diff --git a/paddle/fluid/operators/elementwise/mkldnn/elementwise_mkldnn_op.h b/paddle/fluid/operators/elementwise/mkldnn/elementwise_mkldnn_op.h
index ffcdc079985fa66793599c0b7f1b3a71f400b393..ddad70a6a5f31ccb974f78ca35f045c59f45b8be 100644
--- a/paddle/fluid/operators/elementwise/mkldnn/elementwise_mkldnn_op.h
+++ b/paddle/fluid/operators/elementwise/mkldnn/elementwise_mkldnn_op.h
@@ -47,24 +47,13 @@ class EltwiseMKLDNNKernel : public framework::OpKernel<T> {
     float scale_o = ctx.Attr<float>("Scale_out");
     int axis = ctx.Attr<int>("axis");
 
-    platform::BinaryMKLDNNHandler<T> handler(BINARY_OP, axis, mkldnn_engine,
-                                             ctx.GetPlace(), x, y, z, scale_x,
-                                             scale_y, scale_o);
+    platform::BinaryMKLDNNHandler<T> handler(
+        BINARY_OP, axis, dev_ctx, mkldnn_engine, ctx.GetPlace(), x, y, z,
+        scale_x, scale_y, scale_o, ctx.OutputName("Out"));
 
     const auto src_x_memory = handler.AcquireSrcMemory(x);
     const auto src_y_memory = handler.AcquireSecondSrcMemory(y);
-    // (jczaja) For Inplace src and dst should be the same memory object.
-    // So x should share buffer with z. But UT mechanics is testing inplace
-    // execution for this op not checking that x can be bradcasted to match in
-    // shape y tensor.
-    // This is wrong as when x is to be broadcasted then z(out) will match the
-    // shape of y which is bigger than x. Hence if x is smaller in shape than z
-    // and they share a buffer (of
-    // shape x) then this buffer is not big enough to hold result of elementwise
-    // operation.
-    auto dst_memory = (x->numel() == z->numel() && x->IsSharedBufferWith(*z))
-                          ? src_x_memory
-                          : handler.AcquireDstMemory(z);
+    const auto dst_memory = handler.AcquireDstMemory(z);
 
     const auto binary_prim = handler.AcquireForwardPrimitive();
 
diff --git a/paddle/fluid/operators/elementwise/mkldnn/elementwise_mul_mkldnn_op.cc b/paddle/fluid/operators/elementwise/mkldnn/elementwise_mul_mkldnn_op.cc
index af4aab8047888a9b41b0b576535ac4f7e81002ec..1c246e8d18937087639129d32001a297eec3ca42 100644
--- a/paddle/fluid/operators/elementwise/mkldnn/elementwise_mul_mkldnn_op.cc
+++ b/paddle/fluid/operators/elementwise/mkldnn/elementwise_mul_mkldnn_op.cc
@@ -48,8 +48,9 @@ class EltwiseMulMKLDNNGradKernel : public ElemwiseGradKernel<T> {
     if (dx) {
       // dx = dout*y
       platform::BinaryMKLDNNHandler<T> handler(
-          dnnl::algorithm::binary_mul, axis, mkldnn_engine, ctx.GetPlace(),
-          dout, y, dx, 1.0f, 1.0f, 1.0f);
+          dnnl::algorithm::binary_mul, axis, dev_ctx, mkldnn_engine,
+          ctx.GetPlace(), dout, y, dx, 1.0f, 1.0f, 1.0f,
+          ctx.InputName(framework::GradVarName("Out")));
 
       const auto src_dout_memory = handler.AcquireSrcMemory(dout);
       const auto src_y_memory = handler.AcquireSecondSrcMemory(y);
@@ -74,8 +75,9 @@ class EltwiseMulMKLDNNGradKernel : public ElemwiseGradKernel<T> {
       // Handler is having nullptr passed instead of output tensor as
       // we want Dst buffer to be allocated by oneDNN not to use Tensor
       platform::BinaryMKLDNNHandler<T> handler(
-          dnnl::algorithm::binary_mul, axis, mkldnn_engine, ctx.GetPlace(),
-          dout, x, nullptr, 1.0f, 1.0f, 1.0f);
+          dnnl::algorithm::binary_mul, axis, dev_ctx, mkldnn_engine,
+          ctx.GetPlace(), dout, x, nullptr, 1.0f, 1.0f, 1.0f,
+          ctx.InputName(framework::GradVarName("Out")));
 
