Aware training support patial quant

mindspore/lite/test/CMakeLists.txt

@@ -203,6 +203,7 @@ if(BUILD_CONVERTER)
             ${LITE_DIR}/tools/optimizer/fusion/conv_scale_fusion.cc
             ${LITE_DIR}/tools/optimizer/fusion/conv_bn_fusion.cc
             ${LITE_DIR}/tools/optimizer/fusion/constant_folding_fusion.cc
+            ${LITE_DIR}/tools/optimizer/fusion/quant_dtype_cast_fusion.cc
             )
 endif()
 ### train

mindspore/lite/tools/common/node_util.cc

@@ -75,7 +75,7 @@ static const std::vector<schema::PrimitiveType> int8OpList = {
   schema::PrimitiveType_SoftMax,   schema::PrimitiveType_Split,
   schema::PrimitiveType_Squeeze,   schema::PrimitiveType_Sub,
   schema::PrimitiveType_TopK,      schema::PrimitiveType_Unsqueeze,
-  schema::PrimitiveType_MatMul};
+  schema::PrimitiveType_MatMul,    schema::PrimitiveType_Pad};
 
 static const std::vector<schema::PrimitiveType> needInsertOpList = {
   schema::PrimitiveType_Eltwise, schema::PrimitiveType_Activation, schema::PrimitiveType_Concat,

mindspore/lite/tools/converter/CMakeLists.txt

@@ -61,6 +61,7 @@ file(GLOB_RECURSE CONVERTER_SRC RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
         ../optimizer/fusion/conv_scale_fusion.cc
         ../optimizer/fusion/conv_bn_fusion.cc
         ../optimizer/fusion/constant_folding_fusion.cc
+        ../optimizer/fusion/quant_dtype_cast_fusion.cc
         )
 
 add_subdirectory(../anf_importer anf_importer)

mindspore/lite/tools/converter/anf_transform.cc

@@ -24,6 +24,7 @@
 #include "tools/optimizer/fusion/conv_scale_fusion.h"
 #include "tools/optimizer/fusion/conv_bn_fusion.h"
 #include "tools/optimizer/fusion/constant_folding_fusion.h"
+#include "tools/optimizer/fusion/quant_dtype_cast_fusion.h"
 #include "tools/converter/quantizer/post_training_quantizer.h"
 #include "tools/converter/quantizer/quant_cast.h"
 #include "tools/converter/quantizer/weight_quantizer.h"
@@ -43,6 +44,10 @@ FuncGraphPtr AnfTransform::Transform(const FuncGraphPtr &old_graph, const conver
 
   // for now - trainning is not supporting fuse operations
   if (config != nullptr && config->trainModel == false) {
+    // remove quantdtype when awaretraining
+    if (config->quantType == QuantType_AwareTraining) {
+      pm->AddPass(std::make_shared<opt::QuantDtypeCastFusion>());
+    }
     pm->AddPass(std::make_shared<opt::ConvBiasaddFusion>());
     pm->AddPass(std::make_shared<opt::ConvBatchNormFusion>());
     pm->AddPass(std::make_shared<opt::ConvScaleFusion>());

mindspore/lite/tools/converter/graphdef_transform.cc

@@ -102,7 +102,7 @@ int GraphDefTransform::Transform(const converter::Flags &ctx) {
 
   // generate and infer quant parameters
   {
-    if (mQuantizer != nullptr) {
+    if (fbQuantizer != nullptr) {
       Optimizer topologicalOptimizer;
       topologicalOptimizer.AddPass(new (std::nothrow) TopologicalSortPass());
       status = topologicalOptimizer.Run(graphDefT);
@@ -110,14 +110,13 @@ int GraphDefTransform::Transform(const converter::Flags &ctx) {
         MS_LOG(ERROR) << "Run topologicalOptimizer graphPasses Failed";
         return status;
       }
-      if (!(this->graphDefT->fmkType == converter::FmkType_TF &&
+      if (ctx.quantType == QuantType_AwareTraining) {
-            this->graphDefT->nodes.front()->quantType == QuantType::QuantType_AwareTraining)) {
+        status = fbQuantizer->GenerateQuantParam();
-        status = mQuantizer->GenerateQuantParam();
         if (status != RET_OK) {
           MS_LOG(ERROR) << "GenerateQuantParam failed";
           return status;
         }
-        status = mQuantizer->DetermineNodeQuantType();
+        status = fbQuantizer->DetermineNodeQuantType();
         if (status != RET_OK) {
           MS_LOG(ERROR) << "DetermineNodeQuant failed";
           return status;

