

@@ -18,9 +18,10 @@
 #include <memory>
 #include "src/dequant.h"
 #include "src/huffman_decode.h"
+#include "src/ops/matmul.h"
 
 namespace mindspore::lite {
-float *DequantUtil::DequantWeight(lite::Tensor *input_tensor) {
+float *DequantUtil::DequantWeight(lite::Tensor *input_tensor, bool channel_first) {
   MS_ASSERT(input_tensor != nullptr);
   if (input_tensor->data_type() != kNumberTypeInt8 && input_tensor->data_type() != kNumberTypeInt16) {
     MS_LOG(ERROR) << "Conv weight input type error." << input_tensor->data_type();
@@ -31,9 +32,9 @@ float *DequantUtil::DequantWeight(lite::Tensor *input_tensor) {
     return nullptr;
   }
   if (input_tensor->data_type() == kNumberTypeInt16) {
-    return DequantData<int16_t>(input_tensor);
+    return DequantData<int16_t>(input_tensor, channel_first);
   } else {
-    return DequantData<int8_t>(input_tensor);
+    return DequantData<int8_t>(input_tensor, channel_first);
   }
 }
 
@@ -65,19 +66,35 @@ int DequantUtil::UnPackToInt(const schema::Tensor *input_tensor, void *unpack_in
   return RET_OK;
 }
 
-std::map<Tensor *, std::pair<TypeId, void *>> DequantUtil::DequantTensor(const std::vector<Tensor *> &in_tensors,
+std::map<Tensor *, std::pair<TypeId, void *>> DequantUtil::DequantTensor(const mindspore::lite::PrimitiveC *primitive,
+                                                                         const std::vector<Tensor *> &in_tensors,
                                                                          TypeId data_type, bool need_restore) {
   std::map<Tensor *, std::pair<TypeId, void *>> tensor_origin_data;
   if (data_type == TypeId::kNumberTypeFloat32 || data_type == TypeId::kNumberTypeFloat16) {
+    auto input_i = 0;
     for (auto weight_tensor : in_tensors) {
       MS_ASSERT(weight_tensor != nullptr);
+      input_i++;
+      auto channel_first = true;
+      if ((schema::PrimitiveType)primitive->Type() == schema::PrimitiveType_MatMul &&
+          weight_tensor->shape().size() == 2) {
+        auto param = reinterpret_cast<mindspore::lite::MatMul *>(const_cast<mindspore::lite::PrimitiveC *>(primitive));
+        if (input_i == 1) {
+          channel_first = !param->GetTransposeA();
+        } else if (input_i == 2) {
+          channel_first = param->GetTransposeB();
+        } else {
+          MS_LOG(WARNING) << "unexpected input_i";
+        }
+      }
+
       auto *restore_data = weight_tensor->data_c();
       auto restore_type = weight_tensor->data_type();
       bool dequant_flag = !weight_tensor->quant_params().empty() && weight_tensor->quant_params().front().inited &&
                           restore_data != nullptr &&
                           (restore_type == kNumberTypeInt8 || restore_type == kNumberTypeInt16);
       if (dequant_flag) {
-        auto *dequant_weight = DequantUtil::DequantWeight(weight_tensor);
+        auto *dequant_weight = DequantUtil::DequantWeight(weight_tensor, channel_first);
         if (dequant_weight == nullptr) {
           MS_LOG(ERROR) << "dequant data is nullptr.";
           return tensor_origin_data;

mindspore/lite/src/dequant.h

@@ -29,17 +29,18 @@
 namespace mindspore::lite {
 class DequantUtil {
  public:
-  static float *DequantWeight(lite::Tensor *input_tensor);
+  static float *DequantWeight(lite::Tensor *input_tensor, bool);
 
   static int UnPackToInt(const schema::Tensor *input_tensor, void *weight_unpack_data);
 
