!3848 add anf fusion pass

Merge pull request !3848 from zhengjun10/master

mindspore/lite/src/gllo/common/utils.cc

@@ -22,11 +22,8 @@
 using PrimitiveTValuePtr = std::shared_ptr<mindspore::lite::PrimitiveTValue>;
 namespace mindspore {
 namespace opt {
-
 bool AnfEqual(const BaseRef &a, const BaseRef &b) {
-  if (utils::isa<Var>(a) && utils::isa<Parameter>(b)) {
-    return true;
-  } else if (utils::isa<AnfNodePtr>(a) && utils::isa<AnfNodePtr>(b)) {
+  if (utils::isa<AnfNodePtr>(a) && utils::isa<AnfNodePtr>(b)) {
     auto a_node = utils::cast<AnfNodePtr>(a);
     auto b_node = utils::cast<AnfNodePtr>(b);
     MS_EXCEPTION_IF_NULL(a_node);
@@ -80,6 +77,7 @@ bool AnfEqual(const BaseRef &a, const BaseRef &b) {
     auto b_value_node_ptr = b.m_ptr->cast<PrimitiveTValuePtr>();
     return a_value_node_ptr->GetPrimitiveT()->value.type == b_value_node_ptr->GetPrimitiveT()->value.type;
   }
+
   return a == b;
 }
 
@@ -202,22 +200,72 @@ void CheckIfVarIsNull(const VarPtr &var) {
   }
 }
 
+void CheckIfNodeIsParam(const AnfNodePtr &node) {
+  if (node != nullptr && !utils::isa<ParameterPtr>(node)) {
+    MS_LOG(EXCEPTION) << "The Node is not param.";
+  }
+}
+
 void CheckInputSize(const CNodePtr &node, const int size) {
   if (node->inputs().size() != size) {
     MS_LOG(EXCEPTION) << "The input size of node must be " << size << ", but it is" << node->inputs().size();
   }
 }
 
-schema::PrimitiveType GetCNodeType(const CNodePtr &node) {
-    auto value_primitive = node->input(0);
-    auto value_node = value_primitive->cast<ValueNodePtr>();
-    MS_ASSERT(value_node != nullptr);
-    auto value = value_node->value();
-    MS_ASSERT(value != nullptr);
+void CheckLeastInputSize(const CNodePtr &node, const int size) {
+  if (node->inputs().size() < size) {
+    MS_LOG(EXCEPTION) << "The input size of node must be " << size << ", but it is" << node->inputs().size();
+  }
+}
+
+AnfNodePtr AddNewBiasNode(float *bias_data, const FuncGraphPtr &func_graph, int kernel_num,
+                          const ParamValueLitePtr &weight_tensor) {
+  auto bias_parameter = func_graph->add_parameter();
+  MS_ASSERT(bias_parameter != nullptr);
+  std::vector<int> shape = {kernel_num};
+  auto abstract_tensor = std::make_shared<abstract::AbstractTensor>(TypeIdToType(weight_tensor->tensor_type()), shape);
+  bias_parameter->set_abstract(abstract_tensor);
+
+  ParamValueLitePtr param_value = std::make_shared<ParamValueLite>();
+  MS_ASSERT(param_value != nullptr);
+  param_value->set_tensor_addr(bias_data);
+  param_value->set_tensor_size(kernel_num * sizeof(float) / sizeof(uint8_t));
+  bias_parameter->set_default_param(param_value);
+  return bias_parameter;
+}
+
+schema::PrimitiveType GetCNodeType(const BaseRef &n) {
+  ValueNodePtr value_node;
+  if (utils::isa<CNodePtr>(n)) {
+    auto in = utils::cast<CNodePtr>(n);
+    value_node = in->input(0)->cast<ValueNodePtr>();
+  } else if (utils::isa<ValueNodePtr>(n)) {
+    value_node = utils::cast<ValueNodePtr>(n);
+  } else {
+    MS_LOG(EXCEPTION) << "only value node or cnode has type";
+    return schema::PrimitiveType_NONE;
+  }
+  MS_EXCEPTION_IF_NULL(value_node);
+  auto value = value_node->value();
+  MS_ASSERT(value != nullptr);
+  if (utils::isa<PrimitiveTValuePtr>(value)) {
     auto primitive = value->cast<PrimitiveTValuePtr>();
     MS_ASSERT(primitive != nullptr);
     return primitive->GetPrimitiveT()->value.type;
+  }
+  return schema::PrimitiveType_NONE;
 }
 
+bool IsParamNode(const BaseRef &n) {
+  return utils::isa<ParameterPtr>(n);
+}
+
+bool IsConvNode(const BaseRef &n) {
+  if (utils::isa<CNodePtr>(n) || utils::isa<ValueNodePtr>(n)) {
+    auto type = opt::GetCNodeType(n);
+    return type == schema::PrimitiveType_Conv2D || type == schema::PrimitiveType_DepthwiseConv2D;
+  }
+  return false;
+}
 }  // namespace opt
 }  // namespace mindspore

mindspore/lite/src/gllo/common/utils.h

@@ -17,12 +17,16 @@
 #ifndef MINDSPORE_LITE_SRC_PASS_COMMON_UTILS_H_
 #define MINDSPORE_LITE_SRC_PASS_COMMON_UTILS_H_
 
+#include <mindspore/lite/src/ir/primitive_t_value.h>
+#include <memory>
 #include "ir/anf.h"
 #include "ir/func_graph.h"
 #include "src/common/utils.h"
 #include "src/gllo/common/pattern_engine.h"
 #include "schema/inner/model_generated.h"
+#include "src/param_value_lite.h"
 
+using PrimitiveTValuePtr = std::shared_ptr<mindspore::lite::PrimitiveTValue>;
 namespace mindspore {
 namespace opt {
 
@@ -41,11 +45,20 @@ void CheckIfCNodeIsNull(const CNodePtr &node);
 
 void CheckIfVarIsNull(const VarPtr &var);
 
-void CheckInputSize(const CNodePtr &node, const int size);
+void CheckInputSize(const CNodePtr &node, int size);
 
-schema::PrimitiveType GetCNodeType(const CNodePtr &node);
+void CheckIfNodeIsParam(const AnfNodePtr &node);
 
+void CheckLeastInputSize(const CNodePtr &node, int size);
+
+AnfNodePtr AddNewBiasNode(float *bias_data, const FuncGraphPtr &func_graph, int kernel_num,
+                          const ParamValueLitePtr &weight_tensor);
+
+schema::PrimitiveType GetCNodeType(const BaseRef &node);
+
+bool IsParamNode(const BaseRef &n);
+
+bool IsConvNode(const BaseRef &n);
 }  // namespace opt
 }  // namespace mindspore
 #endif  // MINDSPORE_LITE_SRC_PASS_COMMON_UTILS_H_
-

mindspore/lite/src/gllo/fusion/conv_activation_fusion.cc

@@ -14,23 +14,24 @@
  * limitations under the License.
  */
 
-#include "src/gllo/fusion/conv_activation_fusion.h"
+#include "mindspore/lite/src/gllo/fusion/conv_activation_fusion.h"
 #include <memory>
-#include "schema/inner/model_generated.h"
-#include "src/ir/primitive_t_value.h"
+#include "mindspore/lite/schema/inner/model_generated.h"
+#include "mindspore/lite/src/ir/primitive_t_value.h"
 #include "mindspore/ccsrc/utils/utils.h"
-#include "src/gllo/common/utils.h"
+#include "mindspore/lite/src/gllo/common/utils.h"
 