mindspore/lite/tools/converter/legacy_optimizer/graph/dtype_trans_pass.cc

@@ -151,7 +151,7 @@ STATUS DTypeTransPass::DoNodeInoutDTypeTrans(schema::MetaGraphT *graph) {
       MS_ASSERT(graph->allTensors.size() > (*iter)->inputIndex.at(i));
       auto &preTensor = graph->allTensors.at((*iter)->inputIndex.at(i));
       auto &graphInIdxes = graph->inputIndex;
-      if (preTensor->nodeType == NodeType_ValueNode && !IsContain(graphInIdxes, (*iter)->inputIndex.at(i))) {
+      if (!preTensor->data.empty() && !IsContain(graphInIdxes, (*iter)->inputIndex.at(i))) {
         continue;
       }
       iter = InsertDTypeTransNode(graph, iter, kBefore, i, kInt8ToFP32, &status);

mindspore/lite/tools/converter/parser/tflite/tflite_dequantize_parser.cc

@@ -46,7 +46,7 @@ STATUS TfliteDequantizeParser::Parse(const std::unique_ptr<tflite::OperatorT> &t
     MS_LOG(ERROR) << "output tensor is null";
     return RET_NULL_PTR;
   }
-  if (GetTfliteDataType(in_tensor->type) == kNumberTypeInt8) {
+  if (GetTfliteDataType(in_tensor->type) == kNumberTypeInt8 || GetTfliteDataType(in_tensor->type) == kNumberTypeUInt8) {
     std::unique_ptr<schema::QuantDTypeCastT> attr = std::make_unique<schema::QuantDTypeCastT>();
     if (attr == nullptr) {
       MS_LOG(ERROR) << "new op failed";

mindspore/lite/tools/converter/parser/tflite/tflite_model_parser.cc

@@ -77,8 +77,7 @@ void TfliteModelParser::SetTensorQuantParam(const std::unique_ptr<tflite::Tensor
   }
 
   // change quant param min to 0 to fit ms-lite ops
-  if (GetTfliteDataType(tflite_tensor->type) == TypeId::kNumberTypeUInt8
+  if (GetTfliteDataType(tflite_tensor->type) == TypeId::kNumberTypeUInt8 && tensor->data.empty()) {
-      && tensor->dataType == TypeId::kNumberTypeInt8) {
     quant_param->zeroPoint = quant_param->zeroPoint - 128;
   }
 

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

@@ -115,11 +115,6 @@ STATUS AwareQuantizer::GenerateQuantParam() {
       return status;
     }
   }
-  auto status = GenerateDefaultQuantParam(graph);
-  if (status != RET_OK) {
-    MS_LOG(ERROR) << "GenerateDefaultQuantParam failed";
-    return status;
-  }
   auto *quantParamRegister = QuantParamCalcRegister::GetInstance();
 
