diff --git a/mindspore/ccsrc/backend/optimizer/ascend/ascend_backend_optimization.cc b/mindspore/ccsrc/backend/optimizer/ascend/ascend_backend_optimization.cc
index 3ea8a300e37589b9800ad1d0ff140900258360bf..dcca95fbc02a2f6d75acccdd3e6b4b4a211931af 100644
--- a/mindspore/ccsrc/backend/optimizer/ascend/ascend_backend_optimization.cc
+++ b/mindspore/ccsrc/backend/optimizer/ascend/ascend_backend_optimization.cc
@@ -125,6 +125,10 @@ void AddAscendIRFusionRulesPass(PassManager *ir_fusion_pm) {
   ir_fusion_pm->AddPass(std::make_shared<LambNextMVRuleCond4>());
   ir_fusion_pm->AddPass(std::make_shared<LambNextRightRule>());
   ir_fusion_pm->AddPass(std::make_shared<LambUpdateWithLrV2>());
+  ir_fusion_pm->AddPass(std::make_shared<AdamApplyOneAssignCond1Fusion>());
+  ir_fusion_pm->AddPass(std::make_shared<AdamApplyOneAssignCond2Fusion>());
+  ir_fusion_pm->AddPass(std::make_shared<AdamApplyOneAssignCond3Fusion>());
+  ir_fusion_pm->AddPass(std::make_shared<AdamApplyOneAssignCond4Fusion>());
   ir_fusion_pm->AddPass(std::make_shared<AdamApplyOneCond1Fusion>());
   ir_fusion_pm->AddPass(std::make_shared<AdamApplyOneCond2Fusion>());
   ir_fusion_pm->AddPass(std::make_shared<AdamApplyOneCond3Fusion>());
diff --git a/mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/adam_apply_one_fusion.cc b/mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/adam_apply_one_fusion.cc
index 189ac94546a4cefe39da836a3e3dfdbd8a4f36a5..9164484b30c6557d665feaebc6590181b2084187 100644
--- a/mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/adam_apply_one_fusion.cc
+++ b/mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/adam_apply_one_fusion.cc
@@ -15,30 +15,9 @@
  */
 #include "backend/optimizer/ascend/ir_fusion/adam_apply_one_fusion.h"
 #include "backend/optimizer/common/helper.h"
+#include "backend/session/anf_runtime_algorithm.h"
 namespace mindspore {
 namespace opt {
-AnfNodePtr AdamApplyOneFusion::CreateAdamApplyOneNode(const FuncGraphPtr &func_graph, const EquivPtr &equiv) const {
-  MS_EXCEPTION_IF_NULL(func_graph);
-  MS_EXCEPTION_IF_NULL(equiv);
-  auto prim = std::make_shared<Primitive>(kAdamApplyOneOpName);
-  std::vector<AnfNodePtr> new_node_inputs = {NewValueNode(prim)};
-  for (const auto &input_var : input_vars_) {
-    auto input_node = utils::cast<AnfNodePtr>((*equiv)[input_var]);
-    MS_EXCEPTION_IF_NULL(input_node);
-    new_node_inputs.push_back(input_node);
-  }
-  for (const auto &mul_x_input_var : mul_x_input_vars_) {
-    auto mul_x_input_node = utils::cast<AnfNodePtr>((*equiv)[mul_x_input_var]);
-    MS_EXCEPTION_IF_NULL(mul_x_input_node);
-    new_node_inputs.push_back(mul_x_input_node);
-  }
-  auto add2_y_node = utils::cast<AnfNodePtr>((*equiv)[add2_y_]);
-  MS_EXCEPTION_IF_NULL(add2_y_node);
-  new_node_inputs.push_back(add2_y_node);
-  auto new_node = func_graph->NewCNode(new_node_inputs);
-  return new_node;
-}
-
 const BaseRef AdamApplyOneFusion::DefinePattern() const {
   const auto prim_sqrt = std::make_shared<Primitive>(kSqrtOpName);
   const auto prim_real_div = std::make_shared<Primitive>(kRealDivOpName);
@@ -104,16 +83,152 @@ const BaseRef AdamApplyOneCond4Fusion::DefinePattern() const {
   return VectorRef({prim::kPrimSub, input_vars_[3], VectorRef({prim::kPrimMul, true_div0, input_vars_[4]})});
 }
 
+const BaseRef AdamApplyOneAssignFusion::DefinePattern() const {
+  const auto prim_sqrt = std::make_shared<Primitive>(kSqrtOpName);