       const auto src_dout_memory = handler.AcquireSrcMemory(dout);
       const auto src_x_memory = handler.AcquireSecondSrcMemory(x);
diff --git a/paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc
index d992890adeec3eb4e899f8099c8bbe210e786abc..3b92d2e2d889137c005ae6de9be6942b5af49bd3 100644
--- a/paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc
+++ b/paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc
@@ -79,15 +79,15 @@ void eltwise_forward(const framework::ExecutionContext &ctx,
                     paddle::platform::errors::PreconditionNotMet(
                         "Operator DNNL eletwise_forward must use CPUPlace"));
   auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
-  const auto &mkldnn_engine = dev_ctx.GetEngine();
 
   const auto *x = ctx.Input<Tensor>("X");
   auto *y = ctx.Output<Tensor>("Out");
 
   bool is_inplaced = x->IsSharedBufferWith(*y);
 
-  platform::ActivationMKLDNNHandler<T> handler(algorithm, ctx, mkldnn_engine,
-                                               ctx.GetPlace(), x);
+  platform::ActivationMKLDNNHandler<T> handler(algorithm, ctx, dev_ctx,
+                                               ctx.GetPlace(), x,
+                                               ctx.InputName("X"), is_inplaced);
 
   auto src_memory_p = handler.AcquireSrcMemory(x);
   auto dst_memory_p = is_inplaced ? src_memory_p : handler.AcquireDstMemory(y);
@@ -106,14 +106,13 @@ template <typename T>
 void eltwise_grad(const framework::ExecutionContext &ctx,
                   mkldnn::algorithm algorithm) {
   auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
-  const auto &mkldnn_engine = dev_ctx.GetEngine();
 
   const auto *x = ctx.Input<Tensor>("X");
   const auto *diff_y = ctx.Input<Tensor>(framework::GradVarName("Out"));
   auto *diff_x = ctx.Output<Tensor>(framework::GradVarName("X"));
 
-  platform::ActivationMKLDNNHandler<T> handler(algorithm, ctx, mkldnn_engine,
-                                               ctx.GetPlace(), x, diff_y);
+  platform::ActivationMKLDNNHandler<T> handler(
+      algorithm, ctx, dev_ctx, ctx.GetPlace(), x, diff_y, ctx.InputName("X"));
 
   auto src_memory_p = handler.AcquireBackwardSrcMemory(x);
   auto diff_dst_memory_p = handler.AcquireDiffDstMemory(diff_y);
diff --git a/paddle/fluid/operators/mkldnn/caching_tests.cmake b/paddle/fluid/operators/mkldnn/caching_tests.cmake
index d7c295672e002169dc72e3ae4916280ad2082891..4130c295b203eb0fddaf7e9fd8f398baa4144c99 100644
--- a/paddle/fluid/operators/mkldnn/caching_tests.cmake
+++ b/paddle/fluid/operators/mkldnn/caching_tests.cmake
@@ -1 +1 @@
-cc_test(test_mkldnn_caching SRCS mkldnn/test_mkldnn_caching.cc DEPS op_registry elementwise_mul_op elementwise_add_op activation_op softmax_op conv_op im2col vol2col softmax scope device_context enforce)
+cc_test(test_mkldnn_caching SRCS mkldnn/test_mkldnn_caching.cc DEPS op_registry elementwise_mul_op elementwise_add_op activation_op softmax_op softmax scope device_context enforce)
diff --git a/paddle/fluid/operators/mkldnn/scale_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/scale_mkldnn_op.cc
index 84ac14d04b85b3817ddf50ef0eda9a622b2b4c27..ae17048b5d568baf4722e63299c9ef2ca3fb6bae 100644
--- a/paddle/fluid/operators/mkldnn/scale_mkldnn_op.cc
+++ b/paddle/fluid/operators/mkldnn/scale_mkldnn_op.cc
@@ -29,7 +29,6 @@ class ScaleMKLDNNKernel : public framework::OpKernel<T> {
   void RunKernel(const framework::ExecutionContext& ctx) const {
     const auto& dev_ctx =
         ctx.template device_context<platform::MKLDNNDeviceContext>();
-    const auto& mkldnn_engine = dev_ctx.GetEngine();
 
