!4668 Not Use ParamValueLite

Merge pull request !4668 from xutianchun/quant_ch

mindspore/lite/src/ir/primitive_t_value.h

@@ -47,7 +47,6 @@ class PrimitiveTValue : public Value {
     }
   }
 
-
   void SetInputQuantParam(const std::vector<std::vector<schema::QuantParamT>> &input_quant_param) {
     this->input_quant_param_ = input_quant_param;
   }
@@ -56,6 +55,10 @@ class PrimitiveTValue : public Value {
     this->output_quant_param_ = output_quant_param;
   }
 
+  void ClearInputOutputQuantParam() {
+    input_quant_param_.clear();
+    output_quant_param_.clear();
+  }
 
   void AddInputQuantParam(std::vector<schema::QuantParamT> quant_param) {
     this->input_quant_param_.emplace_back(quant_param);

mindspore/lite/src/param_value_lite.h

@@ -25,16 +25,6 @@
 #include "ir/dtype/type_id.h"
 
 namespace mindspore {
-struct AnfQuantParam {
-  double scale;
-  int32_t zeroPoint;
-  double min;
-  double max;
-  bool narrowRange;
-  bool inited;
-  int32_t numBits;
-  AnfQuantParam() : scale(1.0), zeroPoint(0), min(0.0), max(0.0), narrowRange(false), numBits(8), inited(false) {}
-};
 class ParamValueLite : public Value {
  public:
   ParamValueLite() : tensor_addr_(nullptr), tensor_size_(0) {}
@@ -59,10 +49,6 @@ class ParamValueLite : public Value {
     }
     return size;
   }
-  std::vector<std::unique_ptr<AnfQuantParam>> &quant_param() { return quant_params_; }
-  void set_quant_param(std::unique_ptr<AnfQuantParam> &quant_param) {
-    quant_params_.emplace_back(std::move(quant_param));
-  }
 
   bool operator==(const Value &other) const override {
     return this == &other;
@@ -73,7 +59,6 @@ class ParamValueLite : public Value {
   size_t tensor_size_;
   std::vector<int> tensor_shape_;
   TypeId type_id_;
-  std::vector<std::unique_ptr<AnfQuantParam>> quant_params_;
 };
 
 using ParamValueLitePtr = std::shared_ptr<ParamValueLite>;

mindspore/lite/tools/anf_exporter/anf_exporter.cc

@@ -159,16 +159,6 @@ int AnfExporter::ConvertQuantParam(const std::unique_ptr<schema::MetaGraphT> &me
           primitive->GetPrimitiveT()->value.AsQuantDTypeCast()->dstT == kNumberTypeFloat32)) {
       tensor_output->dataType = kNumberTypeInt8;
     }
-    //      // TensorType
-    //      valuePtr = primitive->GetAttr(kInputTensorDataType);
-    //      if (valuePtr != nullptr) {
-    //        MS_LOG(INFO) << "node: " << node->name << " input tensor data
-    //        type: " << GetValue<int>(valuePtr); for (auto input :
-    //        node->inputIndex) {
-    //          auto tensor = subGraph->allTensors[input].get();
-    //          tensor->dataType = kNumberTypeUInt8;
-    //        }
-    //      }
   }
   return RET_OK;
 }
@@ -295,18 +285,6 @@ int AnfExporter::ConvertInputParameter(const std::shared_ptr<AnfNode> input_anod
     paramTensor->nodeType = schema::NodeType_ValueNode;
     paramTensor->data.resize(paramValue->tensor_size());
     memcpy(paramTensor->data.data(), paramValue->tensor_addr(), paramValue->tensor_size());
-    for (auto &ite : paramValue->quant_param()) {
-      auto quantPar = std::make_unique<schema::QuantParamT>();
-      quantPar->scale = ite->scale;
-      quantPar->zeroPoint = ite->zeroPoint;
-      quantPar->min = ite->zeroPoint;
-      quantPar->max = ite->max;
-      quantPar->narrowRange = ite->narrowRange;
-      quantPar->inited = ite->inited;
-      quantPar->numBits = ite->numBits;
-      paramTensor->quantParams.emplace_back(std::move(quantPar));
-      paramTensor->dataType = paramValue->tensor_type();
-    }
   }
   node_id_map_[paramNode->fullname_with_scope()] = meta_graphT->allTensors.size();
   output_cnode->inputIndex.emplace_back(meta_graphT->allTensors.size());