+  const auto prim_real_div = std::make_shared<Primitive>(kRealDivOpName);
+  VectorRef mul2 = VectorRef({prim::kPrimMul, mul_x_input_vars_[2], input_vars_[1]});
+  VectorRef mul3 = VectorRef({prim::kPrimMul, mul_x_input_vars_[3], VectorRef({prim::kPrimSquare, input_vars_[0]})});
+  VectorRef add1 = VectorRef({add1_var_, mul2, mul3});
+  VectorRef sqrt0 = VectorRef({prim_sqrt, add1});
+  VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
+  VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
+  VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, sqrt0, add2_y_})});
+  VectorRef sub0 = VectorRef({sub0_var_, input_vars_[3], VectorRef({prim::kPrimMul, input_vars_[4], true_div0})});
+  VectorRef assign0 = VectorRef({prim::kPrimAssign, input_vars_[3], sub0});
+  VectorRef depend0 = VectorRef({prim::kPrimDepend, sub0, assign0});
+  VectorRef assign1 = VectorRef({prim::kPrimAssign, input_vars_[2], add0});
+  VectorRef depend1 = VectorRef({prim::kPrimDepend, depend0, assign1});
+  VectorRef assign2 = VectorRef({prim::kPrimAssign, input_vars_[1], add1});
+  return VectorRef({prim::kPrimDepend, depend1, assign2});
+}
+
+const BaseRef AdamApplyOneAssignCond1Fusion::DefinePattern() const {
+  const auto prim_sqrt = std::make_shared<Primitive>(kSqrtOpName);
+  const auto prim_real_div = std::make_shared<Primitive>(kRealDivOpName);
+  VectorRef mul2 = VectorRef({prim::kPrimMul, mul_x_input_vars_[2], input_vars_[1]});
+  VectorRef mul3 = VectorRef({prim::kPrimMul, mul_x_input_vars_[3], VectorRef({prim::kPrimSquare, input_vars_[0]})});
+  VectorRef add1 = VectorRef({add1_var_, mul2, mul3});
+  VectorRef sqrt0 = VectorRef({prim_sqrt, add1});
+  VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
+  VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
+  VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, add2_y_, sqrt0})});
+  VectorRef sub0 = VectorRef({sub0_var_, input_vars_[3], VectorRef({prim::kPrimMul, input_vars_[4], true_div0})});
+  VectorRef assign0 = VectorRef({prim::kPrimAssign, input_vars_[3], sub0});
+  VectorRef depend0 = VectorRef({prim::kPrimDepend, sub0, assign0});
+  VectorRef assign1 = VectorRef({prim::kPrimAssign, input_vars_[2], add0});
+  VectorRef depend1 = VectorRef({prim::kPrimDepend, depend0, assign1});
+  VectorRef assign2 = VectorRef({prim::kPrimAssign, input_vars_[1], add1});
+  return VectorRef({prim::kPrimDepend, depend1, assign2});
+}
+
+const BaseRef AdamApplyOneAssignCond2Fusion::DefinePattern() const {
+  const auto prim_sqrt = std::make_shared<Primitive>(kSqrtOpName);
+  const auto prim_real_div = std::make_shared<Primitive>(kRealDivOpName);
+  VectorRef mul2 = VectorRef({prim::kPrimMul, mul_x_input_vars_[2], input_vars_[1]});
+  VectorRef mul3 = VectorRef({prim::kPrimMul, VectorRef({prim::kPrimSquare, input_vars_[0]}), mul_x_input_vars_[3]});
+  VectorRef add1 = VectorRef({add1_var_, mul2, mul3});
+  VectorRef sqrt0 = VectorRef({prim_sqrt, add1});
+  VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
+  VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
+  VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, sqrt0, add2_y_})});
+  VectorRef sub0 = VectorRef({sub0_var_, input_vars_[3], VectorRef({prim::kPrimMul, true_div0, input_vars_[4]})});
+  VectorRef assign0 = VectorRef({prim::kPrimAssign, input_vars_[3], sub0});
+  VectorRef depend0 = VectorRef({prim::kPrimDepend, sub0, assign0});
+  VectorRef assign1 = VectorRef({prim::kPrimAssign, input_vars_[2], add0});