   for (auto iter = graph->nodes.begin(); iter != graph->nodes.end(); iter++) {
@@ -135,7 +130,7 @@ STATUS AwareQuantizer::GenerateQuantParam() {
                     << ", type: " << GetCNodeTTypeName(*node).c_str() << " set node to QuantNone and skip";
       node->quantType = static_cast<schema::QuantType>(QuantType_QUANT_NONE);
     } else {
-      status = quantParamCalcer->Calc(graph, *node);
+      auto status = quantParamCalcer->Calc(graph, *node);
       if (status != RET_OK) {
         MS_LOG(ERROR) << "quantParamCalcer failed: " << status << " node: " << node->name.c_str();
         node->quantType = schema::QuantType_QUANT_NONE;
@@ -167,17 +162,23 @@ STATUS AwareQuantizer::DoQuantize() {
         return RET_ERROR;
       }
       // quant weight
-      status = QuantConvWeight(graph, node.get());
+      auto &weightTensor = graph->allTensors.at(node->inputIndex.at(1));
-      if (status != RET_OK) {
+      if (!weightTensor->quantParams.empty() && weightTensor->quantParams.at(0)->inited) {
-        MS_LOG(ERROR) << "QuantConvWeight failed!";
+        status = QuantConvWeight(graph, node.get());
-        return RET_ERROR;
+        if (status != RET_OK) {
+          MS_LOG(ERROR) << "QuantConvWeight failed!";
+          return RET_ERROR;
+        }
       }
       // quant bias
       if (inputIndexes.size() == 3) {
-        status = QuantConvBias(graph, node.get());
+        auto &biasTensor = graph->allTensors.at(node->inputIndex.at(2));
-        if (status != RET_OK) {
+        if (!biasTensor->quantParams.empty() && biasTensor->quantParams.at(0)->inited) {
-          MS_LOG(ERROR) << "QuantConvBias failed!";
+          status = QuantConvBias(graph, node.get());
-          return RET_ERROR;
+          if (status != RET_OK) {
+            MS_LOG(ERROR) << "QuantConvBias failed!";
+            return RET_ERROR;
+          }
         }
       }
     } else if (GetCNodeTType(*node) == schema::PrimitiveType_DetectionPostProcess) {
@@ -376,29 +377,17 @@ STATUS AwareQuantizer::DetermineNodeQuantType() {
   for (auto &node : graph->nodes) {
     MS_ASSERT(node != nullptr);
     bool canQuant = true;
-    for (auto &inTensorIdx : node->inputIndex) {
+    for (auto &outTensorIdx : node->outputIndex) {
-      MS_ASSERT(graph->allTensors.size() > inTensorIdx);
+      MS_ASSERT(graph->allTensors.size() > outTensorIdx);
-      auto &inTensor = graph->allTensors.at(inTensorIdx);
+      auto &outTensor = graph->allTensors.at(outTensorIdx);
-      MS_ASSERT(inTensor != nullptr);
+      MS_ASSERT(outTensor != nullptr);
-      if (inTensor->quantParams.empty() || inTensor->quantParams.front() == nullptr ||
+      if (outTensor->quantParams.empty() || outTensor->quantParams.front() == nullptr ||
-          !inTensor->quantParams.front()->inited) {
+          !outTensor->quantParams.front()->inited) {
         canQuant = false;
         break;
       }
     }
 
-    if (canQuant) {
-      for (auto &outTensorIdx : node->outputIndex) {
-        MS_ASSERT(graph->allTensors.size() > outTensorIdx);
-        auto &outTensor = graph->allTensors.at(outTensorIdx);
-        MS_ASSERT(outTensor != nullptr);
-        if (outTensor->quantParams.empty() || outTensor->quantParams.front() == nullptr ||
-            !outTensor->quantParams.front()->inited) {
-          canQuant = false;
-          break;
-        }
-      }
-    }
     if (canQuant && IsContain(GetUint8OpList(), GetCNodeTType(*node))) {
       node->quantType = schema::QuantType_AwareTraining;
     } else {

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

@@ -70,6 +70,9 @@ int QuantParamCalcer::Calc(MetaGraphT *graph, const CNodeT &node) {
     auto &tensor = graph->allTensors.at(node.inputIndex.at(i));
     MS_ASSERT(tensor != nullptr);
     auto quantParam = GetTensorQuantParam(tensor);
+    if (quantParam == nullptr) {
+      continue;
+    }
     if (quantParam->inited) {  // inited
       inputParamDone++;
       continue;
@@ -77,8 +80,7 @@ int QuantParamCalcer::Calc(MetaGraphT *graph, const CNodeT &node) {
     MS_ASSERT(graph->allTensors.size() > node.inputIndex.at(i));
 