mindspore/lite/tools/anf_importer/import_from_meta_graphT.cc

@@ -61,17 +61,17 @@ int AnfImporterFromMetaGraphT::ConverterConstTensor() {
       param_value->set_tensor_addr(tensor_data);
       param_value->set_tensor_size(size);
     }
-    if (!tensor->quantParams.empty()) {
-      std::unique_ptr<AnfQuantParam> quantParam = std::make_unique<AnfQuantParam>();
-      quantParam->scale = tensor->quantParams[0]->scale;
-      quantParam->zeroPoint = tensor->quantParams[0]->zeroPoint;
-      quantParam->min = tensor->quantParams[0]->min;
-      quantParam->max = tensor->quantParams[0]->max;
-      quantParam->narrowRange = tensor->quantParams[0]->narrowRange;
-      quantParam->numBits = tensor->quantParams[0]->numBits;
-      quantParam->inited = tensor->quantParams[0]->inited;
-      param_value->set_quant_param(quantParam);
-    }
+//    if (!tensor->quantParams.empty()) {
+//      std::unique_ptr<AnfQuantParam> quantParam = std::make_unique<AnfQuantParam>();
+//      quantParam->scale = tensor->quantParams[0]->scale;
+//      quantParam->zeroPoint = tensor->quantParams[0]->zeroPoint;
+//      quantParam->min = tensor->quantParams[0]->min;
+//      quantParam->max = tensor->quantParams[0]->max;
+//      quantParam->narrowRange = tensor->quantParams[0]->narrowRange;
+//      quantParam->numBits = tensor->quantParams[0]->numBits;
+//      quantParam->inited = tensor->quantParams[0]->inited;
+//      param_value->set_quant_param(quantParam);
+//    }
     parameter->set_default_param(param_value);
     AddNode(i, parameter);
   }

mindspore/lite/tools/converter/converter.cc

@@ -31,7 +31,6 @@
 #include "tools/anf_exporter/anf_exporter.h"
 #include "tools/anf_importer/import_from_protobuf.h"
 #include "tools/converter/parser/onnx/onnx.pb.h"
-#include "tools/converter/quantizer/weight_quantizer.h"
 #include "tools/converter/quantizer/post_training_quantizer.h"
 #include "tools/converter/quantizer/quant_cast.h"
 

mindspore/lite/tools/converter/quantizer/CMakeLists.txt

@@ -7,7 +7,6 @@ add_library(quantizer_mid OBJECT
     ${CMAKE_CURRENT_SOURCE_DIR}/calc_quant_param.cc
     ${CMAKE_CURRENT_SOURCE_DIR}/quantizer.cc
     ${CMAKE_CURRENT_SOURCE_DIR}/aware_quantizer.cc
-    ${CMAKE_CURRENT_SOURCE_DIR}/weight_quantizer.cc
     ${CMAKE_CURRENT_SOURCE_DIR}/quantize_util.cc
     ${CMAKE_CURRENT_SOURCE_DIR}/general_bitpacking.cc
     ${CMAKE_CURRENT_SOURCE_DIR}/post_training_quantizer.cc

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

@@ -510,7 +510,6 @@ STATUS PostTrainingQuantizer::DoQuantInput(double scale, int zeropoint, struct M
   quant_param.narrowRange = false;
   std::vector<schema::QuantParamT> quant_params = {quant_param};
   lite_primitive->AddInputQuantParam(quant_params);
-  // p->AddAttr("quant_input_dataType", MakeValue((int)DataType_DT_FLOAT));
   return RET_OK;
 }
 
@@ -528,51 +527,67 @@ STATUS PostTrainingQuantizer::DoQuantOutput(double scale, int zeropoint, struct
   quant_param.narrowRange = false;
   std::vector<schema::QuantParamT> quant_params = {quant_param};
   lite_primitive->AddOutputQuantParam(quant_params);
-  // p->AddAttr("quant_output_dataType", MakeValue((int)DataType_DT_FLOAT));
   return RET_OK;
 }
 
-STATUS PostTrainingQuantizer::DoWeightQuant(AnfNodePtr node) {
+STATUS PostTrainingQuantizer::DoWeightQuant(AnfNodePtr weight, std::shared_ptr<PrimitiveTValue> primitiveT_value,
+                                            bool depthwise) {
   // const vector<int> dims = filter->dims;
   // perlayer
-  if (!node->isa<Parameter>()) {
+  if (!weight->isa<Parameter>()) {
     MS_LOG(ERROR) << "not a parameter";
     return RET_PARAM_INVALID;
   }
-  auto parameter = std::dynamic_pointer_cast<Parameter>(node);
+  auto parameter = std::dynamic_pointer_cast<Parameter>(weight);
   ParamValueLitePtr paramValue = std::dynamic_pointer_cast<ParamValueLite>(parameter->default_param());
-  auto status = QuantFilter(paramValue, QuantType_PostTraining, quant_max, quant_min, bit_num, per_channel_);
+  auto status = QuantFilter(paramValue, primitiveT_value, QuantType_PostTraining, quant_max, quant_min, bit_num,
+                            per_channel_, depthwise);
   if (status != RET_OK) {
     MS_LOG(ERROR) << "QuantFilter failed: " << status;
     return status;
   }
+  // set dtype
+  auto abstractBase = parameter->abstract();
+  if (abstractBase == nullptr) {
+    MS_LOG(ERROR) << "Abstract of parameter is nullptr, " << parameter->name();
+    return RET_ERROR;
+  }
+  if (!utils::isa<abstract::AbstractTensorPtr>(abstractBase)) {
+    MS_LOG(ERROR) << "Abstract of parameter should be anstract tensor, " << parameter->name();
+    return RET_ERROR;
+  }
+  auto abstractTensor = utils::cast<abstract::AbstractTensorPtr>(abstractBase);
+  abstractTensor->element()->set_type(TypeIdToType(kNumberTypeInt8));
   return RET_OK;
 }
 
