diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/trt/trt_kernel.cc b/mindspore/ccsrc/backend/kernel_compiler/gpu/trt/trt_kernel.cc
index c416d1f318..6d24c02760 100644
--- a/mindspore/ccsrc/backend/kernel_compiler/gpu/trt/trt_kernel.cc
+++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/trt/trt_kernel.cc
@@ -68,7 +68,7 @@ bool TrtKernel::Init(const CNodePtr &kernel_node) {
   return true;
 }
 
-TrtKernel::ReleaseResource() {
+void TrtKernel::ReleaseResource() {
   // Make sure destroy trt object before TrtLoader destruct.
   context_.reset();
   engine_.reset();
diff --git a/mindspore/ccsrc/backend/optimizer/gpu/adam_fusion.cc b/mindspore/ccsrc/backend/optimizer/gpu/adam_fusion.cc
index 9caedbe23c..b28e95b2d5 100644
--- a/mindspore/ccsrc/backend/optimizer/gpu/adam_fusion.cc
+++ b/mindspore/ccsrc/backend/optimizer/gpu/adam_fusion.cc
@@ -50,6 +50,33 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(CNodePtr node) {
   builder.SetOutputsFormat(outputs_format);
   return builder.Build();
 }
+
+AnfNodePtr RelpaceOutputEdge(const AnfNodePtr &node, CNodePtr adam, AnfNodePtr u_input) {
+  // Replace the parameters of the last UpdateState to maintain
+  // the execution order of FusedAdam and the following operators.
+  // n represents the operator assign_v in {prim::kPrimDepend, next_param, assign_v}
+  const auto &n = node->cast<CNodePtr>()->input(2);
+  MS_EXCEPTION_IF_NULL(n);
+  const auto &fg = n->func_graph();
+  MS_EXCEPTION_IF_NULL(fg);
+  auto mgr = fg->manager();
+  MS_EXCEPTION_IF_NULL(mgr);
+  auto &node_users = mgr->node_users();
+  auto iter = node_users.find(n);
+  if (iter == node_users.end()) {
+    MS_LOG(EXCEPTION) << "Can not find node : " << n->DebugString();
+  }
+
+  auto &users = iter->second;
+  for (auto &user : users) {
+    if (IsPrimitiveCNode(user.first, prim::kPrimUpdateState)) {
+      (user.first)->cast<CNodePtr>()->set_input(1, u_input);
+      (user.first)->cast<CNodePtr>()->set_input(2, adam);
+      break;
+    }
+  }
+  return adam;
+}
 }  // namespace
 
 const BaseRef AdamFusion::DefinePattern() const {
@@ -118,51 +145,19 @@ const AnfNodePtr AdamFusion::Process(const FuncGraphPtr &graph, const AnfNodePtr
   // Fused into a FusedAdam operator.
   auto prim = std::make_shared<Primitive>(kFusedAdamName);
   MS_EXCEPTION_IF_NULL(prim);
-  std::vector<AnfNodePtr> inputs = {NewValueNode(prim),
-                                    beta1_input,
-                                    one_sub_beta1_input,
-                                    beta2_input,
-                                    one_sub_beta2_input,
-                                    eps_input,
-                                    lr_input,
-                                    param,
-                                    m_input,
-                                    v_input,
-                                    gradient_input};
+  auto prim_value = NewValueNode(prim);
+  std::vector<AnfNodePtr> inputs = {
+    prim_value, beta1_input, one_sub_beta1_input, beta2_input, one_sub_beta2_input, eps_input, lr_input, param,
+    m_input,    v_input,     gradient_input};
   auto adam = graph->NewCNode(inputs);
   MS_EXCEPTION_IF_NULL(adam);
   auto types = {AnfAlgo::GetOutputInferDataType(node, 0)};
   auto shapes = {AnfAlgo::GetOutputInferShape(node, 0)};
   AnfAlgo::SetOutputInferTypeAndShape(types, shapes, adam.get());
   adam->set_scope(node->scope());
-
   auto build_info = GenerateKernelBuildInfo(adam);
   AnfAlgo::SetSelectKernelBuildInfo(build_info, adam.get());
-
-  // Replace the parameters of the last UpdateState to maintain
-  // the execution order of FusedAdam and the following operators.