-namespace mindspore {
-namespace opt {
+namespace mindspore::opt {
+namespace {
+constexpr size_t kActivationInputsLength = 2;
+}
 const BaseRef ConvActivationFusion::DefinePattern() const {
-  VarPtr X = std::make_shared<Var>();
-  // conv2d inputs may be 2 or 3 inputs,match move to process
+  auto conv_var = std::make_shared<CondVar>(IsConvNode);
   auto prim = new schema::PrimitiveT();
   prim->value.type = primitive_type;
   auto prim_value = std::make_shared<lite::PrimitiveTValue>(prim);
 
-  return VectorRef({prim_value, X});
+  return VectorRef({prim_value, conv_var});
 }
 
 const AnfNodePtr ConvActivationFusion::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
@@ -41,7 +42,7 @@ const AnfNodePtr ConvActivationFusion::Process(const FuncGraphPtr &func_graph, c
   CheckIfAnfNodeIsNull(node);
   auto act_node = node->cast<CNodePtr>();
   CheckIfCNodeIsNull(act_node);
-  CheckInputSize(act_node, 2);
+  CheckInputSize(act_node, kActivationInputsLength);
 
   auto act_primitive = GetValueNode<std::shared_ptr<lite::PrimitiveTValue>>(act_node->input(0));
   if (act_primitive->GetPrimitiveT()->value.AsActivation()->type != activation_type) {
@@ -52,13 +53,18 @@ const AnfNodePtr ConvActivationFusion::Process(const FuncGraphPtr &func_graph, c
   if (pre_node != nullptr && pre_node->isa<CNode>()) {
     auto conv_node = pre_node->cast<CNodePtr>();
     auto node_type = GetCNodeType(conv_node);
-    if (node_type == schema::PrimitiveType_Conv2D || node_type == schema::PrimitiveType_DepthwiseConv2D) {
-      auto primitiveT_value = GetValueNode<std::shared_ptr<lite::PrimitiveTValue>>(conv_node->input(0));
+    auto primitiveT_value = GetValueNode<std::shared_ptr<lite::PrimitiveTValue>>(conv_node->input(0));
+    MS_ASSERT(primitiveT_value);
+    if (node_type == schema::PrimitiveType_Conv2D) {
       primitiveT_value->GetPrimitiveT()->value.AsConv2D()->activationType = activation_type;
       return pre_node;
+    } else if (node_type == schema::PrimitiveType_DepthwiseConv2D) {
+      primitiveT_value->GetPrimitiveT()->value.AsDepthwiseConv2D()->activationType = activation_type;
+      return pre_node;
+    } else {
+      MS_LOG(EXCEPTION) << "conv activation pass match only conv2d or depthwise_conv2d ";
     }
   }
   return node;
 }
-}  // namespace opt
-}  // namespace mindspore
+}  // namespace mindspore::opt

mindspore/lite/src/gllo/fusion/conv_activation_fusion.h

@@ -17,16 +17,17 @@
 #ifndef MINDSPORE_LITE_SRC_PASS_FUSION_CONV_ACTIVATION_FUSION_H_
 #define MINDSPORE_LITE_SRC_PASS_FUSION_CONV_ACTIVATION_FUSION_H_
 
-#include "src/gllo/common/optimizer.h"
+#include <string>
+#include "mindspore/lite/src/gllo/common/optimizer.h"
 
 namespace mindspore {
 namespace opt {
 class ConvActivationFusion : public PatternProcessPass {
  public:
-  explicit ConvActivationFusion(bool multigraph = true,
+  explicit ConvActivationFusion(bool multigraph = true, const std::string &name = "conv_activation_fusion",
                                 schema::PrimitiveType primitive = schema::PrimitiveType_LeakyReLU,
                                 schema::ActivationType activation = schema::ActivationType_LEAKY_RELU) : primitive_type(
-      primitive), activation_type(activation), PatternProcessPass("conv_activation_fusion", multigraph) {}
+      primitive), activation_type(activation), PatternProcessPass(name, multigraph) {}
   ~ConvActivationFusion() override = default;
   const BaseRef DefinePattern() const override;
   const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;

mindspore/lite/src/gllo/fusion/conv_biasadd_fusion.cc

@@ -13,35 +13,116 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "src/gllo/fusion/conv_biasadd_fusion.h"
+#include "mindspore/lite/src/gllo/fusion/conv_biasadd_fusion.h"
+#include <mindspore/lite/src/param_value_lite.h>
 #include <memory>
-#include "schema/inner/model_generated.h"
-#include "src/ir/primitive_t_value.h"
+#include "mindspore/lite/schema/inner/model_generated.h"
+#include "mindspore/lite/src/ir/primitive_t_value.h"
 #include "mindspore/ccsrc/utils/utils.h"
-#include "src/gllo/common/utils.h"
+#include "mindspore/lite/src/gllo/common/utils.h"
 
-namespace mindspore {
-namespace opt {
-
-const BaseRef ConvBiasaddFusion::DefinePattern() const {
-  MS_LOG(DEBUG) << "Enter pattern";
-
-  VarPtr X = std::make_shared<Var>();
-  VarPtr W = std::make_shared<Var>();
-  VarPtr B = std::make_shared<Var>();
-  CheckIfVarIsNull(X);
-  CheckIfVarIsNull(W);
-  CheckIfVarIsNull(B);
-
-  auto prim1 = new schema::PrimitiveT();
-  prim1->value.type = schema::PrimitiveType_BiasAdd;
-  auto prim11 = std::make_shared<lite::PrimitiveTValue>(prim1);
+namespace mindspore::opt {
+namespace {
+constexpr size_t kAddInputsLength = 3;
+constexpr size_t kAddWEIGHTINDEX = 2;
+constexpr size_t kConvWeightIndex = 2;
+constexpr size_t kConvBiasIndex = 3;
+constexpr size_t kConvNoBiasLen = 3;
+constexpr size_t kConvWithBiasLen = 4;
+bool IsConvExtendNode(const BaseRef &n) {
+  if (utils::isa<CNodePtr>(n) || utils::isa<ValueNodePtr>(n)) {
+    auto type = opt::GetCNodeType(n);
+    return type == schema::PrimitiveType_Conv2D || type == schema::PrimitiveType_DepthwiseConv2D
+        || type == schema::PrimitiveType_DeConv2D;
+  }
+  return false;
+}
+bool IsAddNode(const BaseRef &n) {
+  if (utils::isa<CNodePtr>(n) || utils::isa<ValueNodePtr>(n)) {
+    auto type = opt::GetCNodeType(n);
+    return type == schema::PrimitiveType_Add || type == schema::PrimitiveType_BiasAdd;
+  }
+  return false;
+}
 