-STATUS PostTrainingQuantizer::DoBiasQuant(std::shared_ptr<PrimitiveTValue> input, AnfNodePtr weight, AnfNodePtr bias) {
-  if (input == nullptr || weight == nullptr || bias == nullptr) {
+STATUS PostTrainingQuantizer::DoBiasQuant(AnfNodePtr bias, std::shared_ptr<PrimitiveTValue> primitiveT_value) {
+  if (primitiveT_value == nullptr || bias == nullptr) {
     MS_LOG(ERROR) << "null pointer!";
     return RET_NULL_PTR;
   }
 
-  ParameterPtr weightParameterPtr = std::dynamic_pointer_cast<Parameter>(weight);
-  auto default_param = weightParameterPtr->default_param();
-  auto weight_param = std::dynamic_pointer_cast<ParamValueLite>(default_param);
-  // std::vector<std::unique_ptr<mindspore::QuantParamT>> weight_quant_params = weight_param->get_quant_params();
-
-  ParameterPtr biasParameterPtr = std::dynamic_pointer_cast<Parameter>(bias);
-  auto bias_default_param = biasParameterPtr->default_param();
+  auto bias_parameter_ptr = std::dynamic_pointer_cast<Parameter>(bias);
+  auto bias_default_param = bias_parameter_ptr->default_param();
   auto bias_param = std::dynamic_pointer_cast<ParamValueLite>(bias_default_param);
 
+  auto active_weight_quant_params = primitiveT_value->GetInputQuantParams();
+  if (active_weight_quant_params.size() != 2) {
+    MS_LOG(ERROR) << "unexpected active_weight_quant_params size: " << active_weight_quant_params.size();
+    return RET_ERROR;
+  }
+
+  auto active_params = active_weight_quant_params[0];
+  auto weight_params = active_weight_quant_params[1];
+
   vector<double> input_scales;
   vector<double> filter_scales;
   vector<double> bias_scales;
-  auto quant_params = input->GetInputQuantParams();
-  size_t sizeX = quant_params.size();
+  size_t sizeX = active_params.size();
   for (size_t i = 0; i < sizeX; i++) {
-    input_scales.emplace_back(quant_params[i].front().scale);
+    input_scales.emplace_back(active_params[i].scale);
   }
-  size_t sizeY = weight_param->quant_param().size();
+  size_t sizeY = weight_params.size();
   if (sizeX != sizeY) {
     if (sizeX > 1 && sizeY > 1) {
       MS_LOG(ERROR) << "input and filter's scale count cannot match!";
@@ -580,8 +595,7 @@ STATUS PostTrainingQuantizer::DoBiasQuant(std::shared_ptr<PrimitiveTValue> input
     }
   }
   for (size_t i = 0; i < sizeY; i++) {
-    auto scale = weight_param->quant_param()[i]->scale;
-    filter_scales.push_back(scale);
+    filter_scales.emplace_back(weight_params[i].scale);
   }
   size_t size = std::max(sizeX, sizeY);
   for (size_t i = 0; i < size; i++) {
@@ -593,20 +607,22 @@ STATUS PostTrainingQuantizer::DoBiasQuant(std::shared_ptr<PrimitiveTValue> input
   size_t shape_size = bias_param->tensor_shape_size();
 
   // set bias quant param
-  bias_param->quant_param().clear();
+  vector<schema::QuantParamT> quant_params;
   for (size_t i = 0; i < bias_scales.size(); i++) {
-    std::unique_ptr<AnfQuantParam> param(new (std::nothrow) AnfQuantParam());
-    param->scale = bias_scales[i];
-    param->zeroPoint = 0;
-    bias_param->quant_param().emplace_back(std::move(param));
+    schema::QuantParamT quant_param;
+    quant_param.scale = bias_scales[i];
+    quant_param.zeroPoint = 0;
+    quant_param.inited = true;
+    quant_params.emplace_back(quant_param);
   }
+  primitiveT_value->AddInputQuantParam(quant_params);
   // quant bias data
   int32_t *quant_datas = new (std::nothrow) int32_t[shape_size];
   if (quant_datas == nullptr) {
     MS_LOG(ERROR) << "null pointer dereferencing.";
     return RET_NULL_PTR;
   }
-  float *raw_datas = reinterpret_cast<float *>(bias_param->tensor_addr());
+  float *raw_datas = static_cast<float *>(bias_param->tensor_addr());
   double bias_scale_tmp;
   for (size_t i = 0; i < shape_size; i++) {
     if (bias_scales.size() == 1) {
@@ -625,38 +641,21 @@ STATUS PostTrainingQuantizer::DoBiasQuant(std::shared_ptr<PrimitiveTValue> input
     return RET_ERROR;
   }
   delete[] quant_datas;
-  bias_param->set_tensor_type(kNumberTypeInt32);
+  // set dtype
+  auto abstractBase = bias_parameter_ptr->abstract();
+  if (abstractBase == nullptr) {
+    MS_LOG(ERROR) << "Abstract of parameter is nullptr, " << bias_parameter_ptr->name();
+    return RET_ERROR;
+  }
+  if (!utils::isa<abstract::AbstractTensorPtr>(abstractBase)) {
+    MS_LOG(ERROR) << "Abstract of parameter should be anstract tensor, " << bias_parameter_ptr->name();
+    return RET_ERROR;
+  }
+  auto abstractTensor = utils::cast<abstract::AbstractTensorPtr>(abstractBase);
+  abstractTensor->element()->set_type(TypeIdToType(kNumberTypeInt32));
   return RET_OK;
 }
 