-  // n represents the operator assign_v in {prim::kPrimDepend, next_param, assign_v}
-  auto n = node->cast<CNodePtr>()->input(2);
-  auto fg = n->func_graph();
-  MS_EXCEPTION_IF_NULL(fg);
-  auto mgr = fg->manager();
-  MS_EXCEPTION_IF_NULL(mgr);
-  auto &node_users = mgr->node_users();
-  auto iter = node_users.find(n);
-  if (iter == node_users.end()) {
-    MS_LOG(EXCEPTION) << "Can not find node : " << n->DebugString();
-  }
-
-  auto &users = iter->second;
-  for (auto &user : users) {
-    if (IsPrimitiveCNode(user.first, prim::kPrimUpdateState)) {
-      (user.first)->cast<CNodePtr>()->set_input(1, u_input);
-      (user.first)->cast<CNodePtr>()->set_input(2, adam);
-      break;
-    }
-  }
-
-  return adam;
+  return RelpaceOutputEdge(node, adam, u_input);
 }
 }  // namespace opt
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/backend/optimizer/gpu/adam_weight_decay_fusion.cc b/mindspore/ccsrc/backend/optimizer/gpu/adam_weight_decay_fusion.cc
index 0a2924790c..ee3a7dffc5 100644
--- a/mindspore/ccsrc/backend/optimizer/gpu/adam_weight_decay_fusion.cc
+++ b/mindspore/ccsrc/backend/optimizer/gpu/adam_weight_decay_fusion.cc
@@ -50,6 +50,34 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(CNodePtr node) {
   builder.SetOutputsFormat(outputs_format);
   return builder.Build();
 }
+
+AnfNodePtr ReplaceOutputEdge(const AnfNodePtr &node, CNodePtr adam_weight_decay, AnfNodePtr u_input) {
+  // Replace the parameters of the last UpdateState to maintain
+  // the execution order of FusedAdamWeightDecay and the following operators.
+  // n represents the operator assign_v in {prim::kPrimDepend, next_param, assign_v}
+  const auto &n = node->cast<CNodePtr>()->input(2);
+  MS_EXCEPTION_IF_NULL(n);
+  const auto &fg = n->func_graph();
+  MS_EXCEPTION_IF_NULL(fg);
+  auto mgr = fg->manager();
+  MS_EXCEPTION_IF_NULL(mgr);
+  auto &node_users = mgr->node_users();
+  auto iter = node_users.find(n);
+  if (iter == node_users.end()) {
+    MS_LOG(EXCEPTION) << "Can not find node : " << n->DebugString();
+  }
+
+  auto &users = iter->second;
+  for (auto &user : users) {
+    if (IsPrimitiveCNode(user.first, prim::kPrimUpdateState)) {
+      (user.first)->cast<CNodePtr>()->set_input(1, u_input);
+      (user.first)->cast<CNodePtr>()->set_input(2, adam_weight_decay);
+      break;
+    }
+  }
+
+  return adam_weight_decay;
+}
 }  // namespace
 
 const BaseRef AdamWeightDecayFusion::DefinePattern() const {
@@ -122,18 +150,10 @@ const AnfNodePtr AdamWeightDecayFusion::Process(const FuncGraphPtr &graph, const
   // Fused into a FusedAdamWeightDecay operator.
   auto prim = std::make_shared<Primitive>(kFusedAdamWeightDecayName);
   MS_EXCEPTION_IF_NULL(prim);
-  std::vector<AnfNodePtr> inputs = {NewValueNode(prim),
-                                    beta1_input,
-                                    one_sub_beta1_input,
-                                    beta2_input,
-                                    one_sub_beta2_input,
-                                    eps_input,
-                                    lr_input,
-                                    param,
-                                    m_input,
-                                    v_input,
-                                    gradient_input,
-                                    weight_decay_input};
+  auto prim_value = NewValueNode(prim);
+  std::vector<AnfNodePtr> inputs = {
+    prim_value, beta1_input, one_sub_beta1_input, beta2_input,       one_sub_beta2_input, eps_input, lr_input, param,
+    m_input,    v_input,     gradient_input,      weight_decay_input};
   auto adam_weight_decay = graph->NewCNode(inputs);
   MS_EXCEPTION_IF_NULL(adam_weight_decay);
   auto types = {AnfAlgo::GetOutputInferDataType(node, 0)};
@@ -143,31 +163,7 @@ const AnfNodePtr AdamWeightDecayFusion::Process(const FuncGraphPtr &graph, const
 
   auto build_info = GenerateKernelBuildInfo(adam_weight_decay);
   AnfAlgo::SetSelectKernelBuildInfo(build_info, adam_weight_decay.get());
-
-  // Replace the parameters of the last UpdateState to maintain
-  // the execution order of FusedAdamWeightDecay and the following operators.