-  static std::map<Tensor *, std::pair<TypeId, void *>> DequantTensor(const std::vector<Tensor *> &in_tensors,
+  static std::map<Tensor *, std::pair<TypeId, void *>> DequantTensor(const mindspore::lite::PrimitiveC *primitive,
+                                                                     const std::vector<Tensor *> &in_tensors,
                                                                      TypeId data_type, bool need_restore = true);
 
   static void RestoreTensorData(const std::map<Tensor *, std::pair<TypeId, void *>> &tensor_origin_data_map);
 
   template <typename ST, typename DT = float>
-  static DT *DequantData(lite::Tensor *input_tensor) {
+  static DT *DequantData(lite::Tensor *input_tensor, bool channel_first = true) {
     const auto *quant_datas = static_cast<const ST *>(input_tensor->MutableData());
     if (quant_datas == nullptr) {
       MS_LOG(ERROR) << "Get quant tensor failed.";
@@ -65,6 +66,13 @@ class DequantUtil {
       }
     } else if (input_tensor->quant_params().size() != kPerTensor) {
       auto channels = static_cast<size_t>(input_tensor->Batch());
+      if (!channel_first) {
+        if (input_tensor->shape().size() != 2) {
+          MS_LOG(ERROR) << "unexpected shape size: " << input_tensor->shape().size();
+          return nullptr;
+        }
+        channels = input_tensor->shape()[1];
+      }
       if (input_tensor->quant_params().size() != channels) {
         MS_LOG(ERROR) << "Quant param not equal channel num " << input_tensor->quant_params().size() << channels;
         free(dequant_datas);
@@ -83,8 +91,12 @@ class DequantUtil {
           var_corr = 1;
         }
         for (size_t j = 0; j < per_channel_size; j++) {
-          auto dequant_data = (quant_datas[per_channel_size * i + j] - zero_point) * scale;
+          auto index = per_channel_size * i + j;
-          dequant_datas[per_channel_size * i + j] = static_cast<DT>(dequant_data * var_corr + mean_corr);
+          if (!channel_first) {
+            index = channels * j + i;
+          }
+          auto dequant_data = (quant_datas[index] - zero_point) * scale;
+          dequant_datas[index] = static_cast<DT>(dequant_data * var_corr + mean_corr);
         }
       }
     } else {

mindspore/lite/src/scheduler.cc

@@ -223,7 +223,8 @@ kernel::LiteKernel *Scheduler::FindBackendKernel(const std::vector<Tensor *> &in
   if (mindspore::lite::IsSupportFloat16() &&
       ((context_->IsCpuFloat16Enabled() && data_type == kNumberTypeFloat32) || data_type == kNumberTypeFloat16)) {
     kernel::KernelKey fp16_cpu_desc{desc.arch, kNumberTypeFloat16, desc.type};
-    auto tensor_origin_data_map = DequantUtil::DequantTensor(in_tensors, fp16_cpu_desc.data_type, need_restore);
+    auto tensor_origin_data_map =
+      DequantUtil::DequantTensor(primitive, in_tensors, fp16_cpu_desc.data_type, need_restore);
     auto *kernel =
       KernelRegistry::GetInstance()->GetKernel(in_tensors, out_tensors, primitive, context_, fp16_cpu_desc);
     DequantUtil::RestoreTensorData(tensor_origin_data_map);
@@ -237,7 +238,7 @@ kernel::LiteKernel *Scheduler::FindBackendKernel(const std::vector<Tensor *> &in
     MS_LOG(DEBUG) << "Get fp16 op failed, back to fp32 op.";
     desc.data_type = kNumberTypeFloat32;
   }
-  auto tensor_origin_data_map = DequantUtil::DequantTensor(in_tensors, desc.data_type, need_restore);
+  auto tensor_origin_data_map = DequantUtil::DequantTensor(primitive, in_tensors, desc.data_type, need_restore);
   auto *kernel = KernelRegistry::GetInstance()->GetKernel(in_tensors, out_tensors, primitive, context_, desc);
   DequantUtil::RestoreTensorData(tensor_origin_data_map);
   if (kernel != nullptr) {

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

@@ -358,15 +358,15 @@ static bool SearchUpperBound(const std::vector<float> &data, const size_t &index
   return true;
 }
 
-static float CalPercentile(const std::vector<float> &datas, const int &outlier_percent) {
+static float CalPercentile(const std::vector<float> &data, const int &outlier_percent) {
-  const int size = datas.size();
+  const int size = data.size();
   float val = outlier_percent / 100.0 * size;
   int index = std::ceil(val);
   float result;
   if (index - val > 0) {
-    result = datas.at(index - 1);
+    result = data.at(index - 1);
   } else {
-    result = (datas.at(index - 1) + datas.at(index)) / 2;
+    result = (data.at(index - 1) + data.at(index)) / 2;
   }
   return result;
 }
@@ -522,11 +522,78 @@ std::vector<std::vector<int>> DataToVectors(const string &str) {
   return result;
 }
 