+  VectorRef depend1 = VectorRef({prim::kPrimDepend, depend0, assign1});
+  VectorRef assign2 = VectorRef({prim::kPrimAssign, input_vars_[1], add1});
+  return VectorRef({prim::kPrimDepend, depend1, assign2});
+}
+
+const BaseRef AdamApplyOneAssignCond3Fusion::DefinePattern() const {
+  const auto prim_sqrt = std::make_shared<Primitive>(kSqrtOpName);
+  const auto prim_real_div = std::make_shared<Primitive>(kRealDivOpName);
+  VectorRef mul2 = VectorRef({prim::kPrimMul, mul_x_input_vars_[2], input_vars_[1]});
+  VectorRef mul3 = VectorRef({prim::kPrimMul, mul_x_input_vars_[3], VectorRef({prim::kPrimSquare, input_vars_[0]})});
+  VectorRef add1 = VectorRef({add1_var_, mul2, mul3});
+  VectorRef sqrt0 = VectorRef({prim_sqrt, add1});
+  VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
+  VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
+  VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, sqrt0, add2_y_})});
+  VectorRef sub0 = VectorRef({sub0_var_, input_vars_[3], VectorRef({prim::kPrimMul, true_div0, input_vars_[4]})});
+  VectorRef assign0 = VectorRef({prim::kPrimAssign, input_vars_[3], sub0});
+  VectorRef depend0 = VectorRef({prim::kPrimDepend, sub0, assign0});
+  VectorRef assign1 = VectorRef({prim::kPrimAssign, input_vars_[2], add0});
+  VectorRef depend1 = VectorRef({prim::kPrimDepend, depend0, assign1});
+  VectorRef assign2 = VectorRef({prim::kPrimAssign, input_vars_[1], add1});
+  return VectorRef({prim::kPrimDepend, depend1, assign2});
+}
+
+const BaseRef AdamApplyOneAssignCond4Fusion::DefinePattern() const {
+  const auto prim_sqrt = std::make_shared<Primitive>(kSqrtOpName);
+  const auto prim_real_div = std::make_shared<Primitive>(kRealDivOpName);
+  VectorRef mul2 = VectorRef({prim::kPrimMul, mul_x_input_vars_[2], input_vars_[1]});
+  VectorRef mul3 = VectorRef({prim::kPrimMul, mul_x_input_vars_[3], VectorRef({prim::kPrimSquare, input_vars_[0]})});
+  VectorRef add1 = VectorRef({add1_var_, mul2, mul3});
+  VectorRef sqrt0 = VectorRef({prim_sqrt, add1});
+  VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
+  VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
+  VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, add2_y_, sqrt0})});
+  VectorRef sub0 = VectorRef({sub0_var_, input_vars_[3], VectorRef({prim::kPrimMul, true_div0, input_vars_[4]})});
+  VectorRef assign0 = VectorRef({prim::kPrimAssign, input_vars_[3], sub0});
+  VectorRef depend0 = VectorRef({prim::kPrimDepend, sub0, assign0});
+  VectorRef assign1 = VectorRef({prim::kPrimAssign, input_vars_[2], add0});
+  VectorRef depend1 = VectorRef({prim::kPrimDepend, depend0, assign1});
+  VectorRef assign2 = VectorRef({prim::kPrimAssign, input_vars_[1], add1});
+  return VectorRef({prim::kPrimDepend, depend1, assign2});
+}
+
+AnfNodePtr AdamApplyOneFusion::CreateAdamApplyOneNode(const FuncGraphPtr &func_graph, const EquivPtr &equiv,
+                                                      const AnfNodePtr &final_node) const {
+  MS_EXCEPTION_IF_NULL(func_graph);
+  MS_EXCEPTION_IF_NULL(equiv);
+  PrimitivePtr prim = nullptr;
+  if (AnfAlgo::CheckPrimitiveType(final_node, prim::kPrimDepend)) {
+    prim = std::make_shared<Primitive>(kAdamApplyOneAssignOpName);
+  } else {
+    prim = std::make_shared<Primitive>(kAdamApplyOneOpName);
+  }
+  std::vector<AnfNodePtr> new_node_inputs = {NewValueNode(prim)};
+  for (const auto &input_var : input_vars_) {
+    auto input_node = utils::cast<AnfNodePtr>((*equiv)[input_var]);