-  // n represents the operator assign_v in {prim::kPrimDepend, next_param, assign_v}
-  auto n = node->cast<CNodePtr>()->input(2);
-  auto fg = n->func_graph();
-  MS_EXCEPTION_IF_NULL(fg);
-  auto mgr = fg->manager();
-  MS_EXCEPTION_IF_NULL(mgr);
-  auto &node_users = mgr->node_users();
-  auto iter = node_users.find(n);
-  if (iter == node_users.end()) {
-    MS_LOG(EXCEPTION) << "Can not find node : " << n->DebugString();
-  }
-
-  auto &users = iter->second;
-  for (auto &user : users) {
-    if (IsPrimitiveCNode(user.first, prim::kPrimUpdateState)) {
-      (user.first)->cast<CNodePtr>()->set_input(1, u_input);
-      (user.first)->cast<CNodePtr>()->set_input(2, adam_weight_decay);
-      break;
-    }
-  }
-
-  return adam_weight_decay;
+  return ReplaceOutputEdge(node, adam_weight_decay, u_input);
 }
 }  // namespace opt
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/backend/optimizer/trt_pass/trt_converter_context.cc b/mindspore/ccsrc/backend/optimizer/trt_pass/trt_converter_context.cc
new file mode 100644
index 0000000000..730efb9cb6
--- /dev/null
+++ b/mindspore/ccsrc/backend/optimizer/trt_pass/trt_converter_context.cc
@@ -0,0 +1,339 @@
+/**
+ * Copyright 2021 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 "backend/optimizer/trt_pass/trt_converter_context.h"
+
+#include <unordered_map>
+#include <utility>
+#include <vector>
+#include <string>
+#include <memory>
+#include <sstream>
+#include <algorithm>
+#include "runtime/device/gpu/trt_loader.h"
+#include "backend/optimizer/trt_pass/trt_op_factory.h"
+#include "backend/kernel_compiler/gpu/trt/trt_utils.h"
+#include "utils/convert_utils.h"
+#include "utils/utils.h"
+#include "utils/singleton.h"
+
+namespace mindspore::opt {
+namespace {
+void GetRealOutputRecursively(const AnfNodePtr &node, size_t output_index,
+                              std::vector<session::KernelWithIndex> *inputs) {
+  MS_EXCEPTION_IF_NULL(node);
+  if (node->isa<ValueNode>() || node->isa<Parameter>()) {
+    return inputs->push_back(std::make_pair(node, 0));
+  }
+
+  // Skip control node
+  if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend) || AnfAlgo::CheckPrimitiveType(node, prim::kPrimLoad) ||
+      AnfAlgo::CheckPrimitiveType(node, prim::kPrimUpdateState)) {
+    return GetRealOutputRecursive(node->cast<CNodePtr>()->input(kRealInputIndexInDepend), 0, inputs);
+  }
+
+  // Bypass TupleGetItem
+  if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimTupleGetItem)) {
+    auto tuple_get_item = node->cast<CNodePtr>();
+    MS_EXCEPTION_IF_NULL(tuple_get_item);
+    auto input = AnfAlgo::GetTupleGetItemRealInput(tuple_get_item);
+    auto index = AnfAlgo::GetTupleGetItemOutIndex(tuple_get_item);
+
+    // Conceal MakeTuple + TupleGetItem pair.
+    if (AnfAlgo::CheckPrimitiveType(input, prim::kPrimMakeTuple)) {
+      auto make_tuple = input->cast<CNodePtr>();
+      MS_EXCEPTION_IF_NULL(make_tuple);
+      auto real_input = AnfAlgo::GetInputNode(make_tuple, index);
+      return GetRealOutputRecursive(real_input, 0, inputs);
+    }
+
+    // Skip TupleGetItem.
+    return GetRealOutputRecursive(input, index, inputs);
+  }
+
+  // Flatten MakeTuple inputs.