     MS_ASSERT(tensor != nullptr);
-    if (tensor->refCount == schema::NodeType::NodeType_ValueNode &&
+    if (!tensor->data.empty() && !IsContain(graph->inputIndex, node.inputIndex.at(i))) {
-        !IsContain(graph->inputIndex, node.inputIndex.at(i))) {
       auto status = ComputeConstQuantParam((*tensor), quantParam.get());
       if (status != RET_OK) {
         MS_LOG(WARNING) << "ComputeConstQuantParam failed: " << status;
@@ -95,13 +97,12 @@ int QuantParamCalcer::Calc(MetaGraphT *graph, const CNodeT &node) {
     auto &tensor = graph->allTensors.at(i);
     MS_ASSERT(tensor != nullptr);
     auto quantParam = GetTensorQuantParam(tensor);
-    MS_ASSERT(quantParam != nullptr);
+    if (quantParam != nullptr && quantParam->inited) {  // inited
-    if (quantParam->inited) {  // inited
       outputParamDone++;
       continue;
     }
 
-    if (tensor->refCount == 999) {
+    if (!tensor->data.empty()) {
       MS_ASSERT(false);
     }
   }
@@ -146,10 +147,10 @@ int LinearCalcer::Calc(MetaGraphT *graph, const CNodeT &node) {
       auto &inTensor = graph->allTensors.at(i);
       MS_ASSERT(inTensor != nullptr);
       auto inQuantParam = GetTensorQuantParam(inTensor);
-      if (inQuantParam->inited) {
+      if (inQuantParam == nullptr || inQuantParam->inited) {
         continue;
       }
-      inTensor->quantParams.front() = std::move(inQuantParam);
+      inTensor->quantParams.front() = std::move(outputQuantParam);
     }
   }
   if (outputParamDone != node.outputIndex.size()) {
@@ -157,7 +158,7 @@ int LinearCalcer::Calc(MetaGraphT *graph, const CNodeT &node) {
     auto &inTensor = graph->allTensors.at(node.inputIndex.at(0));
     MS_ASSERT(inTensor != nullptr);
     auto inQuantParam = GetTensorQuantParam(inTensor);
-    if (!inQuantParam->inited) {
+    if (inQuantParam == nullptr || !inQuantParam->inited) {
       MS_LOG(WARNING) << "Can not determine outputTensor quantParam from inputTensor for node %s" << node.name;
       return RET_ERROR;
     }
@@ -166,10 +167,10 @@ int LinearCalcer::Calc(MetaGraphT *graph, const CNodeT &node) {
       auto &outTensor = graph->allTensors.at(node.outputIndex.at(i));
       MS_ASSERT(outTensor != nullptr);
       auto outQuantParam = GetTensorQuantParam(outTensor);
-      if (outQuantParam->inited) {
+      if (outQuantParam == nullptr || outQuantParam->inited) {
         continue;
       }
-      outTensor->quantParams.front() = std::move(outQuantParam);
+      outTensor->quantParams.front() = std::move(inQuantParam);
     }
   }
   return RET_OK;
@@ -225,13 +226,14 @@ class CalcConcat : public QuantParamCalcer {
       MS_ASSERT(graph->allTensors.size() > node.outputIndex.front());
       auto &outTensor = graph->allTensors.at(node.outputIndex.front());
       MS_ASSERT(outTensor != nullptr);
-      auto outQuantParam = GetTensorQuantParam(outTensor);
+      auto outQuantParam = std::make_unique<QuantParamT>();
 
       status = quant::CalQuantizationParams(outQuantParam.get(), minMin, maxMax, narrowRange, numBits);
       if (status != RET_OK) {
         MS_LOG(WARNING) << "in aware quantization run CalQuantizationParams failed!";
         return RET_ERROR;
       }
+      outTensor->quantParams.front() = std::move(outQuantParam);
       outputParamDone++;
     }
 
@@ -261,7 +263,7 @@ class CalcAdd : public QuantParamCalcer {
       MS_ASSERT(graph->allTensors.size() > node.outputIndex.front());
       auto &outTensor = graph->allTensors.at(node.outputIndex.front());
       MS_ASSERT(outTensor != nullptr);
-      auto outQuantParam = GetTensorQuantParam(outTensor);
+      auto outQuantParam = std::make_unique<QuantParamT>();
 