     auto* x = ctx.Input<Tensor>("X");
     auto* out = ctx.Output<Tensor>("Out");
@@ -37,12 +36,11 @@ class ScaleMKLDNNKernel : public framework::OpKernel<T> {
     bool is_inplaced = x->IsSharedBufferWith(*out);
 
     platform::ActivationMKLDNNHandler<T> handler(
-        mkldnn::algorithm::eltwise_linear, ctx, mkldnn_engine, ctx.GetPlace(),
-        x);
+        mkldnn::algorithm::eltwise_linear, ctx, dev_ctx, ctx.GetPlace(), x,
+        ctx.InputName("X"), is_inplaced);
 
     auto src_memory_p = handler.AcquireSrcMemory(x);
-    auto dst_memory_p =
-        is_inplaced ? src_memory_p : handler.AcquireDstMemory(out);
+    auto dst_memory_p = handler.AcquireDstMemory(out);
     auto activation_p = handler.AcquireForwardPrimitive();
 
     auto& astream = paddle::platform::MKLDNNDeviceContext::tls().get_stream();
diff --git a/paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc
index b0f27719bf9adc41d09b486b3bacc484cf506929..e065800e4d1c71ee4bc47fe09b26ed1ea0b9d2c9 100644
--- a/paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc
+++ b/paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc
@@ -32,56 +32,69 @@ using platform::to_void_cast;
 
 template <typename T>
 class SoftmaxMKLDNNHandler
-    : public platform::MKLDNNHandlerNoCachingT<T, mkldnn::softmax_forward,
-                                               mkldnn::softmax_backward> {
+    : public platform::MKLDNNHandlerT<T, mkldnn::softmax_forward,
+                                      mkldnn::softmax_backward> {
  public:
-  SoftmaxMKLDNNHandler(const mkldnn::engine mkldnn_engine,
+  SoftmaxMKLDNNHandler(const MKLDNNDeviceContext& dev_ctx,
+                       const mkldnn::engine mkldnn_engine,
                        platform::Place cpu_place, const Tensor* input,
-                       Tensor* output, const int axis)
-      : platform::MKLDNNHandlerNoCachingT<T, mkldnn::softmax_forward,
-                                          mkldnn::softmax_backward>(
-            mkldnn_engine, cpu_place) {
-    PADDLE_ENFORCE_EQ(
-        input->dims(), output->dims(),
-        platform::errors::InvalidArgument(
-            "The shape of input and output tensor must be identical."));
-
-    auto softmax_tz = framework::vectorize(input->dims());
-    auto md = memory::desc(softmax_tz, platform::MKLDNNGetDataType<T>(),
-                           input->format());
-
-    this->AcquireForwardPrimitiveDescriptor(prop_kind::forward_scoring, md,
-                                            axis);
+                       Tensor* output, const int axis,
+                       const std::string uniq_name, bool is_inplaced)
+      : platform::MKLDNNHandlerT<T, mkldnn::softmax_forward,
+                                 mkldnn::softmax_backward>(
+            dev_ctx, mkldnn_engine, cpu_place,
+            // Softmax may be inplace then uniq_name is no longer unique
+            is_inplaced ? platform::CreateKey(
+                              dev_ctx, framework::vectorize(input->dims()),
+                              axis, uniq_name)
+                        : platform::CreateKey(
+                              dev_ctx, framework::vectorize(input->dims()),
+                              uniq_name)) {
+    if (!this->isCached()) {
+      PADDLE_ENFORCE_EQ(
+          input->dims(), output->dims(),
+          platform::errors::InvalidArgument(
+              "The shape of input and output tensor must be identical."));
+
+      auto softmax_tz = framework::vectorize(input->dims());
+      auto md = memory::desc(softmax_tz, platform::MKLDNNGetDataType<T>(),
+                             input->format());
+
+      this->AcquireForwardPrimitiveDescriptor(prop_kind::forward_scoring, md,
+                                              axis);
+    }
   }
 