-  auto prim2 = new schema::PrimitiveT();
-  prim2->value.type = schema::PrimitiveType_Conv2D;
-  auto prim22 = std::make_shared<lite::PrimitiveTValue>(prim2);
+int Get_Kenrnel_nums(const CNodePtr &conv_node) {
+  MS_ASSERT(conv_node != nullptr);
+  auto value_primitive = conv_node->input(0);
+  auto value_node = value_primitive->cast<ValueNodePtr>();
+  MS_ASSERT(value_node != nullptr);
+  auto value = value_node->value();
+  MS_ASSERT(value != nullptr);
+  auto primitive = value->cast<PrimitiveTValuePtr>();
+  MS_ASSERT(primitive != nullptr);
+  auto type = primitive->GetPrimitiveT()->value.type;
+  if (type == schema::PrimitiveType_Conv2D) {
+    return primitive->GetPrimitiveT()->value.AsConv2D()->channelOut;
+  } else if (type == schema::PrimitiveType_DepthwiseConv2D) {
+    return primitive->GetPrimitiveT()->value.AsDepthwiseConv2D()->channelMultiplier
+        * primitive->GetPrimitiveT()->value.AsDepthwiseConv2D()->channelIn;
+  } else if (type == schema::PrimitiveType_DeConv2D) {
+    return primitive->GetPrimitiveT()->value.AsDeConv2D()->channelOut;
+  } else {
+    MS_LOG(ERROR) << "Unsupported opType, " << type;
+    return 0;
+  }
+}
+void GenConvNewBias(const FuncGraphPtr &func_graph, const CNodePtr &conv_node, const CNodePtr &bias_node) {
+  AnfNodePtr conv_bias_node = nullptr;
+  AnfNodePtr conv_weight_node = nullptr;
+  if (conv_node->inputs().size() == kConvNoBiasLen) {
+    conv_weight_node = conv_node->input(kConvWeightIndex);
+  } else if (conv_node->inputs().size() == kConvWithBiasLen) {
+    conv_weight_node = conv_node->input(kConvWeightIndex);
+    conv_bias_node = conv_node->input(kConvBiasIndex);
+  } else {
+    MS_LOG(EXCEPTION) << "conv node:" << conv_node->DebugString() << "inputs size must 3 or 4";
+  }
+  auto kernel_nums = Get_Kenrnel_nums(conv_node);
+  if (kernel_nums <= 0) {
+    MS_LOG(EXCEPTION) << "kernel num less than 0";
+  }
+  auto add_bias_data = new(std::nothrow) float[kernel_nums];
+  auto bias_add_weight = bias_node->input(kAddWEIGHTINDEX);
+  CheckIfNodeIsParam(bias_add_weight);
+  auto add_weight_param = bias_add_weight->cast<ParameterPtr>()->default_param();
+  auto add_weight_tensor = std::dynamic_pointer_cast<ParamValueLite>(add_weight_param);
+  auto add_weight_data = reinterpret_cast<float *>(add_weight_tensor->tensor_addr());
 
-  return VectorRef({prim11, VectorRef({prim22, X, W}), B});
+  if (add_weight_tensor->tensor_shape().empty()) {
+    if (0 != memset_s(add_bias_data, kernel_nums * sizeof(float), *add_weight_data, kernel_nums * sizeof(float))) {
+      MS_LOG(EXCEPTION) << "memset_s conv_bias_data failed";
+    }
+  } else {
+    if (0 != memcpy_s(add_bias_data, kernel_nums * sizeof(float), add_weight_data, kernel_nums * sizeof(float))) {
+      MS_LOG(EXCEPTION) << "memset_s conv_bias_data failed";
+    }
+  }
+  if (conv_bias_node != nullptr) {
+    CheckIfNodeIsParam(conv_bias_node);
+    auto conv_bias_param = conv_bias_node->cast<ParameterPtr>()->default_param();
+    auto conv_bias_tensor = std::dynamic_pointer_cast<ParamValueLite>(conv_bias_param);
+    if (conv_bias_tensor->tensor_shape().empty() || conv_bias_tensor->tensor_shape()[0] != kernel_nums) {
+      MS_LOG(EXCEPTION) << "conv_bias_node shape error";
+    }
+    auto conv_bias_data = reinterpret_cast<float *>(conv_bias_tensor->tensor_addr());
+    for (size_t i = 0; i < kernel_nums; i++) {
+      conv_bias_data[i] += add_bias_data[i];
+    }
+    delete[] add_bias_data;
+  } else {
+    auto conv_weight_param = conv_weight_node->cast<ParameterPtr>()->default_param();
+    auto conv_weight_tensor = std::dynamic_pointer_cast<ParamValueLite>(conv_weight_param);
+    auto conv_new_bias = AddNewBiasNode(add_bias_data, func_graph, kernel_nums, conv_weight_tensor);
+    conv_node->add_input(conv_new_bias);
+  }
+}
+}  // namespace
+const BaseRef ConvBiasaddFusion::DefinePattern() const {
+  auto conv_var = std::make_shared<CondVar>(IsConvExtendNode);
+  auto add_var = std::make_shared<CondVar>(IsAddNode);
+  auto weight_var = std::make_shared<CondVar>(IsParamNode);
+  return VectorRef({add_var, conv_var, weight_var});
 }
 
 const AnfNodePtr ConvBiasaddFusion::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
@@ -50,24 +131,28 @@ const AnfNodePtr ConvBiasaddFusion::Process(const FuncGraphPtr &func_graph, cons
   CheckIfFuncGraphIsNull(func_graph);
 
   CheckIfAnfNodeIsNull(node);
-  auto cnode = node->cast<CNodePtr>();
-  CheckIfCNodeIsNull(cnode);
-  CheckInputSize(cnode, 3);  // [op, conv_node, bias_node]
+  auto add_node = node->cast<CNodePtr>();
+  CheckIfCNodeIsNull(add_node);
+  CheckInputSize(add_node, kAddInputsLength);
 