-STATUS ParseConfigFile(std::string config_file, PostQuantConfig *post_quant_config) {
+void ParseInputShape(PostQuantConfig *post_quant_config, std::string raw_shape) {
-  if (post_quant_config == nullptr) {
+  MS_ASSERT(post_quant_config != nullptr);
-    MS_LOG(ERROR) << "post_quant_config is null.";
+  auto ind = raw_shape.find('/');
-    return RET_PARAM_INVALID;
+  while (ind != std::string::npos) {
+    auto shape = raw_shape.substr(0, ind);
+    Trim(&shape);
+    post_quant_config->input_shapes.push_back(DataToVectors(shape));
+    raw_shape = raw_shape.substr(ind + 1);
+    Trim(&raw_shape);
+    ind = raw_shape.find('/');
   }
+  if (!raw_shape.empty()) {
+    post_quant_config->input_shapes.push_back(DataToVectors(raw_shape));
+  }
+}
+
+void ParseImagePath(PostQuantConfig *post_quant_config, std::string raw_image_paths) {
+  MS_ASSERT(post_quant_config != nullptr);
+  auto ind = raw_image_paths.find(',');
+  while (ind != std::string::npos) {
+    auto image_path = raw_image_paths.substr(0, ind);
+    Trim(&image_path);
+    post_quant_config->image_paths.push_back(image_path);
+    raw_image_paths = raw_image_paths.substr(ind + 1);
+    Trim(&raw_image_paths);
+    ind = raw_image_paths.find(',');
+  }
+  post_quant_config->image_paths.push_back(raw_image_paths);
+}
+
+void ParseBatchCount(PostQuantConfig *post_quant_config, std::string value) {
+  MS_ASSERT(post_quant_config != nullptr);
+  post_quant_config->batch_count = std::stoul(value);
+}
+
+void ParseThreadNum(PostQuantConfig *post_quant_config, std::string value) {
+  MS_ASSERT(post_quant_config != nullptr);
+  post_quant_config->thread_num = std::stoul(value);
+}
+
+void ParseMethodX(PostQuantConfig *post_quant_config, const std::string &value) {
+  MS_ASSERT(post_quant_config != nullptr);
+  if (value != kMethodKL && value != kMethodMaxMin && value != kMethodOutlier) {
+    MS_LOG(WARNING) << "unsupported method_x: " << value << ". Use default value.";
+  } else {
+    post_quant_config->method_x = value;
+  }
+}
+
+void ParseMixed(PostQuantConfig *post_quant_config, std::string value) {
+  MS_ASSERT(post_quant_config != nullptr);
+  std::for_each(value.begin(), value.end(), ::tolower);
+  if (value == "true") {
+    post_quant_config->mixed = true;
+  }
+}
+
+void ParseMeanErrorThreshold(PostQuantConfig *post_quant_config, std::string value) {
+  MS_ASSERT(post_quant_config != nullptr);
+  post_quant_config->mean_error_threshold = std::stof(value);
+}
+
+void ParseBiasCorrection(PostQuantConfig *post_quant_config, std::string value) {
+  MS_ASSERT(post_quant_config != nullptr);
+  std::for_each(value.begin(), value.end(), ::tolower);
+  if (value == "true") {
+    post_quant_config->bias_correction = true;
+  }
+}
+
+STATUS ParseConfigFile(std::string config_file, PostQuantConfig *post_quant_config) {
+  MS_ASSERT(post_quant_config != nullptr);
 