       MS_ASSERT(graph->allTensors.size() > node.inputIndex.at(0));
       auto &tensor0 = graph->allTensors.at(node.inputIndex.at(0));
@@ -271,10 +273,10 @@ class CalcAdd : public QuantParamCalcer {
       MS_ASSERT(tensor1 != nullptr);
       auto biasTensor = &tensor0;
       auto paramTensor = &tensor1;
-      if (tensor0->refCount == 999 && (tensor0->dims.empty() || tensor0->dims.size() == 1)) {
+      if (!tensor0->data.empty() && (tensor0->dims.empty() || tensor0->dims.size() == 1)) {
         biasTensor = &tensor0;
         paramTensor = &tensor1;
-      } else if (tensor1->refCount == 999 && (tensor1->dims.empty() || tensor1->dims.size() == 1)) {
+      } else if (!tensor1->data.empty() && (tensor1->dims.empty() || tensor1->dims.size() == 1)) {
         biasTensor = &tensor1;
         paramTensor = &tensor0;
       } else {
@@ -310,6 +312,7 @@ class CalcAdd : public QuantParamCalcer {
           return RET_ERROR;
         }
       }
+      outTensor->quantParams.front() = std::move(outQuantParam);
     }
     return RET_OK;
   }
@@ -337,13 +340,13 @@ class CalcRealDiv : public QuantParamCalcer {
       MS_ASSERT(graph->allTensors.size() > node.outputIndex.front());
       auto &outTensor = graph->allTensors.at(node.outputIndex.front());
       MS_ASSERT(outTensor != nullptr);
-      auto outQuantParam = GetTensorQuantParam(outTensor);
+      auto outQuantParam = std::make_unique<QuantParamT>();
 
       MS_ASSERT(graph->allTensors.size() > node.inputIndex.at(0));
       MS_ASSERT(graph->allTensors.size() > node.inputIndex.at(1));
       auto &tensor1 = graph->allTensors.at(node.inputIndex.at(1));
       MS_ASSERT(tensor1 != nullptr);
-      if (tensor1->refCount == 999 && (tensor1->dims.empty() || tensor1->dims.size() == 1)) {
+      if (!tensor1->data.empty() && (tensor1->dims.empty() || tensor1->dims.size() == 1)) {
         auto quantParam = GetTensorQuantParam(tensor1);
         auto min = quantParam->min;
         auto max = quantParam->max;
@@ -371,6 +374,7 @@ class CalcRealDiv : public QuantParamCalcer {
             MS_LOG(WARNING) << "Unsupported tensor dataType: " << tensor1->dataType;
             return RET_ERROR;
           }
+          outTensor->quantParams.front() = std::move(outQuantParam);
         }
       } else {
         MS_LOG(WARNING) << "Can not determine realDiv outputTensor quantParam, node " << node.name;
@@ -399,21 +403,24 @@ class CalcToSet : public QuantParamCalcer {
       return RET_ERROR;
     }
     // output
-    std::unique_ptr<QuantParamT> quantParam(new (std::nothrow) QuantParamT());
+    if (outputParamDone != node.outputIndex.size()) {
-    if (quantParam == nullptr) {
+      std::unique_ptr<QuantParamT> quantParam = std::make_unique<QuantParamT>();
-      MS_LOG(WARNING) << "new QuantParamT failed";
+      if (quantParam == nullptr) {
-      return RET_ERROR;
+        MS_LOG(WARNING) << "new QuantParamT failed";
+        return RET_ERROR;
+      }
+      quantParam->scale = (max - min) / 256;
+      MS_ASSERT(quantParam->scale != 0);
+      quantParam->zeroPoint = int32_t(std::round(256 - max / quantParam->scale));
+      quantParam->min = min;
+      quantParam->max = max;
+      quantParam->inited = true;
+      MS_ASSERT(graph->allTensors.size() > node.outputIndex.front());
+      auto &outTensor = graph->allTensors.at(node.outputIndex.front());
+      MS_ASSERT(outTensor != nullptr);
+      outTensor->quantParams.front() = std::move(quantParam);
+      outputParamDone++;
     }
-    quantParam->scale = (max - min) / 256;
-    MS_ASSERT(quantParam->scale != 0);
-    quantParam->zeroPoint = int32_t(std::round(256 - max / quantParam->scale));
-    quantParam->min = min;
-    quantParam->max = max;
-    quantParam->inited = true;
-    MS_ASSERT(graph->allTensors.size() > node.outputIndex.front());
-    auto &outTensor = graph->allTensors.at(node.outputIndex.front());
-    MS_ASSERT(outTensor != nullptr);
-    outTensor->quantParams.front() = std::move(quantParam);
     return RET_OK;
   }
 