-  AnfNodePtr conv_node_anf = cnode->input(1);
+  AnfNodePtr conv_node_anf = add_node->input(1);
   CheckIfAnfNodeIsNull(conv_node_anf);
   auto conv_node = conv_node_anf->cast<CNodePtr>();
   CheckIfCNodeIsNull(conv_node);
-  CheckInputSize(conv_node, 3);  // [op, X, W]
-
-  conv_node->add_input(cnode->input(2));
-
-  auto primitive = (lite::PrimitiveTValue *)(conv_node->input(0)->cast<ValueNodePtr>()->value().get());
-  primitive->GetPrimitiveT()->value.AsConv2D()->hasBias = true;
-
-  return conv_node_anf;
+  GenConvNewBias(func_graph, conv_node, add_node);
+  auto primitiveT_value = GetValueNode<std::shared_ptr<lite::PrimitiveTValue>>(conv_node->input(0));
+  MS_ASSERT(primitiveT_value);
+  auto type = primitiveT_value->GetPrimitiveT()->value.type;
+  if (type == schema::PrimitiveType_Conv2D) {
+    primitiveT_value->GetPrimitiveT()->value.AsConv2D()->hasBias = true;;
+  } else if (type == schema::PrimitiveType_DepthwiseConv2D) {
+    primitiveT_value->GetPrimitiveT()->value.AsDepthwiseConv2D()->hasBias = true;
+  } else if (type == schema::PrimitiveType_DeConv2D) {
+    primitiveT_value->GetPrimitiveT()->value.AsDeConv2D()->hasBias = true;
+  } else {
+    MS_LOG(EXCEPTION) << "Unsupported opType, " << type;
+  }
+  return conv_node;
 }
-
-}  // namespace opt
-}  // namespace mindspore
+}  // namespace mindspore::opt
 

mindspore/lite/src/gllo/fusion/conv_biasadd_fusion.h

@@ -17,7 +17,7 @@
 #ifndef MINDSPORE_LITE_SRC_PASS_FUSION_CONV_BIASADD_FUSION_H_
 #define MINDSPORE_LITE_SRC_PASS_FUSION_CONV_BIASADD_FUSION_H_
 
-#include "src/gllo/common/optimizer.h"
+#include "mindspore/lite/src/gllo/common/optimizer.h"
 
 namespace mindspore {
 namespace opt {

mindspore/lite/src/gllo/fusion/conv_bn_fusion.cc

@@ -0,0 +1,141 @@
+/**
+ * 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
+ *conv_activation_fusion.h
+ * 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 "mindspore/lite/src/gllo/fusion/conv_bn_fusion.h"
+#include <mindspore/lite/src/param_value_lite.h>
+#include <memory>
+#include "mindspore/lite/schema/inner/model_generated.h"
+#include "mindspore/lite/src/ir/primitive_t_value.h"
+#include "mindspore/ccsrc/utils/utils.h"
+#include "mindspore/lite/src/gllo/common/utils.h"
+
+namespace mindspore::opt {
+namespace {
+constexpr size_t kCaffeBNMeanIndex = 2;
+constexpr size_t kCaffeBNVarIndex = 3;
+constexpr size_t kTFBNScaleIndex = 2;
+constexpr size_t kTFBNBiasIndex = 3;
+constexpr size_t kTFBNMeanIndex = 4;
+constexpr size_t kTFBNVarIndex = 5;
+constexpr const float EPS = 1e-8;
+constexpr const float EPS_DEFAULT_FLOAT = 1e-5;
+constexpr const float POW_NUM = 0.5;
+bool IsBatchNode(const BaseRef &n) {
+  if (utils::isa<CNodePtr>(n) || utils::isa<ValueNodePtr>(n)) {
+    auto type = opt::GetCNodeType(n);
+    return type == schema::PrimitiveType_CaffeBatchNorm || type == schema::PrimitiveType_FusedBatchNorm;
+  }
+  return false;
+}
+void CalTransale(const AnfNodePtr &bn_scale_node, const AnfNodePtr &bn_var_node, float *trans_scale, float eps,
+                 int kernel_num) {
+  auto bn_var_param = bn_var_node->cast<ParameterPtr>()->default_param();
+  auto bn_var_tensor = std::dynamic_pointer_cast<ParamValueLite>(bn_var_param);
+  auto bn_var_data = reinterpret_cast<float *>(bn_var_tensor->tensor_addr());
+  // cal transScale, tf : scale/sqrt(variance + eps); caffe : 1/sqrt(variance + eps)
+  if (memcpy_s(trans_scale, kernel_num * sizeof(float), bn_var_data, kernel_num * sizeof(float)) != 0) {
+    MS_LOG(EXCEPTION) << "memcpy_s transScale error";
+    return;
+  }
+  // 1/sqrt(variance + eps)
+  for (int32_t i = 0; i < kernel_num; i++) {
+    float tmp = trans_scale[i] + eps;
+    tmp = pow(tmp, POW_NUM);
+    trans_scale[i] = 1 / tmp;
+  }
+  if (bn_scale_node != nullptr) {
+    auto bn_scale_param = bn_scale_node->cast<ParameterPtr>()->default_param();
+    auto bn_scale_tensor = std::dynamic_pointer_cast<ParamValueLite>(bn_scale_param);
+    auto bn_scale_data = reinterpret_cast<float *>(bn_scale_tensor->tensor_addr());
+    // scale/sqrt(variance + eps)
+    for (int32_t i = 0; i < kernel_num; i++) {
+      trans_scale[i] *= bn_scale_data[i];
+    }
+  }
+}
+void CalTransBias(const AnfNodePtr &bn_mean_node, const AnfNodePtr &bn_bias_node, const float *trans_scale,
+                  float *trans_bias, int kernel_num) {
+  auto bn_mean_param = bn_mean_node->cast<ParameterPtr>()->default_param();
+  auto bn_mean_tensor = std::dynamic_pointer_cast<ParamValueLite>(bn_mean_param);
+  auto bn_mean_data = reinterpret_cast<float *>(bn_mean_tensor->tensor_addr());
+  // cal transBias, tf : -scale*mean/sqrt(variance + eps) + bias; caffe : -mean/sqrt(variance + eps)
+  // -mean/sqrt(variance + eps)
+  for (int32_t i = 0; i < kernel_num; i++) {
+    trans_bias[i] = -bn_mean_data[i] * trans_scale[i];
+  }
+
+  if (bn_bias_node != nullptr) {
+    auto bn_bias_param = bn_bias_node->cast<ParameterPtr>()->default_param();
+    auto bn_bias_tensor = std::dynamic_pointer_cast<ParamValueLite>(bn_bias_param);
+    auto bn_bias_data = reinterpret_cast<float *>(bn_bias_tensor->tensor_addr());
+    // -scale*mean/sqrt(variance + eps) + bias
+    for (int32_t i = 0; i < kernel_num; i++) {
+      trans_bias[i] += bn_bias_data[i];
+    }
+  }
+}
+}  // namespace
+const BaseRef ConvBatchNormFusion::DefinePattern() const {
+  auto conv_var = std::make_shared<CondVar>(IsConvNode);
+  auto bn_var = std::make_shared<CondVar>(IsBatchNode);
+  auto bn_mean_var = std::make_shared<CondVar>(IsParamNode);
+  auto bn_variable_var = std::make_shared<CondVar>(IsParamNode);
+  auto bn_other_var = std::make_shared<SeqVar>();
+  return VectorRef({bn_var, conv_var, bn_mean_var, bn_variable_var, bn_other_var});;
+}
+// BatchNorm weight Tensor definition:
+// caffe
+//   estimated_mean  --0
+//   estimated_variance  --1
+// tensorflow
+//   scale    -- 0
+//   bias        --1
+//   estimated_mean  --2
+//   estimated_variance  --3
+const void ConvBatchNormFusion::InitTransParam(const CNodePtr &bn_node, int kernel_num, float *trans_scale,
+                                                       float *trans_bias) const {
+  MS_ASSERT(bn_node != nullptr);
+  AnfNodePtr bn_mean_node = nullptr;
+  AnfNodePtr bn_variance_node = nullptr;
+  AnfNodePtr bn_scale_node = nullptr;
+  AnfNodePtr bn_bias_node = nullptr;
+  float eps = 0;
+  auto primitiveT_value = GetValueNode<std::shared_ptr<lite::PrimitiveTValue>>(bn_node->input(0));
+  if (GetCNodeType(bn_node) == schema::PrimitiveType_CaffeBatchNorm) {
+    bn_mean_node = bn_node->input(kCaffeBNMeanIndex);
+    bn_variance_node = bn_node->input(kCaffeBNVarIndex);
+    CheckIfNodeIsParam(bn_mean_node);
+    CheckIfNodeIsParam(bn_variance_node);
+    eps = primitiveT_value->GetPrimitiveT()->value.AsCaffeBatchNorm()->epsilon;
+  } else if (GetCNodeType(bn_node) == schema::PrimitiveType_FusedBatchNorm) {
+    bn_scale_node = bn_node->input(kTFBNScaleIndex);
+    bn_bias_node = bn_node->input(kTFBNBiasIndex);
+    bn_mean_node = bn_node->input(kTFBNMeanIndex);
+    bn_variance_node = bn_node->input(kTFBNVarIndex);
+    eps = primitiveT_value->GetPrimitiveT()->value.AsFusedBatchNorm()->epsilon;
+  } else {
+    MS_LOG(EXCEPTION) << "not caffe or tf batchnorm op.";
+  }
+  CheckIfNodeIsParam(bn_mean_node);
+  CheckIfNodeIsParam(bn_variance_node);
+  if (eps < EPS) {
+    eps = EPS_DEFAULT_FLOAT;
+  }
+
+  CalTransale(bn_scale_node, bn_variance_node, trans_scale, eps, kernel_num);
+  CalTransBias(bn_mean_node, bn_bias_node, trans_scale, trans_bias, kernel_num);
+}
+}  // namespace mindspore::opt