+    MS_EXCEPTION_IF_NULL(input_node);
+    new_node_inputs.push_back(input_node);
+  }
+  for (const auto &mul_x_input_var : mul_x_input_vars_) {
+    auto mul_x_input_node = utils::cast<AnfNodePtr>((*equiv)[mul_x_input_var]);
+    MS_EXCEPTION_IF_NULL(mul_x_input_node);
+    new_node_inputs.push_back(mul_x_input_node);
+  }
+  auto add2_y_node = utils::cast<AnfNodePtr>((*equiv)[add2_y_]);
+  MS_EXCEPTION_IF_NULL(add2_y_node);
+  new_node_inputs.push_back(add2_y_node);
+  auto new_node = func_graph->NewCNode(new_node_inputs);
+  return new_node;
+}
+
 const AnfNodePtr AdamApplyOneFusion::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
                                              const EquivPtr &equiv) const {
   MS_EXCEPTION_IF_NULL(func_graph);
-  MS_EXCEPTION_IF_NULL(node);
-  if (!CheckSupportDataType(node, kFloatDataTypeSet)) {
+  auto sub0 = node;
+  if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend)) {
+    auto iter_sub0 = (*equiv).find(sub0_var_);
+    if (iter_sub0 == (*equiv).end()) {
+      MS_LOG(EXCEPTION) << "The equiv map is expected to contains the sub0 var after matched.";
+    }
+    sub0 = utils::cast<AnfNodePtr>(iter_sub0->second);
+  }
+  MS_EXCEPTION_IF_NULL(sub0);
+  if (!CheckSupportDataType(sub0, kFloatDataTypeSet)) {
     return nullptr;
   }
-  auto new_node = CreateAdamApplyOneNode(func_graph, equiv);
+  auto new_node = CreateAdamApplyOneNode(func_graph, equiv, node);
   MS_EXCEPTION_IF_NULL(new_node);
-  new_node->set_scope(node->scope());
+  new_node->set_scope(sub0->scope());
   // Set abstract of new node
   AbstractBasePtrList new_node_abstract_list;
   auto iter_add0 = (*equiv).find(add0_var_);
@@ -130,7 +245,7 @@ const AnfNodePtr AdamApplyOneFusion::Process(const FuncGraphPtr &func_graph, con
   MS_EXCEPTION_IF_NULL(add1);
   new_node_abstract_list.push_back(add1->abstract());
   new_node_abstract_list.push_back(add0->abstract());
-  new_node_abstract_list.push_back(node->abstract());
+  new_node_abstract_list.push_back(sub0->abstract());
   auto abstract_tuple = std::make_shared<abstract::AbstractTuple>(new_node_abstract_list);
   new_node->set_abstract(abstract_tuple);
   // Create tuple_getitem node for outputs
diff --git a/mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/adam_apply_one_fusion.h b/mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/adam_apply_one_fusion.h
index 2fe2d32fa09f646b366b05150721e7da1cdbd764..4c5649f978853053c9a9cb6042bd13a5df893d67 100644
--- a/mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/adam_apply_one_fusion.h
+++ b/mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/adam_apply_one_fusion.h
@@ -40,6 +40,7 @@ class AdamApplyOneFusion : public PatternProcessPass {
     add2_y_ = std::make_shared<Var>();
     add0_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimTensorAdd->name()));
     add1_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimTensorAdd->name()));
+    sub0_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimSub->name()));
   }
 
   ~AdamApplyOneFusion() override = default;
@@ -47,12 +48,14 @@ class AdamApplyOneFusion : public PatternProcessPass {
   const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
 
  protected:
-  AnfNodePtr CreateAdamApplyOneNode(const FuncGraphPtr &func_graph, const EquivPtr &equiv) const;
+  AnfNodePtr CreateAdamApplyOneNode(const FuncGraphPtr &func_graph, const EquivPtr &equiv,
+                                    const AnfNodePtr &final_node) const;