   if (config_file.empty() || config_file.length() > PATH_MAX) {
     MS_LOG(ERROR) << "invalid config path!";
@@ -552,6 +619,26 @@ STATUS ParseConfigFile(std::string config_file, PostQuantConfig *post_quant_conf
     MS_LOG(ERROR) << "config file open failed: " << config_file;
     return RET_PARAM_INVALID;
   }
+
+  std::string INPUT_SHAPES = "input_shapes";
+  std::string IMAGE_PATH = "image_path";
+  std::string BATCH_COUNT = "batch_count";
+  std::string THREAD_NUM = "thread_num";
+  std::string METHOD_X = "method_x";
+  std::string MIXED = "mixed";
+  std::string MEAN_ERROR_THRESHOLD = "mean_error_threshold";
+  std::string BIAS_CORRECTION = "bias_correction";
+
+  std::map<std::string, std::function<void(PostQuantConfig *, std::string)>> value_parser;
+  value_parser[INPUT_SHAPES] = ParseInputShape;
+  value_parser[IMAGE_PATH] = ParseImagePath;
+  value_parser[BATCH_COUNT] = ParseBatchCount;
+  value_parser[THREAD_NUM] = ParseThreadNum;
+  value_parser[METHOD_X] = ParseMethodX;
+  value_parser[MIXED] = ParseMixed;
+  value_parser[MEAN_ERROR_THRESHOLD] = ParseMeanErrorThreshold;
+  value_parser[BIAS_CORRECTION] = ParseBiasCorrection;
+
   std::string line;
   while (std::getline(fs, line)) {
     Trim(&line);
@@ -567,54 +654,9 @@ STATUS ParseConfigFile(std::string config_file, PostQuantConfig *post_quant_conf
     auto value = line.substr(index + 1);
     Trim(&key);
     Trim(&value);
-    if (key == "image_path") {
+    auto it = value_parser.find(key);
-      auto &raw_image_paths = value;
+    if (it != value_parser.end()) {
-      auto ind = raw_image_paths.find(',');
+      it->second(post_quant_config, value);
-      while (ind != std::string::npos) {
-        auto image_path = raw_image_paths.substr(0, ind);
-        Trim(&image_path);
-        post_quant_config->image_paths.push_back(image_path);
-        raw_image_paths = raw_image_paths.substr(ind + 1);
-        Trim(&raw_image_paths);
-        ind = raw_image_paths.find(',');
-      }
-      post_quant_config->image_paths.push_back(raw_image_paths);
-    } else if (key == "batch_count") {
-      post_quant_config->batch_count = std::stoul(value);
-    } else if (key == "thread_num") {
-      post_quant_config->thread_num = std::stoul(value);
-    } else if (key == "method_x") {
-      if (value != kMethodKL && value != kMethodMaxMin && value != kMethodOutlier) {
-        MS_LOG(WARNING) << "unsupported method_x: " << value << ". Use default value.";
-      } else {
-        post_quant_config->method_x = value;
-      }
-    } else if (key == "bias_correction") {
-      std::for_each(value.begin(), value.end(), ::tolower);
-      if (value == "true") {
-        post_quant_config->bias_correction = true;
-      }
-    } else if (key == "mixed") {
-      std::for_each(value.begin(), value.end(), ::tolower);
-      if (value == "true") {
-        post_quant_config->mixed = true;
-      }
-    } else if (key == "mean_error_threshold") {
-      post_quant_config->mean_error_threshold = std::stof(value);
-    } else if (key == "input_shapes") {
-      auto &raw_shape = value;
-      auto ind = raw_shape.find('/');
-      while (ind != std::string::npos) {
-        auto shape = raw_shape.substr(0, ind);
-        Trim(&shape);
-        post_quant_config->input_shapes.push_back(DataToVectors(shape));
-        raw_shape = raw_shape.substr(ind + 1);
-        Trim(&raw_shape);
-        ind = raw_shape.find('/');
-      }
-      if (!raw_shape.empty()) {
-        post_quant_config->input_shapes.push_back(DataToVectors(raw_shape));
-      }
     } else {
       MS_LOG(WARNING) << "unsupported parameter: " << key;
     }
@@ -881,4 +923,24 @@ STATUS UpdateTensorDataAndSize(ParamValueLitePtr weight, void *quant_datas, int
   return RET_OK;
 }
 
+void GetMaxMinPerchannel(int channels, int one_filter_size, int i, int elem_count, float *raw_datas,
+                         bool channel_at_first, float *desired_max, float *desired_min) {
+  float min = FLT_MAX;
+  float max = -FLT_MAX;
+  // find min and max
+  for (int j = 0; j < one_filter_size; j++) {
+    auto index = j + i * one_filter_size;
+    if (!channel_at_first) {
+      index = j * channels + i;
+    }
+    if (index >= elem_count) {
+      MS_LOG(ERROR) << "over flow!";
+    }
+    min = std::min(min, raw_datas[index]);
+    max = std::max(max, raw_datas[index]);
+  }
+  *desired_max = max;
+  *desired_min = min;
+}
+
 }  // namespace mindspore::lite::quant