mindspore/lite/src/gllo/fusion/conv_bn_fusion.h

@@ -0,0 +1,31 @@
+/**
+ * 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
+ *conv_activation_fusion.h
+ * 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 MINDSPORE_LITE_SRC_PASS_FUSION_CONV_BN_FUSION_H_
+#define MINDSPORE_LITE_SRC_PASS_FUSION_CONV_BN_FUSION_H_
+
+#include "mindspore/lite/src/gllo/fusion/conv_transform_fusion.h"
+
+namespace mindspore::opt {
+class ConvBatchNormFusion : public ConvTransformFusion {
+ public:
+  explicit ConvBatchNormFusion(bool multigraph = true) : ConvTransformFusion(multigraph, "conv_batchnorm_fusion") {}
+  ~ConvBatchNormFusion() override = default;
+  const BaseRef DefinePattern() const override;
+  const void InitTransParam(const CNodePtr &, int, float *, float *) const override;
+};
+}  // namespace mindspore::opt
+#endif  // MINDSPORE_LITE_SRC_PASS_FUSION_CONV_BN_FUSION_H_

mindspore/lite/src/gllo/fusion/conv_scale_fusion.cc

@@ -14,78 +14,51 @@
  * limitations under the License.
  */
 
-#include "src/gllo/fusion/conv_scale_fusion.h"
+#include "mindspore/lite/src/gllo/fusion/conv_scale_fusion.h"
 #include <memory>
-#include "schema/inner/model_generated.h"
-#include "src/ir/primitive_t_value.h"
-#include "src/param_value_lite.h"
+#include "mindspore/lite/src/param_value_lite.h"
+#include "mindspore/lite/schema/inner/model_generated.h"
+#include "mindspore/lite/src/ir/primitive_t_value.h"
 #include "mindspore/ccsrc/utils/utils.h"
-#include "src/gllo/common/utils.h"
+#include "mindspore/lite/src/gllo/common/utils.h"
 #include "include/errorcode.h"
 
-namespace mindspore {
-namespace opt {
-const BaseRef ConvScaleFusion::DefinePattern() const {
-  VarPtr X = std::make_shared<Var>();
-  // conv2d inputs may be 2 or 3 inputs,match move to process
-  auto prim = new schema::PrimitiveT();
-  prim->value.type = schema::PrimitiveType_Scale;
-  auto prim_value = std::make_shared<lite::PrimitiveTValue>(prim);
-
-  return VectorRef({prim_value, X});
+namespace mindspore::opt {
+namespace {
+constexpr size_t kScaleWeightIndex = 2;
+constexpr size_t kScaleBiasIndex = 3;
+constexpr size_t kScaleNoBiasLen = 3;
+constexpr size_t kScaleWithBiasLen = 4;
+bool IsScaleNode(const BaseRef &n) {
+  if (utils::isa<CNodePtr>(n) || utils::isa<ValueNodePtr>(n)) {
+    auto type = opt::GetCNodeType(n);
+    return type == schema::PrimitiveType_Scale;
+  }
+  return false;
 }
+}  // namespace
 
-const AnfNodePtr ConvScaleFusion::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
-                                          const EquivPtr &) const {
-  MS_LOG(DEBUG) << "conv activation pass process";
-  CheckIfFuncGraphIsNull(func_graph);
-
-  CheckIfAnfNodeIsNull(node);
-  auto scale_node = node->cast<CNodePtr>();
-  CheckIfCNodeIsNull(scale_node);
-  CheckInputSize(scale_node, 2);
+const BaseRef ConvScaleFusion::DefinePattern() const {
+  auto conv_var = std::make_shared<CondVar>(IsConvNode);
+  auto bn_var = std::make_shared<CondVar>(IsScaleNode);
+  auto weight_var = std::make_shared<CondVar>(IsParamNode);
+  auto bias_var = std::make_shared<SeqVar>();
 