-// STATUS PostTrainingQuantizer::reformatConvWeight(GraphDefT *graph) {
-//   for (auto &subGraph : graphDefT->subgraphs) {
-//      for (auto iter = subGraph->nodes.begin(); iter != subGraph->nodes.end(); iter++) {
-//          OpDefT *node = (*iter).get();
-//          bool isConv = false;
-//          kTransFilterType tansType;
-//          if ((*node).attr.type == OpT_Conv2D) {
-//              tansType = kKCHW2HWCK;
-//              isConv = true;
-//          }
-//          else if ((*node).attr.type == OpT_DepthwiseConv2D) {
-//              tansType = kCKHW2HWCK;
-//              isConv = true;
-//          }
-//          if (isConv) {
-//              auto status =  TransFilterFormat<uint8_t>(&(*subGraph.get()->allTensors.at(node->inputIndex[1])),
-//                                                        tansType);
-//              if (status != RET_OK) {
-//                  return status;
-//              }
-//              TensorDefT *weight = subGraph->allTensors.at(node->inputIndex[1]).get();
-//              weight->format = Format_HWCK;
-//              PostBitPack(weight, bitNum);
-//          }
-//      }
-//  }
-//}
-
 STATUS PostTrainingQuantizer::QuantNode() {
   auto input_min_max = this->calibrator_->GetMinMax(this->calibrator_->GetInputDivergInfo());
   auto input_scale = this->calibrator_->GetResult(this->calibrator_->GetInputDivergInfo());
@@ -682,7 +681,7 @@ STATUS PostTrainingQuantizer::QuantNode() {
       primitiveT_value->SetQuantType(schema::QuantType_QUANT_NONE);
       continue;
     }
-    auto input_vec = cnode->inputs();
+    primitiveT_value->ClearInputOutputQuantParam();
     auto op_name = cnode->fullname_with_scope();
     auto op_type = primitiveT_value->GetPrimitiveT()->value.type;
     MS_LOG(INFO) << "OpName: " << op_name;
@@ -711,11 +710,12 @@ STATUS PostTrainingQuantizer::QuantNode() {
       DoQuantInput(scale, convInputzeropoint, &input_min_max[cnode], primitiveT_value);
       // do weight quant
       auto weight = cnode->input(2);
-      DoWeightQuant(weight);
+      bool depthwise = op_type == PrimitiveType_DeDepthwiseConv2D;
+      DoWeightQuant(weight, primitiveT_value, depthwise);
       // do bias quant
       if (cnode->inputs().size() == 4) {
         auto bias = cnode->input(3);
-        DoBiasQuant(primitiveT_value, weight, bias);
+        DoBiasQuant(bias, primitiveT_value);
       }
     }
     // do output quant

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

@@ -65,8 +65,8 @@ class PostTrainingQuantizer : public Quantizer {
   STATUS DoQuantize(FuncGraphPtr funcGraph) override;
 
   size_t bit_num;
-  int quant_max{127};
-  int quant_min{-128};
+  int quant_max{INT8_MAX};
+  int quant_min{INT8_MIN};
 
  private:
   bool per_channel_;
@@ -96,9 +96,9 @@ class PostTrainingQuantizer : public Quantizer {
   STATUS DoQuantInput(double scale, int32_t zeropoint, struct MaxMin *max_min, std::shared_ptr<PrimitiveTValue>);
   STATUS DoQuantOutput(double scale, int32_t zeropoint, struct MaxMin *max_min, std::shared_ptr<PrimitiveTValue>);
 
-  STATUS DoWeightQuant(AnfNodePtr node);
+  STATUS DoWeightQuant(AnfNodePtr weight, std::shared_ptr<PrimitiveTValue> primitiveT_value, bool depthwise);
 
-  STATUS DoBiasQuant(std::shared_ptr<PrimitiveTValue> input, AnfNodePtr weight, AnfNodePtr bias);
+  STATUS DoBiasQuant(AnfNodePtr bias, std::shared_ptr<PrimitiveTValue> primitiveT_value);
 };
 
 struct DivergInfo;

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

@@ -99,7 +99,9 @@ bool QuantStrategy::CanOpPostQuantized(AnfNodePtr &node) const {
     schema::PrimitiveType_Nchw2Nhwc, schema::PrimitiveType_Nhwc2Nchw,
     schema::PrimitiveType_Conv2D,    schema::PrimitiveType_DepthwiseConv2D,
     schema::PrimitiveType_Add,       schema::PrimitiveType_Pooling,
-    schema::PrimitiveType_Concat,    /*schema::PrimitiveType_SoftMax,*/ schema::PrimitiveType_Reshape,
+    schema::PrimitiveType_Concat,    /*schema::PrimitiveType_SoftMax,*/
+    schema::PrimitiveType_Reshape,   /*schema::PrimitiveType_FullConnection,*/
+    schema::PrimitiveType_MatMul,
     schema::PrimitiveType_Activation};
   return IsContain(uint8OpList, type);
 }
@@ -191,7 +193,7 @@ void CalFakeNode(const AnfNodePtr &inTensor) {
   // }
 }
 