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

@@ -107,6 +107,9 @@ std::vector<int8_t> KMeans(float *data, size_t elem_count, size_t k, size_t epoc
 
 STATUS UpdateTensorDataAndSize(ParamValueLitePtr weight, void *quant_datas, int new_size);
 
+void GetMaxMinPerchannel(int channels, int one_filter_size, int i, int elem_count, float *raw_datas,
+                         bool channel_at_first, float *desired_max, float *desired_min);
+
 template <typename T>
 T QuantizeData(const float originData, const schema::QuantParamT *quantParam) {
   MS_ASSERT(quantParam != nullptr);
@@ -163,11 +166,19 @@ template <typename T>
 STATUS DoPerChannelQuant(const ParamValueLitePtr &weight, const QuantType &quant_type,
                          std::vector<schema::QuantParamT> *quant_params, const int &quant_max, const int &quant_min,
                          const size_t &bit_num, const bool &k_means, std::vector<T> *quant_datas,
-                         std::vector<float> *dequant_datas) {
+                         std::vector<float> *dequant_datas, bool channel_at_first = true) {
   auto dims = weight->tensor_shape();
   size_t elem_count = weight->tensor_shape_size();
   auto *raw_datas = static_cast<float *>(weight->tensor_addr());
   auto channels = dims[0];
+  if (!channel_at_first) {
+    if (dims.size() != 2) {
+      MS_LOG(ERROR) << "unexpected dims size: " << dims.size();
+      channel_at_first = true;
+    } else {
+      channels = dims[1];
+    }
+  }
   if (channels == 0) {
     MS_LOG(ERROR) << "channels is zero";
     return RET_ERROR;
@@ -181,16 +192,7 @@ STATUS DoPerChannelQuant(const ParamValueLitePtr &weight, const QuantType &quant
   for (int i = 0; i < channels; i++) {
     float min = FLT_MAX;
     float max = -FLT_MAX;
-    // find min and max
+    GetMaxMinPerchannel(channels, one_filter_size, i, elem_count, raw_datas, channel_at_first, &max, &min);
-    for (size_t j = 0; j < one_filter_size; j++) {
-      auto index = j + i * one_filter_size;
-      if (index >= elem_count) {
-        MS_LOG(ERROR) << "over flow!";
-        return RET_ERROR;
-      }
-      min = std::min(min, raw_datas[index]);
-      max = std::max(max, raw_datas[index]);
-    }
     schema::QuantParamT quant_param;
     STATUS status = CalQuantizationParams(&quant_param, min, max, false, quant_max, quant_min, bit_num);
     if (status != RET_OK) {
@@ -202,10 +204,10 @@ STATUS DoPerChannelQuant(const ParamValueLitePtr &weight, const QuantType &quant
     double average_raw = 0;
     for (uint32_t j = 0; j < one_filter_size; j++) {
       auto index = j + i * one_filter_size;
-      if (index >= elem_count) {
+      if (!channel_at_first) {
-        MS_LOG(ERROR) << "over flow!";
+        index = j * channels + i;
-        return RET_ERROR;
       }
+      MS_ASSERT(index < elem_count);
       float raw_data = raw_datas[index];
       auto quant_data = QuantizeData<T>(raw_data, quant_param, quant_max, quant_min);
       (*quant_datas)[index] = quant_data;
@@ -226,10 +228,10 @@ STATUS DoPerChannelQuant(const ParamValueLitePtr &weight, const QuantType &quant
       double variance_raw = 0;
       for (uint32_t j = 0; j < one_filter_size; j++) {
         auto index = j + i * one_filter_size;
-        if (index >= elem_count) {
+        if (!channel_at_first) {
-          MS_LOG(ERROR) << "over flow!";
+          index = j * channels + i;
-          return RET_ERROR;
         }
+        MS_ASSERT(index < elem_count);
         variance_dequant += std::pow((*dequant_datas)[index] - average_dequant, 2);
         variance_raw += std::pow(raw_datas[index] - average_raw, 2);
       }
@@ -339,20 +341,26 @@ STATUS QuantFilter(const ParamValueLitePtr &weight, const std::shared_ptr<Primit
 
   std::vector<schema::QuantParamT> quant_params;
   size_t elem_count = weight->tensor_shape_size();
-  auto *raw_datas = static_cast<float *>(weight->tensor_addr());
+  auto *raw_data = static_cast<float *>(weight->tensor_addr());
-  if (raw_datas == nullptr) {
+  if (raw_data == nullptr) {
     MS_LOG(ERROR) << "rawDatas is nullptr";
     return RET_ERROR;
   }
 