+  if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimMakeTuple)) {
+    auto make_tuple = node->cast<CNodePtr>();
+    MS_EXCEPTION_IF_NULL(make_tuple);
+    size_t input_num = AnfAlgo::GetInputTensorNum(make_tuple);
+    for (size_t input_index = 0; input_index < input_num; ++input_index) {
+      auto input_node = AnfAlgo::GetInputNode(make_tuple, input_index);
+      GetRealOutputRecursive(input_node, 0, inputs);
+    }
+    return;
+  }
+
+  return inputs->push_back(std::make_pair(node, output_index));
+}
+
+/* Get node real inputs bypass control nodes.
+ *   Examples:
+ *     Case 1:
+ *       c = Conv2D(a, b)
+ *       d = ReLU(c)
+ *     result: d--> (c)
+ *
+ *     Case 2:
+ *       c = Conv2D(a, b)
+ *       d = Depend(c, v)
+ *       e = ReLU(d)
+ *     result: d -> (c)
+ *
+ *     Case 3:
+ *       (f, g, h, i, j) = BatchNorm(a, b, c, d, e)
+ *       k = TupleGetItem((f, g, h, i, j), 0)
+ *       l = ReLU(k)
+ *     result: l -> (f)
+ *
+ *     Case 4:
+ *       c = Conv2D(a, b)
+ *       e = MakeTuple(c, d)
+ *       f = TupleGetItem(e, 0)
+ *       g = ReLU(k)
+ *     result: g -> (c)
+ *
+ *     Case 5:
+ *       b = MakeTuple(a1, a2, a3)
+ *       c = MakeTuple(b, a4)
+ *       d = return(c)
+ *     result d -> (a1, a2, a3, a4)
+ */
+void GetRealInputs(const AnfNodePtr &node, std::vector<session::KernelWithIndex> *inputs) {
+  size_t input_num = AnfAlgo::GetInputTensorNum(node);
+  for (size_t input_index = 0; input_index < input_num; ++input_index) {
+    auto input_node = AnfAlgo::GetInputNode(node->cast<CNodePtr>(), input_index);
+    GetRealOutputRecursively(input_node, 0, inputs);
+  }
+}
+}  // namespace
+
+bool TrtConverterContext::Init() {
+  auto trt_loader = Singleton<device::gpu::TrtLoader>::Instance();
+  builder_ = trt_loader.CreateInferBuilder(&Singleton<TrtLogger>::Instance());
+  MS_EXCEPTION_IF_NULL(builder_);
+
+  auto batch_type = 1U << static_cast<uint32_t>(nvinfer1::NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
+  network_ = TrtPtr(builder_->createNetworkV2(batch_type));
+  MS_EXCEPTION_IF_NULL(network_);
+
+  config_ = TrtPtr(builder_->createBuilderConfig());
+  MS_EXCEPTION_IF_NULL(config_);
+  return true;
+}
+
+bool TrtConverterContext::Parser() {
+  InitInputTable();
+  InitValueNodeTable();
+
+  std::vector<AnfNodePtr> node_list = TopoSort(func_graph_->get_return());
+  const auto &converter_factory = TrtOpFactory::GetInstance();
+  for (auto node : node_list) {
+    if (!node->isa<CNode>()) {
+      continue;
+    }
+
+    // Mark graph outputs
+    std::string op_name = AnfAlgo::GetCNodePrimitive(node)->name();
+    if (op_name == kReturnOpName) {
+      std::vector<LayerInput> inputs;
+      (void)LoadLayerInput(node, &inputs);
+
+      for (size_t i = 0; i < inputs.size(); ++i) {
+        const auto &input = inputs[i].tensor();
+        std::string name = "return_output_" + std::to_string(i);
+        input->setName(name.c_str());
+        network_->markOutput(*input);
+      }
+      return true;
+    }
+
+    // Transform AnfNode To Trt layer.
+    // Bypass control node including Depend, Load, UpdateState, TupleGetItem, MakeTuple.