-STATUS CalQuantizationParams(std::unique_ptr<AnfQuantParam> &quantParam, double mMin, double mMax, bool narrowRange,
+STATUS CalQuantizationParams(schema::QuantParamT *quantParam, double mMin, double mMax, bool narrowRange,
                              int quant_max, int quant_min, int num_bits) {
   MS_ASSERT(quantParam != nullptr);
   if (mMin > 0.0f) {
@@ -306,133 +308,178 @@ STATUS CalQuantizationParams(schema::QuantParamT *quantParam, double mMin, doubl
   return RET_OK;
 }
 
-STATUS QuantFilter(ParamValueLitePtr &weightPtr, QuantType quantType, int quant_max, int quant_min, size_t bitNum,
-                   bool per_channel) {
-  auto dims = weightPtr->tensor_shape();
-  if (dims.size() != 4) {
-    MS_LOG(ERROR) << "weight dims size error: " << dims.size() << " Back to per layer.";
-    per_channel = false;
-  } else {
-    uint32_t channels = dims[0];
-    if (channels == 0) {
-      MS_LOG(ERROR) << "channels is 0";
-      return RET_ERROR;
+STATUS QuantFilter(ParamValueLitePtr weight, std::shared_ptr<PrimitiveTValue> primitiveT_value, QuantType quantType,
+                   int quant_max, int quant_min, size_t bitNum, bool per_channel, bool depth_wise) {
+  auto dims = weight->tensor_shape();
+  if (per_channel) {
+    if (dims.size() != 4) {
+      MS_LOG(ERROR) << "weight dims size error: " << dims.size() << " Back to per layer.";
+      per_channel = false;
+    } else {
+      uint32_t channels = dims[0];
+      if (channels == 0) {
+        MS_LOG(ERROR) << "channels is 0";
+        return RET_ERROR;
+      }
     }
   }
 
+  vector<schema::QuantParamT> quant_params;
+  size_t elem_count = weight->tensor_shape_size();
+  auto *raw_datas = static_cast<float *>(weight->tensor_addr());
+  if (raw_datas == nullptr) {
+    MS_LOG(ERROR) << "rawDatas is nullptr";
+    return RET_ERROR;
+  }
+  vector<int8_t> quant_datas(elem_count);
+
   if (per_channel) {
     // notice:
     // at now for tflite model, Conv2D's weight format is KHWC, so is DepthwiseConv2D
     // if TransWeightFormat is done before PostTraingingQuantization, the DepthwiseCon2D's weight is CHWK
-    size_t shapeSize = weightPtr->tensor_shape_size();
-    auto channels = dims[0];
-    size_t oneFilterSize = shapeSize / channels;
-    auto *rawDatas = reinterpret_cast<const float *>(weightPtr->tensor_addr());
-    if (rawDatas == nullptr) {
-      MS_LOG(ERROR) << "rawDatas is nullptr";
-      return RET_ERROR;
-    }
-
-    float min = FLT_MAX;
-    float max = -FLT_MAX;
-    weightPtr->quant_param().clear();
-    vector<int8_t> qDatas(shapeSize);
-
-    for (uint32_t i = 0; i < channels; i++) {
-      // find min and max
-      for (uint32_t j = 0; j < oneFilterSize; j++) {
-        auto index = j + i * channels;
-        if (index >= shapeSize) {
-          MS_LOG(ERROR) << "over flow!";
-          return RET_ERROR;
+    if (depth_wise) {
+      // channel at last
+      auto channels = dims[3];
+      if (channels == 0) {
+        MS_LOG(ERROR) << "channels is zero";
+        return RET_ERROR;
+      }
+      size_t one_filter_size = elem_count / channels;
+
+      for (uint32_t i = 0; i < channels; i++) {
+        float min = FLT_MAX;
+        float max = -FLT_MAX;
+        // find min and max
+        for (uint32_t j = 0; j < one_filter_size; j++) {
+          auto index = i + j * channels;
+          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, bitNum);
+        if (status != RET_OK) {
+          MS_LOG(ERROR) << "CalQuantizationParams failed" << status;
+          return status;
         }
-        min = std::min(min, rawDatas[index]);