   SoftmaxMKLDNNHandler(const framework::ExecutionContext& ctx,
-                       const mkldnn::engine mkldnn_engine,
+                       const MKLDNNDeviceContext& dev_ctx,
                        platform::Place cpu_place, const Tensor* out,
                        const Tensor* out_grad, Tensor* in_x_grad,
                        const std::string& unique_name)
-      : platform::MKLDNNHandlerNoCachingT<T, mkldnn::softmax_forward,
-                                          mkldnn::softmax_backward>(
-            mkldnn_engine, cpu_place) {
-    PADDLE_ENFORCE_EQ(out_grad->dims(), in_x_grad->dims(),
-                      platform::errors::InvalidArgument(
-                          "The shape of softmax_grad's input "
-                          "and output must be identical, but shapes differ, "
-                          "out_grad: %s in_grad: %s",
-                          out_grad->dims(), in_x_grad->dims()));
-
-    auto dims = out_grad->dims();  // input and output share the same shape
-    const int axis = CanonicalAxis(ctx.Attr<int>("axis"), dims.size());
-    auto softmax_tz = framework::vectorize<int64_t>(dims);
-
-    auto data_softmax_md = MKLDNNMemDesc(
-        softmax_tz, platform::MKLDNNGetDataType<T>(), out->format());
-    auto diff_softmax_md = MKLDNNMemDesc(
-        softmax_tz, platform::MKLDNNGetDataType<T>(), out_grad->format());
-
-    this->AcquireForwardPrimitiveDescriptor(prop_kind::forward_scoring,
-                                            data_softmax_md, axis);
-    this->AcquireBackwardPrimitiveDescriptor(diff_softmax_md, data_softmax_md,
-                                             axis);
+      : platform::MKLDNNHandlerT<T, mkldnn::softmax_forward,
+                                 mkldnn::softmax_backward>(
+            dev_ctx, dev_ctx.GetEngine(), cpu_place,
+            platform::CreateKey(dev_ctx, framework::vectorize(out->dims()),
+                                unique_name)) {
+    if (!this->isBwdCached()) {
+      PADDLE_ENFORCE_EQ(
+          out_grad->dims(), in_x_grad->dims(),
+          platform::errors::InvalidArgument("The shape of softmax_grad's input "
+                                            "and output must be identical."));
+
+      auto dims = out_grad->dims();  // input and output share the same shape
+      const int axis = CanonicalAxis(ctx.Attr<int>("axis"), dims.size());
+      auto softmax_tz = framework::vectorize<int64_t>(dims);
+
+      auto data_softmax_md = MKLDNNMemDesc(
+          softmax_tz, platform::MKLDNNGetDataType<T>(), out->format());
+      auto diff_softmax_md = MKLDNNMemDesc(
+          softmax_tz, platform::MKLDNNGetDataType<T>(), out_grad->format());
+
+      this->AcquireForwardPrimitiveDescriptor(prop_kind::forward_scoring,
+                                              data_softmax_md, axis);
+      this->AcquireBackwardPrimitiveDescriptor(diff_softmax_md, data_softmax_md,
+                                               axis);
+    }
   }
 };
 
@@ -98,8 +111,9 @@ class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel<T> {
 
     const int axis = CanonicalAxis(ctx.Attr<int>("axis"), input->dims().size());
 
-    SoftmaxMKLDNNHandler<T> handler(mkldnn_engine, ctx.GetPlace(), input,
-                                    output, axis);
+    SoftmaxMKLDNNHandler<T> handler(dev_ctx, mkldnn_engine, ctx.GetPlace(),
+                                    input, output, axis, ctx.OutputName("Out"),
+                                    is_inplaced);
 
     auto softmax_src_memory_p = handler.AcquireSrcMemory(input);
     // For Inplace src and and dst are the same memory object
@@ -135,12 +149,11 @@ class SoftmaxMKLDNNGradKernel : public paddle::framework::OpKernel<T> {
                       paddle::platform::errors::PreconditionNotMet(
                           "Operator DNNL SoftmaxGrad must use CPUPlace"));
     auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
-    const auto& mkldnn_engine = dev_ctx.GetEngine();
     const Tensor* output = ctx.Input<Tensor>("Out");
     auto* out_grad = ctx.template Input<Tensor>(framework::GradVarName("Out"));
     auto* in_x_grad = ctx.template Output<Tensor>(framework::GradVarName("X"));
 