+    if (!AnfAlgo::IsRealKernel(node)) {
+      continue;
+    }
+
+    ConvertFunc convert_func = converter_factory.GetConvertFunc(op_name);
+    auto result = convert_func(node, this->shared_from_this());
+    if (!result.first) {
+      MS_LOG(ERROR) << op_name << " converter failed.";
+      return false;
+    }
+    auto ret = StoreLayerOutput(node, result.second);
+    if (!ret) {
+      MS_LOG(ERROR) << op_name << " converter failed.";
+      return false;
+    }
+  }
+
+  MS_LOG(ERROR) << "Graph ended without return node.";
+  return false;
+}
+
+bool TrtConverterContext::Serialize(std::string *model) {
+  MS_EXCEPTION_IF_NULL(model);
+  builder_->setMaxBatchSize(batch_size_);
+  config_->setMaxWorkspaceSize(workspace_size_);
+  engine_ = TrtPtr(builder_->buildEngineWithConfig(*network_, *config_));
+  MS_EXCEPTION_IF_NULL(engine_);
+
+  std::shared_ptr<nvinfer1::IHostMemory> model_data = TrtPtr(engine_->serialize());
+  *model = string(static_cast<const char *>(model_data->data()), model_data->size());
+  return true;
+}
+
+bool TrtConverterContext::InitInputTable() {
+  const std::vector<AnfNodePtr> graph_inputs = func_graph_->parameters();
+  for (auto input_node : graph_inputs) {
+    if (!input_node->isa<Parameter>()) {
+      continue;
+    }
+
+    auto input = input_node->cast<ParameterPtr>();
+    if (AnfAlgo::IsParameterWeight(input)) {
+      const auto &param_value = input->default_param();
+      MS_EXCEPTION_IF_NULL(param_value);
+      auto tensor = std::dynamic_pointer_cast<tensor::Tensor>(param_value);
+      MS_EXCEPTION_IF_NULL(tensor);
+
+      nvinfer1::Weights weight;
+      weight.values = tensor->data_c();
+      weight.type = TrtUtils::MsDtypeToTrtDtype(tensor->data_type());
+      weight.count = tensor->DataSize();
+      output_map_[input_node][0] = LayerInput(weight);
+    } else {
+      nvinfer1::DataType trt_dtype = TrtUtils::MsDtypeToTrtDtype(AnfAlgo::GetOutputInferDataType(input_node, 0));
+      nvinfer1::Dims trt_dims = TrtUtils::MsDimsToTrtDims(AnfAlgo::GetOutputInferShape(input_node, 0), false);
+      nvinfer1::ITensor *tensor = network_->addInput(input->name().c_str(), trt_dtype, trt_dims);
+      output_map_[input_node][0] = LayerInput(tensor);
+    }
+  }
+  return true;
+}
+
+bool TrtConverterContext::InitValueNodeTable() {
+  auto kernel_graph = std::dynamic_pointer_cast<session::KernelGraph>(func_graph_);
+  MS_EXCEPTION_IF_NULL(kernel_graph);
+
+  for (auto &value_node : kernel_graph->graph_value_nodes()) {
+    MS_EXCEPTION_IF_NULL(value_node);
+    auto &node_value = value_node->value();
+    MS_EXCEPTION_IF_NULL(node_value);
+
+    if (node_value->isa<tensor::Tensor>() || node_value->isa<ValueTuple>()) {
+      std::vector<tensor::TensorPtr> tensors;
+      TensorValueToTensor(node_value, &tensors);
+      for (size_t i = 0; i < tensors.size(); i++) {
+        const auto &tensor = tensors[i];
+        nvinfer1::Weights weight;
+        weight.values = tensor->data_c();
+        weight.type = TrtUtils::MsDtypeToTrtDtype(tensor->data_type());
+        weight.count = tensor->DataSize();
+        output_map_[value_node][i] = LayerInput(weight);
+      }
+    }
+  }
+  return true;
+}
+
+bool TrtConverterContext::StoreLayerOutput(const AnfNodePtr &node, const std::vector<LayerInput> &nv_tensors) {
+  if (nv_tensors.size() != AnfAlgo::GetOutputTensorNum(node)) {
+    MS_LOG(INFO) << node->DebugString() << " output num not match. expect: " << AnfAlgo::GetOutputTensorNum(node)
+                 << ", while got: " << nv_tensors.size();
+  }
+
+  for (size_t tensor_index = 0; tensor_index < nv_tensors.size(); ++tensor_index) {
+    if (nv_tensors[tensor_index].tensor() != nullptr) {
+      output_map_[node][tensor_index] = nv_tensors[tensor_index];
+
+      std::ostringstream oss;
+      nvinfer1::Dims dim = nv_tensors[tensor_index].tensor()->getDimensions();
+      oss << node->fullname_with_scope() << ", output: " << tensor_index << ": [ ";
+      for (int32_t dim_index = 0; dim_index < dim.nbDims; dim_index++) {
+        oss << dim.d[dim_index] << " ";
+      }
+      oss << "]";
+      MS_LOG(INFO) << oss.str();
+    }
+  }
+  return true;
+}
+
+bool TrtConverterContext::LoadLayerInput(const AnfNodePtr &node, std::vector<LayerInput> *inputs) {
+  std::vector<session::KernelWithIndex> real_inputs;
+  GetRealInputs(node, &real_inputs);
+  for (auto item : real_inputs) {
+    auto node_iter = output_map_.find(item.first);
+    if (node_iter == output_map_.end()) {
+      MS_LOG(ERROR) << "node: " << node->DebugString() << " not found.";
+      return false;
+    }
+
+    auto out_iter = node_iter->second.find(item.second);
+    if (out_iter == node_iter->second.end()) {
+      MS_LOG(ERROR) << "node: " << node->DebugString() << "output index: " << item.second << " not found.";
+      return false;
+    }
+
+    inputs->push_back(out_iter->second);
+  }
+  return true;
+}
+
+std::vector<AnfNodePtr> TrtConverterContext::GetGraphInputs() {
+  // Get Anf-graph inputs without weights. All weights were binded to Trt-graph.