-        max = std::max(max, rawDatas[index]);
+        quant_params.emplace_back(quant_param);
+        // do quantization
+        for (uint32_t j = 0; j < one_filter_size; j++) {
+          auto index = i + j * channels;
+          if (index >= elem_count) {
+            MS_LOG(ERROR) << "over flow!";
+            return RET_ERROR;
+          }
+          float raw_data = raw_datas[index];
+          auto quant_data = QuantizeData<int8_t>(raw_data, quant_param, quant_max, quant_min);
+          quant_datas[index] = quant_data;
+        }
+      }
+      auto ret = memcpy_s(const_cast<float *>(raw_datas), weight->tensor_size(), quant_datas.data(),
+                          elem_count * sizeof(int8_t));
+      if (ret != EOK) {
+        MS_LOG(ERROR) << "memcpy error: " << ret;
+        return RET_ERROR;
       }
-      std::unique_ptr<AnfQuantParam> quantParam = std::unique_ptr<AnfQuantParam>(new AnfQuantParam);
-      STATUS status = CalQuantizationParams(quantParam, min, max, false, quant_max, quant_min, bitNum);
-      if (status != RET_OK) {
-        MS_LOG(ERROR) << "CalQuantizationParams failed" << status;
-        return status;
+      if (quantType == QuantType_WeightQuant) {
+        PostBitPack(const_cast<float *>(raw_datas), elem_count, bitNum);
       }
-      // do quantization
-      for (uint32_t j = 0; j < oneFilterSize; j++) {
-        auto index = j + i * channels;
-        if (index >= shapeSize) {
-          MS_LOG(ERROR) << "over flow!";
-          return RET_ERROR;
+
+      weight->set_tensor_size(elem_count * sizeof(int8_t));
+  } else {
+      // channel at first
+      auto channels = dims[0];
+      if (channels == 0) {
+        MS_LOG(ERROR) << "channels is zero";
+        return RET_ERROR;
+      }
+      size_t one_filter_size = elem_count / channels;
+
+      for (uint32_t i = 0; i < channels; i++) {
+        float min = FLT_MAX;
+        float max = -FLT_MAX;
+        // find min and max
+        for (uint32_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, bitNum);
+        if (status != RET_OK) {
+          MS_LOG(ERROR) << "CalQuantizationParams failed" << status;
+          return status;
+        }
+        quant_params.emplace_back(quant_param);
+        // do quantization
+        for (uint32_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;
+          }
+          float raw_data = raw_datas[index];
+          auto quant_data = QuantizeData<int8_t>(raw_data, quant_param, quant_max, quant_min);
+          quant_datas[index] = quant_data;
         }
-        float rawData = rawDatas[index];
-        auto qData = QuantizeData<int8_t>(rawData, quantParam.get(), quant_max, quant_min);
-        qDatas[index] = qData;
       }
-      weightPtr->set_quant_param(quantParam);
-    }
-    auto ret =
-      memcpy_s(const_cast<float *>(rawDatas), weightPtr->tensor_size(), qDatas.data(), shapeSize * sizeof(int8_t));
-    if (ret != EOK) {
-      MS_LOG(ERROR) << "memcpy error: " << ret;
-      return RET_ERROR;
-    }
-    if (quantType == QuantType_WeightQuant) {
-      PostBitPack(const_cast<float *>(rawDatas), shapeSize, bitNum);
+      auto ret =
+        memcpy_s(raw_datas, weight->tensor_size(), quant_datas.data(), elem_count * sizeof(int8_t));
+      if (ret != EOK) {
+        MS_LOG(ERROR) << "memcpy error: " << ret;
+        return RET_ERROR;
+      }
+      if (quantType == QuantType_WeightQuant) {
+        PostBitPack(const_cast<float *>(raw_datas), elem_count, bitNum);
+      }
+      weight->set_tensor_size(elem_count * sizeof(int8_t));
     }
 
