!6754 promote weight quantization precision

Merge pull request !6754 from xutianchun/quant_0923

mindspore/lite/schema/model.fbs

@@ -32,6 +32,8 @@ table QuantParam {
     narrowRange: bool = true;
     numBits: int = 8;
     inited: bool = false;
+    var_corr: double = 1;
+    mean_corr: double = 0;
 }
 
 table Tensor {

mindspore/lite/src/lite_kernel.cc

@@ -174,9 +174,9 @@ float *LiteKernelUtil::DequantWeight(lite::Tensor *input_tensor) {
     MS_LOG(ERROR) << "no quant param";
     return nullptr;
   }
-  const auto *quant_data = static_cast<const int8_t *>(input_tensor->MutableData());
-  auto *dequant_data = static_cast<float *>(malloc(input_tensor->ElementsNum() * sizeof(float)));
-  if (dequant_data == nullptr) {
+  const auto *quant_datas = static_cast<const int8_t *>(input_tensor->MutableData());
+  auto *dequant_datas = static_cast<float *>(malloc(input_tensor->ElementsNum() * sizeof(float)));
+  if (dequant_datas == nullptr) {
     MS_LOG(ERROR) << "malloc faile";
     return nullptr;
   }
@@ -185,7 +185,7 @@ float *LiteKernelUtil::DequantWeight(lite::Tensor *input_tensor) {
     size_t channels = static_cast<size_t>(input_tensor->Batch());
     if (input_tensor->GetQuantParams().size() != channels) {
       MS_LOG(ERROR) << "Quant param not equal channel num " << input_tensor->GetQuantParams().size() << channels;
-      free(dequant_data);
+      free(dequant_datas);
       return nullptr;
     }
     size_t per_channel_size = input_tensor->ElementsNum() / channels;
@@ -194,9 +194,15 @@ float *LiteKernelUtil::DequantWeight(lite::Tensor *input_tensor) {
       auto param = quant_param.at(i);
       auto scale = param.scale;
       auto zero_point = param.zeroPoint;
+      auto var_corr = param.var_corr;
+      auto mean_corr = param.mean_corr;
+      if (var_corr < 0 || var_corr > 10) {
+        MS_LOG(WARNING) << "unexpeted var_corr: " << var_corr;
+        var_corr = 1;
+      }
       for (size_t j = 0; j < per_channel_size; j++) {
-        dequant_data[per_channel_size * i + j] =
-          static_cast<float>((quant_data[per_channel_size * i + j] - zero_point) * scale);
+        auto dequant_data = (quant_datas[per_channel_size * i + j] - zero_point) * scale;
+        dequant_datas[per_channel_size * i + j] = static_cast<float>(dequant_data * var_corr + mean_corr);
       }
     }
   } else {
@@ -205,9 +211,9 @@ float *LiteKernelUtil::DequantWeight(lite::Tensor *input_tensor) {
     auto scale = param.scale;
     auto zero_point = param.zeroPoint;
     for (int64_t j = 0; j < input_tensor->ElementsNum(); j++) {
-      dequant_data[j] = static_cast<float>((quant_data[j] - zero_point) * scale);
+      dequant_datas[j] = static_cast<float>((quant_datas[j] - zero_point) * scale);
     }
   }
-  return dequant_data;
+  return dequant_datas;
 }
 }  // namespace mindspore::kernel

mindspore/lite/src/lite_session.cc

@@ -106,6 +106,8 @@ int LiteSession::ConvertTensors(const lite::Model *model) {
         QuantArg quant_arg{};
         quant_arg.scale = quant_params->Get(j)->scale();
         quant_arg.zeroPoint = quant_params->Get(j)->zeroPoint();
+        quant_arg.var_corr = quant_params->Get(j)->var_corr();
+        quant_arg.mean_corr = quant_params->Get(j)->mean_corr();
         dstTensor->AddQuantParam(quant_arg);
       }
     }
@@ -351,7 +353,7 @@ int LiteSession::Init(Context *context) {
     }
   }
 #endif
-  executor = new(std::nothrow) Executor();
+  executor = new (std::nothrow) Executor();
   if (nullptr == executor) {
     MS_LOG(ERROR) << "New Executor failed";
     is_running_.store(false);

mindspore/lite/src/tensor.h

@@ -33,6 +33,8 @@ namespace lite {
 struct QuantArg {
   double scale;
   int32_t zeroPoint;
+  double var_corr{1};
+  double mean_corr{0};
 };
 
 class Tensor : public mindspore::tensor::MSTensor {

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

@@ -143,7 +143,7 @@ STATUS QuantFilter(ParamValueLitePtr weight, std::shared_ptr<PrimitiveC> primiti
     return RET_ERROR;
   }
   std::vector<T> quant_datas(elem_count);
-
+  std::vector<float> dequant_datas(elem_count);
   if (per_channel) {
     // notice: assume Con2D\DepthwiseConv2D's weight format are same: KHWC
     // channel at first
@@ -173,8 +173,9 @@ STATUS QuantFilter(ParamValueLitePtr weight, std::shared_ptr<PrimitiveC> primiti
         MS_LOG(ERROR) << "CalQuantizationParams failed" << status;
         return status;
       }
-      quant_params.emplace_back(quant_param);
       // do quantization
+      double average_dequant = 0;
+      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) {
@@ -184,7 +185,44 @@ STATUS QuantFilter(ParamValueLitePtr weight, std::shared_ptr<PrimitiveC> primiti
         float raw_data = raw_datas[index];
         auto quant_data = QuantizeData<T>(raw_data, quant_param, quant_max, quant_min);
         quant_datas[index] = quant_data;
+
+        if (quantType == QuantType_WeightQuant) {
+          float dequant_data = quant_param.scale * (quant_data - quant_param.zeroPoint);
+          dequant_datas[index] = dequant_data;
+          average_dequant += dequant_data;
+          average_raw += raw_data;
+        }
       }
+      if (quantType == QuantType_WeightQuant) {
+        // mean
+        average_dequant = average_dequant / one_filter_size;
+        average_raw = average_raw / one_filter_size;
+        // std
+        double variance_dequant = 0;
+        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) {
+            MS_LOG(ERROR) << "over flow!";
+            return RET_ERROR;
+          }
+          variance_dequant += std::pow(dequant_datas[index] - average_dequant, 2);
+          variance_raw += std::pow(raw_datas[index] - average_raw, 2);
+        }
+        variance_dequant = std::sqrt(variance_dequant / one_filter_size);
+        variance_raw = std::sqrt(variance_raw / one_filter_size);
+        quant_param.var_corr = 1;
+        if (variance_raw != 0 && variance_dequant != 0) {
+          auto temp_var_corr = variance_raw / variance_dequant;
+          if (temp_var_corr > 0 && temp_var_corr < 10) {
+            quant_param.var_corr = temp_var_corr;
+          } else {
+            MS_LOG(WARNING) << "unexpected var_corr: " << temp_var_corr;
+          }
+        }
+        quant_param.mean_corr = average_raw - average_dequant * quant_param.var_corr;
+      }
+      quant_params.emplace_back(quant_param);
     }
     auto ret = memcpy_s(raw_datas, weight->tensor_size(), quant_datas.data(), elem_count * sizeof(int8_t));
     if (ret != EOK) {