mindspore/lite/tools/optimizer/common/gllo_utils.cc

@@ -357,6 +357,14 @@ bool IsPoolingNode(const BaseRef &n) {
   return false;
 }
 
+bool IsQuantNode(const BaseRef &n) {
+  if (utils::isa<CNodePtr>(n) || utils::isa<ValueNodePtr>(n)) {
+    auto type = opt::GetCNodeType(n);
+    return type == schema::PrimitiveType_QuantDTypeCast;
+  }
+  return false;
+}
+
 bool CheckIsAllInputsParam(const AnfNodePtr &node) {
   if (utils::isa<CNode>(node)) {
     auto cnode = node->cast<CNodePtr>();

mindspore/lite/tools/optimizer/common/gllo_utils.h

@@ -58,6 +58,8 @@ bool IsConvNode(const BaseRef &n);
 
 bool IsPoolingNode(const BaseRef &n);
 
+bool IsQuantNode(const BaseRef &n);
+
 bool CheckIsAllInputsParam(const AnfNodePtr &node);
 
 size_t GetOutputTensorNum(const AnfNodePtr &node);

mindspore/lite/tools/optimizer/fusion/quant_dtype_cast_fusion.cc

@@ -0,0 +1,47 @@
+/**
+ * Copyright 2019 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/optimizer/fusion/quant_dtype_cast_fusion.h"
+#include <memory>
+#include "src/ops/primitive_c.h"
+#include "src/ops/conv2d.h"
+#include "src/ops/depthwise_conv2d.h"
+#include "src/ops/activation.h"
+#include "schema/inner/model_generated.h"
+#include "tools/optimizer/common/gllo_utils.h"
+namespace mindspore::opt {
+namespace {
+constexpr size_t kActivationInputsLength = 2;
+}
+const BaseRef QuantDtypeCastFusion::DefinePattern() const {
+  auto quant_var = std::make_shared<CondVar>(IsQuantNode);
+  auto input_var = std::make_shared<Var>();
+  return VectorRef({quant_var, input_var});
+}
+
+const AnfNodePtr QuantDtypeCastFusion::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
+                                               const EquivPtr &) const {
+  MS_LOG(DEBUG) << "quant dtype cast fusion pass process";
+  CheckIfFuncGraphIsNull(func_graph);
+
+  CheckIfAnfNodeIsNull(node);
+  auto act_node = node->cast<CNodePtr>();
+  CheckIfCNodeIsNull(act_node);
+  CheckInputSize(act_node, kActivationInputsLength);
+  AnfNodePtr pre_node = act_node->input(1);
+  CheckIfAnfNodeIsNull(pre_node);
+  return pre_node;
+}
+}  // namespace mindspore::opt

mindspore/lite/tools/optimizer/fusion/quant_dtype_cast_fusion.h

@@ -0,0 +1,35 @@
+/**
+ * Copyright 2019 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 LITE_QUANT_DTYPE_CAST_FUSION_H
+#define LITE_QUANT_DTYPE_CAST_FUSION_H
+
+#include <string>
+#include "backend/optimizer/common/optimizer.h"
+#include "schema/inner/model_generated.h"
+
+namespace mindspore {
+namespace opt {
+class QuantDtypeCastFusion : public PatternProcessPass {
+ public:
+  explicit QuantDtypeCastFusion(bool multigraph = true, const std::string &name = "quant_dtype_cast_fusion")
+      : PatternProcessPass(name, multigraph) {}
+  ~QuantDtypeCastFusion() override = default;
+  const BaseRef DefinePattern() const override;
+  const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
+};
+}  // namespace opt
+}  // namespace mindspore
+#endif  // LITE_QUANT_DTYPE_CAST_FUSION_H