   std::vector<VarPtr> input_vars_;
   std::vector<VarPtr> mul_x_input_vars_;
   VarPtr add2_y_;
   VarPtr add0_var_;
   VarPtr add1_var_;
+  VarPtr sub0_var_;
 };
 
 class AdamApplyOneCond1Fusion : public AdamApplyOneFusion {
@@ -90,6 +93,51 @@ class AdamApplyOneCond4Fusion : public AdamApplyOneFusion {
   ~AdamApplyOneCond4Fusion() override = default;
   const BaseRef DefinePattern() const override;
 };
+
+class AdamApplyOneAssignFusion : public AdamApplyOneFusion {
+ public:
+  explicit AdamApplyOneAssignFusion(bool multigraph = true)
+      : AdamApplyOneFusion("adam_apply_one_assign_fusion", multigraph) {}
+
+  ~AdamApplyOneAssignFusion() override = default;
+  const BaseRef DefinePattern() const override;
+};
+
+class AdamApplyOneAssignCond1Fusion : public AdamApplyOneFusion {
+ public:
+  explicit AdamApplyOneAssignCond1Fusion(bool multigraph = true)
+      : AdamApplyOneFusion("adam_apply_one_assign_cond1_fusion", multigraph) {}
+
+  ~AdamApplyOneAssignCond1Fusion() override = default;
+  const BaseRef DefinePattern() const override;
+};
+
+class AdamApplyOneAssignCond2Fusion : public AdamApplyOneFusion {
+ public:
+  explicit AdamApplyOneAssignCond2Fusion(bool multigraph = true)
+      : AdamApplyOneFusion("adam_apply_one_assign_cond2_fusion", multigraph) {}
+
+  ~AdamApplyOneAssignCond2Fusion() override = default;
+  const BaseRef DefinePattern() const override;
+};
+
+class AdamApplyOneAssignCond3Fusion : public AdamApplyOneFusion {
+ public:
+  explicit AdamApplyOneAssignCond3Fusion(bool multigraph = true)
+      : AdamApplyOneFusion("adam_apply_one_assign_cond3_fusion", multigraph) {}
+
+  ~AdamApplyOneAssignCond3Fusion() override = default;
+  const BaseRef DefinePattern() const override;
+};
+
+class AdamApplyOneAssignCond4Fusion : public AdamApplyOneFusion {
+ public:
+  explicit AdamApplyOneAssignCond4Fusion(bool multigraph = true)
+      : AdamApplyOneFusion("adam_apply_one_assign_cond4_fusion", multigraph) {}
+
+  ~AdamApplyOneAssignCond4Fusion() override = default;
+  const BaseRef DefinePattern() const override;
+};
 }  // namespace opt
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_BACKEND_OPTIMIZER_ASCEND_IR_FUSION_ADAM_APPLY_ONE_FUSION_H_
diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h
index 6be32a3df59d133a35808bcba086d25e5fe2f358..c1f551258b6d2d81b5b02563be95849c34255c44 100644
--- a/mindspore/ccsrc/utils/utils.h
+++ b/mindspore/ccsrc/utils/utils.h
@@ -119,6 +119,7 @@ constexpr auto kAdamApplyOneWithDecayOpName = "AdamApplyOneWithDecay";
 constexpr auto kBatchNormGradOpName = "BatchNormGrad";
 constexpr auto kBNInferOpName = "BNInfer";
 constexpr auto kAdamApplyOneOpName = "AdamApplyOne";
+constexpr auto kAdamApplyOneAssignOpName = "AdamApplyOneAssign";
 constexpr auto kResizeNearestNeighborGradOpName = "ResizeNearestNeighborGrad";
 constexpr auto kFusedMulAddOpName = "FusedMulAdd";
 constexpr auto kFusedMulAddNOpName = "FusedMulAddN";
diff --git a/tests/ut/cpp/pre_activate/ascend/ir_fusion/adam_apply_one_fusion_test.cc b/tests/ut/cpp/pre_activate/ascend/ir_fusion/adam_apply_one_fusion_test.cc
index 2759864037970bbbdbd316530fb2e87bf9c39783..add4ef8e41ad8f049421f1c9107fec4ee0f7dfac 100644
--- a/tests/ut/cpp/pre_activate/ascend/ir_fusion/adam_apply_one_fusion_test.cc
+++ b/tests/ut/cpp/pre_activate/ascend/ir_fusion/adam_apply_one_fusion_test.cc
@@ -217,5 +217,105 @@ TEST_F(TestHWAdamApplyOneFusion, test_adam_apply_one_cond4_fusion) {
   FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_adam_apply_one_fusion", "after");
   EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
 }
+
+TEST_F(TestHWAdamApplyOneFusion, test_adam_apply_one_assign_fusion) {
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_adam_apply_one_assign_fusion", "before");
+  std::vector<int> shp{2, 32, 224, 224};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+  AbstractBasePtrList args_spec_list;
+  for (size_t i = 0; i < 10; ++i) {
+    args_spec_list.push_back(x_abstract);
+  }
+  auto fg = GetKernelGraph(g, args_spec_list);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  pm->AddPass(std::make_shared<opt::AdamApplyOneAssignFusion>());
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(fg);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_adam_apply_one_assign_fusion", "after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
+
+TEST_F(TestHWAdamApplyOneFusion, test_adam_apply_one_assign_cond1_fusion) {
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_adam_apply_one_assign_fusion", "before_cond1");
+  std::vector<int> shp{2, 32, 224, 224};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+  AbstractBasePtrList args_spec_list;
+  for (size_t i = 0; i < 10; ++i) {
+    args_spec_list.push_back(x_abstract);
+  }
+  auto fg = GetKernelGraph(g, args_spec_list);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  pm->AddPass(std::make_shared<opt::AdamApplyOneAssignCond1Fusion>());
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(fg);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_adam_apply_one_assign_fusion", "after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
+
+TEST_F(TestHWAdamApplyOneFusion, test_adam_apply_one_assign_cond2_fusion) {
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_adam_apply_one_assign_fusion", "before_cond2");
+  std::vector<int> shp{2, 32, 224, 224};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+  AbstractBasePtrList args_spec_list;
+  for (size_t i = 0; i < 10; ++i) {
+    args_spec_list.push_back(x_abstract);
+  }
+  auto fg = GetKernelGraph(g, args_spec_list);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  pm->AddPass(std::make_shared<opt::AdamApplyOneAssignCond2Fusion>());
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(fg);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_adam_apply_one_assign_fusion", "after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
+
+TEST_F(TestHWAdamApplyOneFusion, test_adam_apply_one_assign_cond3_fusion) {
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_adam_apply_one_assign_fusion", "before_cond3");
+  std::vector<int> shp{2, 32, 224, 224};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+  AbstractBasePtrList args_spec_list;
+  for (size_t i = 0; i < 10; ++i) {
+    args_spec_list.push_back(x_abstract);
+  }
+  auto fg = GetKernelGraph(g, args_spec_list);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  pm->AddPass(std::make_shared<opt::AdamApplyOneAssignCond3Fusion>());
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(fg);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_adam_apply_one_assign_fusion", "after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
+
+TEST_F(TestHWAdamApplyOneFusion, test_adam_apply_one_assign_cond4_fusion) {
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_adam_apply_one_assign_fusion", "before_cond4");