-  AnfNodePtr pre_node = scale_node->input(1);
-  CheckIfAnfNodeIsNull(pre_node);
-  if (pre_node != nullptr && pre_node->isa<CNode>()) {
-    auto conv_node = pre_node->cast<CNodePtr>();
-    auto node_type = GetCNodeType(conv_node);
-    if (node_type == schema::PrimitiveType_Conv2D || node_type == schema::PrimitiveType_DepthwiseConv2D) {
-      return DoFusion(conv_node, scale_node);
-    }
-  }
-  return node;
+  return VectorRef({bn_var, conv_var, weight_var, bias_var});
 }
-const AnfNodePtr ConvScaleFusion::DoFusion(const CNodePtr &conv_node, const CNodePtr &scale_node) const {
-  if (scale_node->inputs().size() == 3) {
-    GetTransParam(scale_node->input(2), nullptr);
-  } else if (scale_node->inputs().size() == 4) {
-    // todo add bias fusion zhengjun10
-    GetTransParam(scale_node->input(2), scale_node->input(3));
-  } else {
-    MS_LOG(ERROR) << "scale inputs size is error:" << scale_node->DebugString();
-    return nullptr;
-  }
-
-  AnfNodePtr conv_weight_node;
-  if (conv_node->inputs().size() == 3) {
-     conv_weight_node = conv_node->input(2);
+const void ConvScaleFusion::InitTransParam(const CNodePtr &scale_node, int kernel_num, float *trans_scale,
+                                           float *trans_bias) const {
+  MS_ASSERT(scale_node != nullptr);
+  AnfNodePtr scale_weight_node;
+  AnfNodePtr scale_bias_node;
+  if (scale_node->inputs().size() == kScaleNoBiasLen) {
+    scale_weight_node = scale_node->input(kScaleWeightIndex);
+  } else if (scale_node->inputs().size() == kScaleWithBiasLen) {
+    scale_weight_node = scale_node->input(kScaleWeightIndex);
+    scale_bias_node = scale_node->input(kScaleBiasIndex);
   } else {
-    MS_LOG(ERROR) << "scale inputs size is error:" << scale_node->DebugString();
-    return nullptr;
+    MS_LOG(EXCEPTION) << "Scale should has 2 or 3 input tensors, current inputs is" << scale_node->inputs().size();
   }
-  auto conv_weight_param = conv_weight_node->cast<ParameterPtr>()->default_param();
-  auto weight_value = std::dynamic_pointer_cast<ParamValueLite>(conv_weight_param);
-  auto old_conv_weight = reinterpret_cast<const float *>(weight_value->tensor_addr());
-
-  auto new_conv_weight = new(std::nothrow) float[weight_value->tensor_shape_size()];
-  CalNewWeightTensor(old_conv_weight, new_conv_weight, weight_value->tensor_shape_size());
-  weight_value->set_tensor_addr(new_conv_weight);
-  return conv_node;
-}
-
-const lite::STATUS ConvScaleFusion::GetTransParam(const AnfNodePtr &scale_weight_node,
-                                                  const AnfNodePtr &scale_bias_node) const {
   if (!scale_weight_node->isa<Parameter>()) {
     MS_LOG(EXCEPTION) << "scale weight node not paramter node";
   }
@@ -96,31 +69,17 @@ const lite::STATUS ConvScaleFusion::GetTransParam(const AnfNodePtr &scale_weight
   auto weight_value = std::dynamic_pointer_cast<ParamValueLite>(scale_weight_param);
   auto weight_data = reinterpret_cast<const float *>(weight_value->tensor_addr());
 
-  if (0 != memcpy_s(trans_scale, kernel_nums * sizeof(float), weight_data, kernel_nums * sizeof(float))) {
-    MS_LOG(ERROR) << "memcpy_s transScale failed";
-    return lite::RET_ERROR;
+  if (0 != memcpy_s(trans_scale, kernel_num * sizeof(float), weight_data, kernel_num * sizeof(float))) {
+    MS_LOG(EXCEPTION) << "memcpy_s transScale failed";
   }
-  return lite::RET_OK;
-}
 
-const lite::STATUS ConvScaleFusion::CalNewWeightTensor(const float *oldWeightTensor, float *newWeightTensor,
-                                                       const size_t tensor_shape_size) const {
-  MS_ASSERT(oldWeightTensor != nullptr);
-  if (0 != memset_s(newWeightTensor, tensor_shape_size * sizeof(float), 0, tensor_shape_size * sizeof(float))) {
-    MS_LOG(ERROR) << "memset newWeightData failed";
-    return lite::RET_ERROR;
-  }
-  if (kernel_nums == 0) {
-    MS_LOG(ERROR) << "kernel nums is 0";
-    return lite::RET_ERROR;
-  }
-  auto kernel_size = tensor_shape_size / kernel_nums;
-  for (size_t i = 0; i < kernel_nums; i++) {
-    for (size_t j = 0; j < kernel_size; j++) {
-      newWeightTensor[i * kernel_size + j] = oldWeightTensor[i * kernel_size + j] * trans_scale[i];
+  if (scale_bias_node != nullptr) {
+    auto scale_bias_param = scale_bias_node->cast<ParameterPtr>()->default_param();
+    auto bias_value = std::dynamic_pointer_cast<ParamValueLite>(scale_bias_param);
+    auto bias_data = reinterpret_cast<const float *>(bias_value->tensor_addr());
+    if (0 != memcpy_s(trans_bias, kernel_num * sizeof(float), bias_data, kernel_num * sizeof(float))) {
+      MS_LOG(EXCEPTION) << "memcpy_s transScale failed";
     }
   }
-  return lite::RET_OK;
 }
-}  // namespace opt
-}  // namespace mindspore
+}  // namespace mindspore::opt

mindspore/lite/src/gllo/fusion/conv_scale_fusion.h

@@ -17,24 +17,15 @@
 #ifndef MINDSPORE_LITE_SRC_PASS_FUSION_CONV_SCALE_FUSION_H_
 #define MINDSPORE_LITE_SRC_PASS_FUSION_CONV_SCALE_FUSION_H_
 
-#include "src/gllo/common/optimizer.h"
+#include "mindspore/lite/src/gllo/fusion/conv_transform_fusion.h"
 
-namespace mindspore {
-namespace opt {
-class ConvScaleFusion : public PatternProcessPass {
+namespace mindspore::opt {
+class ConvScaleFusion : public ConvTransformFusion {
  public:
-  explicit ConvScaleFusion(bool multigraph = true) : PatternProcessPass("conv_scale_fusion", multigraph) {}
+  explicit ConvScaleFusion(bool multigraph = true) : ConvTransformFusion(multigraph, "conv_scale_fusion") {}
   ~ConvScaleFusion() override = default;
   const BaseRef DefinePattern() const override;
-  const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
-  const AnfNodePtr DoFusion(const CNodePtr &, const CNodePtr &) const;
-  const lite::STATUS GetTransParam(const AnfNodePtr &, const AnfNodePtr &) const;
-  const lite::STATUS CalNewWeightTensor(const float *, float *, const size_t) const;
- private:
-  float *trans_scale = nullptr;
-  int kernel_nums = 0;
+  const void InitTransParam(const CNodePtr &, int, float *, float *) const override;
 };
-}  // namespace opt
-}  // namespace mindspore
+}  // namespace mindspore::opt
 #endif  // MINDSPORE_LITE_SRC_PASS_FUSION_CONV_SCALE_FUSION_H_
-