+  std::unordered_map<std::string, AnfNodePtr> graph_inputs;
+  for (const auto &input_node : func_graph_->parameters()) {
+    if (!input_node->isa<Parameter>()) {
+      continue;
+    }
+
+    auto input = input_node->cast<ParameterPtr>();
+    if (!AnfAlgo::IsParameterWeight(input)) {
+      graph_inputs.insert(std::make_pair(input->name(), input_node));
+    }
+  }
+
+  // Keep the graph inputs in order of the binding name.
+  std::vector<AnfNodePtr> trt_inputs;
+  for (int32_t i = 0; i < engine_->getNbBindings(); ++i) {
+    if (!engine_->bindingIsInput(i)) {
+      continue;
+    }
+    auto iter = graph_inputs.find(engine_->getBindingName(i));
+    if (iter == graph_inputs.end()) {
+      MS_LOG(EXCEPTION) << "Get graph inputs failed. input name" << engine_->getBindingName(i);
+    }
+    trt_inputs.push_back(iter->second);
+  }
+  return trt_inputs;
+}
+
+std::vector<session::KernelWithIndex> TrtConverterContext::GetGraphOutputs() {
+  std::vector<session::KernelWithIndex> graph_outputs;
+  GetRealInputs(func_graph_->get_return(), &graph_outputs);
+  return graph_outputs;
+}
+
+std::shared_ptr<tensor::Tensor> TrtConverterContext::CreateTempWeight(const TypeId &type,
+                                                                      const std::vector<size_t> &shape) {
+  ShapeVector shape_int;
+  std::transform(shape.begin(), shape.end(), std::back_inserter(shape_int), SizeToLong);
+  auto tensor = std::make_shared<tensor::Tensor>(type, shape_int);
+  temp_weights_.push_back(tensor);
+  return tensor;
+}
+}  // namespace mindspore::opt
diff --git a/mindspore/ccsrc/backend/optimizer/trt_pass/trt_converter_context.h b/mindspore/ccsrc/backend/optimizer/trt_pass/trt_converter_context.h
new file mode 100644
index 0000000000..c9e5157114
--- /dev/null
+++ b/mindspore/ccsrc/backend/optimizer/trt_pass/trt_converter_context.h
@@ -0,0 +1,89 @@
+/**
+ * Copyright 2021 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 MINDSPORE_CCSRC_BACKEND_OPTITIMIZER_TRT_CONVERTER_CONTEXT_H_
+#define MINDSPORE_CCSRC_BACKEND_OPTITIMIZER_TRT_CONVERTER_CONTEXT_H_
+
+#include <unordered_map>
+#include <vector>
+#include <string>
+#include <memory>
+#include <NvInfer.h>
+#include "base/base.h"
+#include "ir/anf.h"
+#include "backend/session/anf_runtime_algorithm.h"
+#include "backend/optimizer/trt_pass/layer_input.h"
+
+namespace mindspore {
+namespace opt {
+class TrtConverterContext : public std::enable_shared_from_this<TrtConverterContext> {
+ public:
+  explicit TrtConverterContext(FuncGraphPtr fg)
+      : func_graph_(fg),
+        batch_size_(1),
+        workspace_size_(4UL << 30),
+        builder_(nullptr),
+        network_(nullptr),
+        config_(nullptr),
+        engine_(nullptr) {}
+  ~TrtConverterContext() = default;
+
+  bool Init();
+
+  // Parser KernelGraph to trt graph
+  bool Parser();
+
+  // Serialize trt models.