+  std::vector<int> shp{2, 32, 224, 224};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+  AbstractBasePtrList args_spec_list;
+  for (size_t i = 0; i < 10; ++i) {
+    args_spec_list.push_back(x_abstract);
+  }
+  auto fg = GetKernelGraph(g, args_spec_list);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  pm->AddPass(std::make_shared<opt::AdamApplyOneAssignCond4Fusion>());
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(fg);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_adam_apply_one_assign_fusion", "after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
 }  // namespace opt
 }  // namespace mindspore
diff --git a/tests/ut/cpp/python_input/gtest_input/pre_activate/adam_apply_one_fusion_test.py b/tests/ut/cpp/python_input/gtest_input/pre_activate/adam_apply_one_fusion_test.py
index 27cfe38b8f00f0051716ed5f3fe14ad7ae99fb9c..654b922c251060308248f74f2abdfa0c9a77d7ff 100644
--- a/tests/ut/cpp/python_input/gtest_input/pre_activate/adam_apply_one_fusion_test.py
+++ b/tests/ut/cpp/python_input/gtest_input/pre_activate/adam_apply_one_fusion_test.py
@@ -14,6 +14,7 @@
 # ============================================================================
 from mindspore.ops import Primitive
 from mindspore.ops import operations as P
+from mindspore.ops import functional as F
 
 Add = P.TensorAdd()
 Sub = P.Sub()
@@ -21,9 +22,11 @@ Mul = P.Mul()
 RealDiv = P.RealDiv()
 Sqrt = P.Sqrt()
 Square = P.Square()
+Assign = P.Assign()
 make_tuple = Primitive('make_tuple')
 tuple_getitem = Primitive('tuple_getitem')
 AdamApplyOne = Primitive('AdamApplyOne')
+AdamApplyOneAssign = Primitive('AdamApplyOneAssign')
 
 
 class FnDict:
@@ -139,3 +142,138 @@ def test_adam_apply_one_fusion(tag):
         return make_tuple(output)
 
     return fns[tag]
+
+
+def test_adam_apply_one_assign_fusion(tag):
+    fns = FnDict()
+
+    @fns
+    def before(input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x, mul3_x, add2_y):
+        square0 = Square(input0)
+        mul1 = Mul(mul1_x, input0)
+        mul0 = Mul(mul0_x, input2)
+        mul2 = Mul(mul2_x, input1)
+        mul3 = Mul(mul3_x, square0)
+        add0 = Add(mul0, mul1)
+        add1 = Add(mul2, mul3)
+        sqrt0 = Sqrt(add1)
+        add2 = Add(sqrt0, add2_y)
+        true_div0 = RealDiv(add0, add2)
+        mul4 = Mul(input4, true_div0)
+        sub0 = Sub(input3, mul4)
+        assign0 = Assign(input3, sub0)
+        depend0 = F.depend(sub0, assign0)
+        assign1 = Assign(input2, add0)
+        depend1 = F.depend(depend0, assign1)
+        assign2 = Assign(input1, add1)
+        depend2 = F.depend(depend1, assign2)
+        outputs = make_tuple(add1, add0, depend2)
+        output = tuple_getitem(outputs, 0)
+        return output
+
+    @fns
+    def before_cond1(input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x, mul3_x, add2_y):
+        square0 = Square(input0)
+        mul1 = Mul(mul1_x, input0)
+        mul0 = Mul(mul0_x, input2)
+        mul2 = Mul(mul2_x, input1)
+        mul3 = Mul(mul3_x, square0)
+        add0 = Add(mul0, mul1)
+        add1 = Add(mul2, mul3)
+        sqrt0 = Sqrt(add1)
+        add2 = Add(add2_y, sqrt0)
+        true_div0 = RealDiv(add0, add2)
+        mul4 = Mul(input4, true_div0)
+        sub0 = Sub(input3, mul4)
+        assign0 = Assign(input3, sub0)
+        depend0 = F.depend(sub0, assign0)
+        assign1 = Assign(input2, add0)
+        depend1 = F.depend(depend0, assign1)
+        assign2 = Assign(input1, add1)