mindspore/lite/src/gllo/fusion/conv_transform_fusion.cc

@@ -0,0 +1,201 @@
+/**
+ * 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
+ *conv_activation_fusion.h
+ * 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 "mindspore/lite/src/gllo/fusion/conv_transform_fusion.h"
+#include <memory>
+#include "mindspore/lite/src/param_value_lite.h"
+#include "mindspore/lite/schema/inner/model_generated.h"
+#include "mindspore/lite/src/ir/primitive_t_value.h"
+#include "mindspore/ccsrc/utils/utils.h"
+#include "mindspore/lite/src/gllo/common/utils.h"
+#include "include/errorcode.h"
+
+namespace mindspore::opt {
+namespace {
+constexpr size_t kConvWeightIndex = 2;
+constexpr size_t kConvBiasIndex = 3;
+constexpr size_t kConvNoBiasLen = 3;
+constexpr size_t kConvWithBiasLen = 4;
+
+int Get_Kenrnel_nums(const CNodePtr &conv_node) {
+  MS_ASSERT(conv_node != nullptr);
+  auto value_primitive = conv_node->input(0);
+  auto value_node = value_primitive->cast<ValueNodePtr>();
+  MS_ASSERT(value_node != nullptr);
+  auto value = value_node->value();
+  MS_ASSERT(value != nullptr);
+  auto primitive = value->cast<PrimitiveTValuePtr>();
+  MS_ASSERT(primitive != nullptr);
+  auto type = primitive->GetPrimitiveT()->value.type;
+  if (type == schema::PrimitiveType_Conv2D) {
+    return primitive->GetPrimitiveT()->value.AsConv2D()->channelOut;
+  } else if (type == schema::PrimitiveType_DepthwiseConv2D) {
+    return primitive->GetPrimitiveT()->value.AsDepthwiseConv2D()->channelMultiplier
+        * primitive->GetPrimitiveT()->value.AsDepthwiseConv2D()->channelIn;
+  } else {
+    MS_LOG(ERROR) << "Unsupported opType, " << type;
+    return 0;
+  }
+}
+}  // namespace
+
+const AnfNodePtr ConvTransformFusion::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
+                                              const EquivPtr &) const {
+  MS_LOG(DEBUG) << "conv activation pass process";
+  CheckIfFuncGraphIsNull(func_graph);
+
+  CheckIfAnfNodeIsNull(node);
+  // transform node means scale,bn
+  auto transform_node = node->cast<CNodePtr>();
+  CheckIfCNodeIsNull(transform_node);
+  CheckLeastInputSize(transform_node, 2);
+
+  auto pre_node = transform_node->input(1);
+  auto conv_node = pre_node->cast<CNodePtr>();
+
+  int kernel_nums = Get_Kenrnel_nums(conv_node);
+  if (kernel_nums <= 0) {
+    MS_LOG(ERROR) << "Unsupported conv node, " << conv_node->DebugString();
+    return node;
+  }
+  auto trans_scale = new(std::nothrow) float[kernel_nums];
+  auto trans_bias = new(std::nothrow) float[kernel_nums];
+  GenTransParam(transform_node, kernel_nums, trans_scale, trans_bias);
+  GenNewConvTensor(func_graph, conv_node, kernel_nums, trans_scale, trans_bias);
+  delete[] trans_bias;
+  delete[] trans_scale;
+  return pre_node;
+}
+
+const void ConvTransformFusion::GenTransParam(const CNodePtr &transform_node, int kernel_nums,
+                                              float *trans_scale, float *trans_bias) const {
+  if (trans_scale == nullptr) {
+    MS_LOG(EXCEPTION) << "new transScale failed";
+  }
+  if (trans_bias == nullptr) {
+    MS_LOG(EXCEPTION) << "new transBias failed";
+  }
+  if (0 != memset_s(trans_scale, kernel_nums * sizeof(float), 0, kernel_nums * sizeof(float))) {
+    MS_LOG(EXCEPTION) << "memset transScale failed";
+  }
+  if (0 != memset_s(trans_bias, kernel_nums * sizeof(float), 0, kernel_nums * sizeof(float))) {
+    MS_LOG(EXCEPTION) << "memset transBias failed";
+  }
+
+  InitTransParam(transform_node, kernel_nums, trans_scale, trans_bias);
+}
+
+const void ConvTransformFusion::GenNewConvTensor(const FuncGraphPtr &func_graph, const CNodePtr &conv_node,
+                                                 int kernel_num, const float *trans_scale, const float *trans_bias)
+const {
+  MS_ASSERT(conv_node != nullptr);
+  AnfNodePtr conv_weight_node = nullptr;
+  AnfNodePtr conv_bias_node = nullptr;
+  if (conv_node->inputs().size() == kConvNoBiasLen) {
+    conv_weight_node = conv_node->input(kConvWeightIndex);
+  } else if (conv_node->inputs().size() == kConvWithBiasLen) {
+    conv_weight_node = conv_node->input(kConvWeightIndex);
+    conv_bias_node = conv_node->input(kConvBiasIndex);
+  } else {
+    MS_LOG(ERROR) << "conv node:" << conv_node->DebugString() << "inputs size must 3 or 4";
+    return;
+  }
+  if (!conv_weight_node->isa<Parameter>()) {
+    MS_LOG(EXCEPTION) << "scale weight node not paramter node";
+  }
+  if (conv_bias_node != nullptr && !conv_bias_node->isa<Parameter>()) {
+    MS_LOG(EXCEPTION) << "scale bias node not paramter node";
+  }
+
+  auto conv_weight_param = conv_weight_node->cast<ParameterPtr>()->default_param();
+  auto weight_tensor = std::dynamic_pointer_cast<ParamValueLite>(conv_weight_param);
+  auto weight_data = reinterpret_cast<float *>(weight_tensor->tensor_addr());
+  if (kernel_num <= 0) {
+    MS_LOG(EXCEPTION) << "kernel num less than 0";
+  }
+  auto kernel_size = weight_tensor->tensor_shape_size() / kernel_num;
+
+  CalNewWeightTensor(weight_data, kernel_num, kernel_size, trans_scale);
+
+  float *bias_data = nullptr;
+  // conv has bias,bias_flag true
+  bool bias_flag = false;
+  if (conv_bias_node != nullptr) {
+    auto bias_weight_param = conv_weight_node->cast<ParameterPtr>()->default_param();
+    auto bias_tensor = std::dynamic_pointer_cast<ParamValueLite>(bias_weight_param);
+    bias_data = reinterpret_cast<float *>(bias_tensor->tensor_addr());
+    bias_flag = true;
+  } else {
+    bias_data = new(std::nothrow) float[kernel_num];
+  }
+  CalNewBiasTensor(bias_data, kernel_num, bias_flag, trans_scale, trans_bias);
+  if (!bias_flag) {
+    auto bias_node = AddNewBiasNode(bias_data, func_graph, kernel_num, weight_tensor);
+    conv_node->add_input(bias_node);
+  }
+}
+const void ConvTransformFusion::CalNewWeightTensor(float *weight_data, int kernel_num, int kernel_size,
+                                                   const float *trans_scale) const {
+  MS_ASSERT(weight_data != nullptr);
+  auto tmp_weight_data = new(std::nothrow) float[kernel_num * kernel_size];
+  MS_ASSERT(new_weight_data != nullptr);
+  auto data_size = kernel_num * kernel_size * sizeof(float);
+  if (0 != memset_s(tmp_weight_data, data_size, 0, data_size)) {
+    MS_LOG(EXCEPTION) << "memset newWeightData failed";
+    return;
+  }
+
+  for (size_t i = 0; i < kernel_num; i++) {
+    for (size_t j = 0; j < kernel_size; j++) {
+      tmp_weight_data[i * kernel_size + j] = weight_data[i * kernel_size + j] * trans_scale[i];
+    }
+  }
+
+  auto ret = memcpy_s(weight_data, data_size, tmp_weight_data, data_size);
+  if (ret != EOK) {
+    MS_LOG(EXCEPTION) << "memcpy error: " << ret;
+  }
+
+  delete[] tmp_weight_data;
+}
+const void ConvTransformFusion::CalNewBiasTensor(float *bias_data, int kernel_num, bool bias_flag,
+                                                 const float *trans_scale, const float *trans_bias) const {
+  MS_ASSERT(bias_data != nullptr);
+  if (bias_flag) {
+    auto tmp_bias_data = new(std::nothrow) float[kernel_num];
+    if (0 != memset_s(bias_data, kernel_num * sizeof(float), 0, kernel_num * sizeof(float))) {
+      MS_LOG(EXCEPTION) << "memset bias data failed";
+    }
+    for (size_t i = 0; i < kernel_num; i++) {
+      tmp_bias_data[i] = bias_data[i] * trans_scale[i] + trans_bias[i];
+    }
+
+    auto ret = memcpy_s(bias_data, kernel_num * sizeof(float), tmp_bias_data, kernel_num * sizeof(float));
+    if (ret != EOK) {
+      MS_LOG(EXCEPTION) << "memcpy error: " << ret;
+    }
+    delete[] tmp_bias_data;
+  } else {
+    if (0 != memset_s(bias_data, kernel_num * sizeof(float), 0, kernel_num * sizeof(float))) {
+      MS_LOG(EXCEPTION) << "memset bias data failed";
+    }
+    auto ret = memcpy_s(bias_data, kernel_num * sizeof(float), trans_bias, kernel_num * sizeof(float));
+    if (ret != EOK) {
+      MS_LOG(EXCEPTION) << "memcpy error: " << ret;
+    }
+  }
+}
+}  // namespace mindspore::opt