+  bool Serialize(std::string *model);
+
+  // Get trt graph inputs without weights. The inputs keep same order as binding name.
+  std::vector<AnfNodePtr> GetGraphInputs();
+
+  // Get trt graph outputs. All outputs are flatten to vector with concret shape.
+  std::vector<session::KernelWithIndex> GetGraphOutputs();
+
+  // Store trt layer outputs to the cache.
+  bool StoreLayerOutput(const AnfNodePtr &node, const std::vector<LayerInput> &inputs);
+
+  // Get trt layer inputs from the cache.
+  bool LoadLayerInput(const AnfNodePtr &node, std::vector<LayerInput> *inputs);
+
+  // Create and keep temporary weight, as constant folding demanding new weight excluded in graph,
+  // which should release until building finish.
+  std::shared_ptr<tensor::Tensor> CreateTempWeight(const TypeId &type, const std::vector<size_t> &shape);
+
+  std::shared_ptr<nvinfer1::INetworkDefinition> network() const { return network_; }
+
+ private:
+  bool InitInputTable();
+  bool InitValueNodeTable();
+
+  FuncGraphPtr func_graph_;
+  uint32_t batch_size_;
+  size_t workspace_size_;
+  std::shared_ptr<nvinfer1::IBuilder> builder_;
+  std::shared_ptr<nvinfer1::INetworkDefinition> network_;
+  std::shared_ptr<nvinfer1::IBuilderConfig> config_;
+  std::shared_ptr<nvinfer1::ICudaEngine> engine_;
+
+  // Cache (AnfNode + output_index : ILayer output).
+  std::unordered_map<AnfNodePtr, std::unordered_map<size_t, LayerInput>> output_map_;
+  std::vector<std::shared_ptr<tensor::Tensor>> temp_weights_;
+};
+}  // namespace opt
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_BACKEND_OPTITIMIZER_TRT_CONVERTER_HELPER_H_
diff --git a/mindspore/ccsrc/backend/optimizer/trt_pass/trt_op_factory.h b/mindspore/ccsrc/backend/optimizer/trt_pass/trt_op_factory.h
index c42f11bf8e..87febe09c8 100644
--- a/mindspore/ccsrc/backend/optimizer/trt_pass/trt_op_factory.h
+++ b/mindspore/ccsrc/backend/optimizer/trt_pass/trt_op_factory.h
@@ -29,9 +29,9 @@
 namespace mindspore {
 namespace opt {
 class LayerInput;
-class TrtConverterHelper;
+class TrtConverterContext;
 using ConvertResult = std::pair<bool, std::vector<LayerInput>>;
-using ConvertFunc = std::function<ConvertResult(AnfNodePtr, std::shared_ptr<TrtConverterHelper>)>;
+using ConvertFunc = std::function<ConvertResult(AnfNodePtr, std::shared_ptr<TrtConverterContext>)>;
 
 class TrtOpFactory {
  public:
@@ -69,10 +69,10 @@ class TrtOpRegister {
 };
 
 // Register operator converter from AnfNode to trt layer: `OPNAME` should keep the same as primitive definition.
-#define MS_TRT_CONVERTER_FUNC_REG(OPNAME)                                                                \
-  ConvertResult Gpu##OPNAME##TrtConverter(AnfNodePtr node, std::shared_ptr<TrtConverterHelper> context); \
-  static const TrtOpRegister(Gpu##OPNAME##ConverterRegister)(#OPNAME, Gpu##OPNAME##TrtConverter);        \
-  ConvertResult Gpu##OPNAME##TrtConverter(AnfNodePtr node, std::shared_ptr<TrtConverterHelper> context)
+#define MS_TRT_CONVERTER_FUNC_REG(OPNAME)                                                                 \
+  ConvertResult Gpu##OPNAME##TrtConverter(AnfNodePtr node, std::shared_ptr<TrtConverterContext> context); \
+  static const TrtOpRegister(Gpu##OPNAME##ConverterRegister)(#OPNAME, Gpu##OPNAME##TrtConverter);         \
+  ConvertResult Gpu##OPNAME##TrtConverter(AnfNodePtr node, std::shared_ptr<TrtConverterContext> context)
 }  // namespace opt
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_BACKEND_OPTITIMIZER_TRT_PASS_OP_FACTORY_H_