-    weightPtr->set_tensor_type(kNumberTypeInt8);
-    weightPtr->set_tensor_size(shapeSize * sizeof(int8_t));
   } else {
     // per layer
-    size_t shapeSize = weightPtr->tensor_shape_size();
-    auto *rawDatas = static_cast<float *>(weightPtr->tensor_addr());
-    if (rawDatas == nullptr) {
-      MS_LOG(ERROR) << "rawDatas is nullptr";
-      return RET_ERROR;
-    }
-
-    weightPtr->quant_param().clear();
-    vector<int8_t> qDatas(shapeSize);
-
-    float min = 0;
-    float max = 0;
-    for (uint32_t i = 0; i < shapeSize; i++) {
+    float min = FLT_MAX;
+    float max = -FLT_MIN;
+    for (uint32_t i = 0; i < elem_count; i++) {
       // find max min
-      min = std::min(min, rawDatas[i]);
-      max = std::max(max, rawDatas[i]);
+      min = std::min(min, raw_datas[i]);
+      max = std::max(max, raw_datas[i]);
     }
 
-    std::unique_ptr<AnfQuantParam> quantParam = std::unique_ptr<AnfQuantParam>(new AnfQuantParam);
-    STATUS status = CalQuantizationParams(quantParam, min, max, false, quant_max, quant_min, bitNum);
+    schema::QuantParamT quant_param;
+    STATUS status = CalQuantizationParams(&quant_param, min, max, false, quant_max, quant_min, bitNum);
     if (status != RET_OK) {
       MS_LOG(ERROR) << "CalQuantizationParams failed" << status;
       return status;
     }
+    quant_params.emplace_back(quant_param);
     // update data and datatype
-    for (uint32_t i = 0; i < shapeSize; i++) {
-      float rawData = rawDatas[i];
-      auto quant_data = std::round(rawData / quantParam->scale + quantParam->zeroPoint);
-      if (quant_data > quant_max) {
-        qDatas[i] = quant_max;
-      } else if (quant_data < quant_min) {
-        qDatas[i] = quant_min;
-      } else {
-        qDatas[i] = static_cast<int8_t>(quant_data);
-      }
+    for (uint32_t i = 0; i < elem_count; i++) {
+      float raw_data = raw_datas[i];
+      auto quant_data = QuantizeData<int8_t>(raw_data, quant_param, quant_max, quant_min);
+      quant_datas[i] = quant_data;
     }
-
-    weightPtr->set_quant_param(quantParam);
-    auto ret = memcpy_s(rawDatas, weightPtr->tensor_size(), qDatas.data(), shapeSize * sizeof(int8_t));
+    auto ret = memcpy_s(raw_datas, weight->tensor_size(), quant_datas.data(), elem_count * sizeof(int8_t));
     if (ret != EOK) {
       MS_LOG(ERROR) << "memcpy error: " << ret;
       return RET_ERROR;
     }
     if (quantType == QuantType_WeightQuant) {
-      PostBitPack(rawDatas, shapeSize, bitNum);
+      PostBitPack(raw_datas, elem_count, bitNum);
     }
-
-    weightPtr->set_tensor_type(kNumberTypeInt8);
-    weightPtr->set_tensor_size(shapeSize * sizeof(int8_t));
+    weight->set_tensor_size(elem_count * sizeof(int8_t));
   }
-
+  if (quant_params.empty()) {
+    MS_LOG(ERROR) << "quant_params empty";
+    return RET_ERROR;
+  }
+  primitiveT_value->AddInputQuantParam(quant_params);
   return RET_OK;
 }
 

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

@@ -29,6 +29,7 @@
 #include "ir/primitive.h"
 #include "abstract/dshape.h"
 #include "mindspore/lite/tools/converter/quantizer/quantizer.h"
+#include "mindspore/lite/src/ir/primitive_t_value.h"
 
 namespace mindspore {
 namespace lite {
@@ -58,7 +59,7 @@ class QuantStrategy {
   static const std::array<std::string, 4> mMulTypes;
 };
 
-STATUS CalQuantizationParams(std::unique_ptr<AnfQuantParam> &quantParam, double mMin, double mMax,
+STATUS CalQuantizationParams(schema::QuantParamT *quantParam, double mMin, double mMax,
                              bool narrowRange, int quant_max, int quant_min, int num_bits);
 
 STATUS CalQuantizationParams(schema::QuantParamT *quantParam, double mMin, double mMax,
@@ -97,12 +98,12 @@ T QuantizeData(const float originData, const schema::QuantParamT *quantParam) {
 }
 
 template <typename T>
-T QuantizeData(float originData, const AnfQuantParam *quantParam, int quant_max, int quant_min) {
+T QuantizeData(float originData, const schema::QuantParamT &quantParam, int quant_max, int quant_min) {
   MS_ASSERT(quantParam != nullptr);
   MS_ASSERT(quantParam->inited);
-  const auto scale = quantParam->scale;
-  const int zeroPoint = quantParam->zeroPoint;
-  const auto narrowRange = quantParam->narrowRange;
+  const auto scale = quantParam.scale;
+  const int zeroPoint = quantParam.zeroPoint;
+  const auto narrowRange = quantParam.narrowRange;
   const int maxLimit = quant_max;
   const int minLimit = quant_min;
 
@@ -119,8 +120,9 @@ T QuantizeData(float originData, const AnfQuantParam *quantParam, int quant_max,
 
 void CalFakeNode(const AnfNodePtr &inTensor);
 
-STATUS QuantFilter(ParamValueLitePtr &weightPtr, QuantType quantType, int quant_max, int quant_min,
-                   size_t bitNum = UINT8_QUANTIZATION, bool per_channel = false);
+STATUS QuantFilter(ParamValueLitePtr weight, std::shared_ptr<PrimitiveTValue> primitiveT_value, QuantType quantType,
+                   int quant_max, int quant_min, size_t bitNum = UINT8_QUANTIZATION, bool per_channel = false,
+                   bool depth_wise = false);
 
 STATUS PostBitPack(float *weights, size_t shapeSize, size_t bitNum = UINT8_QUANTIZATION);
 }  // namespace quant