mindspore/lite/src/gllo/fusion/conv_transform_fusion.h

@@ -0,0 +1,37 @@
+/**
+ * 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
+ *conv_activation_fusion.h
+ * 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 MINDSPORE_LITE_SRC_PASS_FUSION_CONV_TRANSFORM_FUSION_H_
+#define MINDSPORE_LITE_SRC_PASS_FUSION_CONV_TRANSFORM_FUSION_H_
+
+#include <string>
+#include "mindspore/lite/src/gllo/common/optimizer.h"
+
+namespace mindspore::opt {
+class ConvTransformFusion : public PatternProcessPass {
+ public:
+  explicit ConvTransformFusion(bool multigraph = true, const std::string &name = "conv_transform_fusion")
+      : PatternProcessPass(name, multigraph) {}
+  ~ConvTransformFusion() override = default;
+  const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
+  const void GenTransParam(const CNodePtr &, int, float *, float *) const;
+  virtual const void InitTransParam(const CNodePtr &, int, float *, float *) const = 0;
+  const void GenNewConvTensor(const FuncGraphPtr &, const CNodePtr &, int, const float *, const float *) const;
+  const void CalNewWeightTensor(float *, int, int, const float *) const;
+  const void CalNewBiasTensor(float *, int, bool, const float *, const float *) const;
+};
+}  // namespace mindspore::opt
+#endif  // MINDSPORE_LITE_SRC_PASS_FUSION_CONV_TRANSFORM_FUSION_H_

mindspore/lite/test/CMakeLists.txt

@@ -199,7 +199,9 @@ if(BUILD_CONVERTER)
             ${LITE_DIR}/src/gllo/common/utils.cc
             ${LITE_DIR}/src/gllo/fusion/conv_biasadd_fusion.cc
             ${LITE_DIR}/src/gllo/fusion/conv_activation_fusion.cc
+            ${LITE_DIR}/src/gllo/fusion/conv_transform_fusion.cc
             ${LITE_DIR}/src/gllo/fusion/conv_scale_fusion.cc
+            ${LITE_DIR}/src/gllo/fusion/conv_bn_fusion.cc
             )
 endif()
 ### train

mindspore/lite/tools/converter/CMakeLists.txt

@@ -79,7 +79,9 @@ file(GLOB_RECURSE CONVERTER_SRC RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
         ${CMAKE_CURRENT_SOURCE_DIR}/../../src/gllo/common/utils.cc
         ${CMAKE_CURRENT_SOURCE_DIR}/../../src/gllo/fusion/conv_biasadd_fusion.cc
         ${CMAKE_CURRENT_SOURCE_DIR}/../../src/gllo/fusion/conv_activation_fusion.cc
+        ${CMAKE_CURRENT_SOURCE_DIR}/../../src/gllo/fusion/conv_transform_fusion.cc
         ${CMAKE_CURRENT_SOURCE_DIR}/../../src/gllo/fusion/conv_scale_fusion.cc
+        ${CMAKE_CURRENT_SOURCE_DIR}/../../src/gllo/fusion/conv_bn_fusion.cc
         )
 
 add_subdirectory(parser/caffe)

mindspore/lite/tools/converter/anf_transform.cc

@@ -18,8 +18,10 @@
 #include <memory>
 #include <string>
 #include "utils/log_adapter.h"
-#include "src/gllo/fusion/conv_biasadd_fusion.h"
-
+#include "mindspore/lite/src/gllo/fusion/conv_biasadd_fusion.h"
+#include "mindspore/lite/src/gllo/fusion/conv_activation_fusion.h"
+#include "mindspore/lite/src/gllo/fusion/conv_scale_fusion.h"
+#include "mindspore/lite/src/gllo/fusion/conv_bn_fusion.h"
 
 using std::string;
 namespace mindspore {
@@ -34,8 +36,13 @@ FuncGraphPtr AnfTransform::Transform(const FuncGraphPtr &old_graph) {
   // return old_graph;
   auto optimizer = std::make_shared<opt::GraphOptimizer>();
   auto pm = std::make_shared<opt::PassManager>();
-  auto pass = std::make_shared<opt::ConvBiasaddFusion>();
-  pm->AddPass(pass);
+  pm->AddPass(std::make_shared<opt::ConvBiasaddFusion>());
+  pm->AddPass(std::make_shared<opt::ConvBatchNormFusion>());
+  pm->AddPass(std::make_shared<opt::ConvScaleFusion>());
+  pm->AddPass(std::make_shared<opt::ConvActivationFusion>(true, "conv_relu", schema::PrimitiveType_Activation,
+                                                         schema::ActivationType_RELU));
+  pm->AddPass(std::make_shared<opt::ConvActivationFusion>(true, "conv_relu6", schema::PrimitiveType_Activation,
+                                                         schema::ActivationType_RELU6));
   optimizer->AddPassManager(pm);
   FuncGraphPtr new_graph = optimizer->Optimize(old_graph);
   return new_graph;