-  std::vector<T> quant_datas(elem_count);
+  std::vector<T> quant_data(elem_count);
   std::vector<float> dequant_datas(elem_count);
   int ret = RET_OK;
   if (per_channel) {
-    // notice: assume Con2D\DepthwiseConv2D's weight format are same: KHWC
+    bool channel_at_first = true;
+    auto op_type = (schema::PrimitiveType)primitive_c->Type();
+    if (op_type == schema::PrimitiveType_MatMul && weight->tensor_shape().size() == 2) {
+      auto matmul_op = primitive_c->primitiveT()->value.AsMatMul();
+      MS_ASSERT(matmul_op != nullptr);
+      channel_at_first = !(index == 1 && !matmul_op->transposeB);
+    }
     // channel at first
-    ret = DoPerChannelQuant<T>(weight, quant_type, &quant_params, quant_max, quant_min, bit_num, k_means, &quant_datas,
+    ret = DoPerChannelQuant<T>(weight, quant_type, &quant_params, quant_max, quant_min, bit_num, k_means, &quant_data,
-                               &dequant_datas);
+                               &dequant_datas, channel_at_first);
     if (ret == RET_CONTINUE) {
       return ret;
     } else if (ret != RET_OK) {
@@ -360,7 +368,7 @@ STATUS QuantFilter(const ParamValueLitePtr &weight, const std::shared_ptr<Primit
       return ret;
     }
   } else {
-    ret = DoPerLayerQuant<T>(weight, quant_type, &quant_params, quant_max, quant_min, bit_num, k_means, &quant_datas);
+    ret = DoPerLayerQuant<T>(weight, quant_type, &quant_params, quant_max, quant_min, bit_num, k_means, &quant_data);
     if (ret != RET_OK) {
       MS_LOG(ERROR) << "Do per layer quant failed.";
       return ret;
@@ -376,7 +384,7 @@ STATUS QuantFilter(const ParamValueLitePtr &weight, const std::shared_ptr<Primit
   }
 #else
   // do bit pack
-  ret = DoBitPack(weight, bit_num, quant_datas);
+  ret = DoBitPack(weight, bit_num, quant_data);
   if (ret != RET_OK) {
     MS_LOG(ERROR) << "Do bit pack failed.";
     return ret;

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

@@ -127,6 +127,7 @@ STATUS WeightQuantizer::DoMulQuantize(CNodePtr cnode) {
   auto already_quant = false;
   ParamValueLitePtr param_value = nullptr;
   ParameterPtr param_node = nullptr;
+  int index = 0;
   for (size_t i = 1; i < cnode->size(); i++) {
     auto inputNode = cnode->input(i);
     if (inputNode->isa<Parameter>()) {
@@ -146,6 +147,7 @@ STATUS WeightQuantizer::DoMulQuantize(CNodePtr cnode) {
           param_value = nullptr;
           continue;
         } else {
+          index = i;
           break;
         }
       }
@@ -169,11 +171,11 @@ STATUS WeightQuantizer::DoMulQuantize(CNodePtr cnode) {
 
   auto status = RET_ERROR;
   if (type_id_ == kNumberTypeInt8) {
-    status =
+    status = QuantFilter<int8_t>(param_value, primitive_c, QuantType_WeightQuant, quant_max_, quant_min_, bit_num_,
-      QuantFilter<int8_t>(param_value, primitive_c, QuantType_WeightQuant, quant_max_, quant_min_, bit_num_, true);
+                                 true, index - 1);
   } else if (type_id_ == kNumberTypeInt16) {
-    status =
+    status = QuantFilter<int16_t>(param_value, primitive_c, QuantType_WeightQuant, quant_max_, quant_min_, bit_num_,
-      QuantFilter<int16_t>(param_value, primitive_c, QuantType_WeightQuant, quant_max_, quant_min_, bit_num_, true);
+                                  true, index - 1);
   }
   if (status == RET_CONTINUE) {
     return RET_OK;