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

@@ -1,150 +0,0 @@
-/**
- * Copyright 2020 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "tools/converter/quantizer/weight_quantizer.h"
-#include <list>
-#include <string>
-#include "src/common/common.h"
-#include "ir/dtype/type_id.h"
-
-using std::string;
-using std::vector;
-
-namespace mindspore {
-namespace lite {
-namespace quant {
-WeightQuantizer::WeightQuantizer(FuncGraphPtr graph, const string &weightSize,
-                                 const std::string &convWeightChannelThreshold, const std::string &bitNum)
-    : Quantizer(graph) {
-  auto quantSize = static_cast<size_t>(std::stoull(weightSize));
-  this->bitNum = static_cast<size_t>(std::stoull(bitNum));
-  auto convQuantWeightChannelThreshold = static_cast<size_t>(std::stoull(convWeightChannelThreshold));
-  // TODO(...): update stractory
-  mStrategy.reset(new QuantStrategy(quantSize, convQuantWeightChannelThreshold));
-}
-
-// uint32_t GetConvChannel(TensorDefT *weight) {
-//   uint32_t channel = 0;
-//   const vector<int> dims = weight->dims;
-
-//   switch (weight->format) {
-//     case Format_NCHW:
-//     case Format_KCHW:
-//     case Format_NC4HW4:
-//       channel = static_cast<uint32_t>(dims[NCHW_N]);
-//       break;
-//     case Format_NHWC:
-//     case Format_HWKC:
-//       channel = static_cast<uint32_t>(dims[NHWC_N]);
-//       break;
-//     case Format_HWCK:
-//       channel = static_cast<uint32_t>(dims[HWCK_K]);
-//       break;
-//     case Format_CKHW:
-//       channel = static_cast<uint32_t>(dims[CKHW_K]);
-//       break;
-//     default:
-//       MS_LOGE("Unsupported format: %d", weight->format);
-//       return 0;
-//   }
-//   return channel;
-// }
-
-STATUS WeightQuantizer::DoConvQuantize(const std::list<CNodePtr> &nodes) {
-    for (auto &cnode : nodes) {
-        if (!mStrategy->CanConvOpQuantized(cnode)) {
-            continue;
-        }
-
-        auto inputNode = cnode->input(2);
-        if (!inputNode->isa<Parameter>()) {
-            return RET_ERROR;
-        }
-
-        auto paramNode = inputNode->cast<ParameterPtr>();
-        if (!paramNode->has_default()) {
-            return RET_ERROR;
-        }
-
-        ParamValueLitePtr paramValue = std::static_pointer_cast<ParamValueLite>(paramNode->default_param());
-        auto status = QuantFilter(paramValue, QuantType_WeightQuant, 127, -128, bitNum);
-        if (status != RET_OK) {
-            MS_LOG(ERROR) << "QuantFilter failed : " << status;
-            return status;
-        }
-    }
-
-    return RET_OK;
-}
-
-STATUS WeightQuantizer::DoMulQuantize(const std::list<CNodePtr> &nodes) {
-    for (auto &node : nodes) {
-        if (!mStrategy->CanMulOpQuantized(node)) {
-            continue;
-        }
-
-        ParamValueLitePtr paramValue = nullptr;
-        for (size_t i = 1; i < node->size(); i++) {
-            auto inputNode = node->input(i);
-            if (inputNode->isa<Parameter>() == true) {
-                auto paramNode = inputNode->cast<ParameterPtr>();
-                if ((paramNode != nullptr) && (paramNode->has_default() == true)) {
-                    paramValue = std::static_pointer_cast<ParamValueLite>(paramNode->default_param());
-                    if ((paramValue == nullptr) || (paramValue->tensor_size() == 0)
-                        || (paramValue->tensor_shape().size() != 4)
-                        || (paramValue->tensor_addr() == nullptr)
-                        || (paramValue->tensor_type() != mindspore::kNumberTypeFloat32)) {
-                            paramValue = nullptr;
-                            continue;
-                    } else {
-                        break;
-                    }
-                }
-            }
-        }
-        if (paramValue == nullptr) {
-            MS_LOG(ERROR) << "No valid input param node !";
-            continue;
-        }
-        auto status = QuantFilter(paramValue, QuantType_WeightQuant, 127, -128, bitNum);
-        if (status != RET_OK) {
-            MS_LOG(ERROR) << "QunatFilter failed" << status;
-            return RET_ERROR;
-        }
-    }
-
-    return RET_OK;
-}
-
-STATUS WeightQuantizer::DoQuantize(FuncGraphPtr funcGraph) {
-  auto ret = RET_OK;
-  auto cnodes = funcGraph->GetOrderedCnodes();
-  ret = DoConvQuantize(cnodes);
-  if (ret != RET_OK) {
-    MS_LOG(ERROR) << "DoConvQuantize failed :" << ret;
-    return ret;
-  }
-  ret = DoMulQuantize(cnodes);
-  if (ret != RET_OK) {
-    MS_LOG(ERROR) << "DoMulQuantize failed :" << ret;
-    return ret;
-  }
-  return ret;
-}
-}  // namespace quant
-}  // namespace lite
-}  // namespace mindspore
-

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

@@ -1,53 +0,0 @@
-/**
- * Copyright 2020 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef WEIGHT_QUANTIZER_H
-#define WEIGHT_QUANTIZER_H
-
-#include <memory>
-#include <list>
-#include <string>
-#include "tools/converter/quantizer/quantizer.h"
-#include "tools/converter/quantizer/quantize_util.h"
-#include "ir/func_graph.h"
-#include "ir/anf.h"
-#include "include/model.h"
-#include "base/base.h"
-#include "abstract/dshape.h"
-
-namespace mindspore {
-namespace lite {
-namespace quant {
-class WeightQuantizer : public Quantizer {
- public:
-  WeightQuantizer(FuncGraphPtr graph, const std::string& weightSize,
-                  const std::string& covWeightChannelThreshold, const std::string& bitNum);
-
-  ~WeightQuantizer() = default;
-
-  STATUS DoQuantize(FuncGraphPtr funcGraph) override;
-  STATUS DoConvQuantize(const std::list<CNodePtr> &nodes);
-  STATUS DoMulQuantize(const std::list<CNodePtr> &nodes);
-
- private:
-  std::unique_ptr<QuantStrategy> mStrategy;
-  size_t bitNum;
-};
-}  // namespace quant
-}  // namespace lite
-}  // namespace mindspore
-#endif
-