+        depend2 = F.depend(depend1, assign2)
+        outputs = make_tuple(add1, add0, depend2)
+        output = tuple_getitem(outputs, 0)
+        return output
+
+    @fns
+    def before_cond2(input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x, mul3_x, add2_y):
+        square0 = Square(input0)
+        mul1 = Mul(mul1_x, input0)
+        mul0 = Mul(mul0_x, input2)
+        mul2 = Mul(mul2_x, input1)
+        mul3 = Mul(square0, mul3_x)
+        add0 = Add(mul0, mul1)
+        add1 = Add(mul2, mul3)
+        sqrt0 = Sqrt(add1)
+        add2 = Add(sqrt0, add2_y)
+        true_div0 = RealDiv(add0, add2)
+        mul4 = Mul(true_div0, input4)
+        sub0 = Sub(input3, mul4)
+        assign0 = Assign(input3, sub0)
+        depend0 = F.depend(sub0, assign0)
+        assign1 = Assign(input2, add0)
+        depend1 = F.depend(depend0, assign1)
+        assign2 = Assign(input1, add1)
+        depend2 = F.depend(depend1, assign2)
+        outputs = make_tuple(add1, add0, depend2)
+        output = tuple_getitem(outputs, 0)
+        return output
+
+    @fns
+    def before_cond3(input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x, mul3_x, add2_y):
+        square0 = Square(input0)
+        mul1 = Mul(mul1_x, input0)
+        mul0 = Mul(mul0_x, input2)
+        mul2 = Mul(mul2_x, input1)
+        mul3 = Mul(mul3_x, square0)
+        add0 = Add(mul0, mul1)
+        add1 = Add(mul2, mul3)
+        sqrt0 = Sqrt(add1)
+        add2 = Add(sqrt0, add2_y)
+        true_div0 = RealDiv(add0, add2)
+        mul4 = Mul(true_div0, input4)
+        sub0 = Sub(input3, mul4)
+        assign0 = Assign(input3, sub0)
+        depend0 = F.depend(sub0, assign0)
+        assign1 = Assign(input2, add0)
+        depend1 = F.depend(depend0, assign1)
+        assign2 = Assign(input1, add1)
+        depend2 = F.depend(depend1, assign2)
+        outputs = make_tuple(add1, add0, depend2)
+        output = tuple_getitem(outputs, 0)
+        return output
+
+    @fns
+    def before_cond4(input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x, mul3_x, add2_y):
+        square0 = Square(input0)
+        mul1 = Mul(mul1_x, input0)
+        mul0 = Mul(mul0_x, input2)
+        mul2 = Mul(mul2_x, input1)
+        mul3 = Mul(mul3_x, square0)
+        add0 = Add(mul0, mul1)
+        add1 = Add(mul2, mul3)
+        sqrt0 = Sqrt(add1)
+        add2 = Add(add2_y, sqrt0)
+        true_div0 = RealDiv(add0, add2)
+        mul4 = Mul(true_div0, input4)
+        sub0 = Sub(input3, mul4)
+        assign0 = Assign(input3, sub0)
+        depend0 = F.depend(sub0, assign0)
+        assign1 = Assign(input2, add0)
+        depend1 = F.depend(depend0, assign1)
+        assign2 = Assign(input1, add1)
+        depend2 = F.depend(depend1, assign2)
+        outputs = make_tuple(add1, add0, depend2)
+        output = tuple_getitem(outputs, 0)
+        return output
+
+    @fns
+    def after(input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x, mul3_x, add2_y):
+        adam_apply_one_assign = AdamApplyOneAssign(input0, input1, input2, input3, input4, mul0_x, mul1_x, mul2_x,
+                                                   mul3_x, add2_y)
+        outputs = make_tuple(tuple_getitem(adam_apply_one_assign, 0), tuple_getitem(adam_apply_one_assign, 1),
+                             tuple_getitem(adam_apply_one_assign, 2))
+        output = tuple_getitem(outputs, 0)
+        return make_tuple(output)
+
+    return fns[tag]