-    SoftmaxMKLDNNHandler<T> handler(ctx, mkldnn_engine, ctx.GetPlace(), output,
+    SoftmaxMKLDNNHandler<T> handler(ctx, dev_ctx, ctx.GetPlace(), output,
                                     out_grad, in_x_grad, ctx.InputName("Out"));
 
     auto dst_memory_p = handler.AcquireDstMemory(output);
diff --git a/paddle/fluid/operators/mkldnn/test_mkldnn_caching.cc b/paddle/fluid/operators/mkldnn/test_mkldnn_caching.cc
index 7251653793f89900efa5382db74201a1fc232574..cad4f47ec14022243ec04b50901a13f8d305a54e 100644
--- a/paddle/fluid/operators/mkldnn/test_mkldnn_caching.cc
+++ b/paddle/fluid/operators/mkldnn/test_mkldnn_caching.cc
@@ -33,8 +33,6 @@ USE_OP(relu);
 USE_OP_DEVICE_KERNEL(relu, MKLDNN);
 USE_OP(softmax);
 USE_OP_DEVICE_KERNEL(softmax, MKLDNN);
-USE_OP(conv2d);
-USE_OP_DEVICE_KERNEL_WITH_CUSTOM_TYPE(conv2d, MKLDNN, FP32);
 
 namespace paddle {
 namespace operators {
@@ -66,19 +64,16 @@ class CacheTester {
 
 template <typename T>
 void RunOperator(const platform::Place &place, const std::string &op_type,
-                 const framework::DDim &dims, const std::string &first_input) {
+                 const framework::DDim &dims, const std::string &output_name,
+                 bool inplace = false) {
   framework::Scope scope;
 
   std::map<const std::string, int> num_inputs = {{"softmax", 1},
                                                  {"relu", 1},
-                                                 {"conv2d", 2},
                                                  {"elementwise_add", 2},
                                                  {"elementwise_mul", 2}};
 
-  std::string first_input_var_name = (op_type == "conv2d") ? "Input" : "X";
-  std::string second_input_var_name = (op_type == "conv2d") ? "Filter" : "Y";
-  std::string output_var_name = (op_type == "conv2d") ? "Output" : "Out";
-  std::string output_name = "output";
+  std::string first_input = inplace == true ? output_name : "x";
 
   std::vector<InputVars> input_names = {
       {first_input, scope.Var(first_input)->GetMutable<framework::LoDTensor>()},
@@ -118,40 +113,71 @@ void RunOperator(const platform::Place &place, const std::string &op_type,
 
   auto &pool = platform::DeviceContextPool::Instance();
 
-  auto op =
-      num_inputs[op_type] > 1
-          ? framework::OpRegistry::CreateOp(
-                op_type, {{first_input_var_name, {first_input}},
-                          {second_input_var_name, {"x1"}}},
-                {{output_var_name, {output_name}}}, {{"use_mkldnn", {true}}})
-          : framework::OpRegistry::CreateOp(
-                op_type, {{first_input_var_name, {first_input}}},
-                {{output_var_name, {output_name}}}, {{"use_mkldnn", {true}}});
+  auto op = num_inputs[op_type] > 1
+                ? framework::OpRegistry::CreateOp(
+                      op_type, {{"X", {first_input}}, {"Y", {"x1"}}},
+                      {{"Out", {output_name}}}, {{"use_mkldnn", {true}}})
+                : framework::OpRegistry::CreateOp(
+                      op_type, {{"X", {first_input}}}, {{"Out", {output_name}}},
+                      {{"use_mkldnn", {true}}});
 
   op->Run(scope, place);
   pool.Get(place)->Wait();
 }
 
-TEST(test_conv2d_reuse_cache, cpu_place) {
-  framework::DDim dims({1, 16, 32, 64});
+TEST(test_softmax_reuse_cache, cpu_place) {
+  framework::DDim dims({32, 64});
   platform::CPUPlace p;
   CacheTester ct;
-  RunOperator<float>(p, "conv2d", dims, "input_signal");
-  RunOperator<float>(p, "conv2d", dims, "input_signal");
-  PADDLE_ENFORCE_EQ(ct.Analyze(9), true,
+  RunOperator<float>(p, "softmax", dims, "softmax_out");
+  RunOperator<float>(p, "softmax", dims, "softmax_out");
+  PADDLE_ENFORCE_EQ(ct.Analyze(4), true,
                     platform::errors::InvalidArgument(
-                        "Invalid number of cached oneDNN objects"));
+                        "Wrong number of cached oneDNN objects"));
 }
 
-TEST(test_conv2d_noreuse_cache, cpu_place) {
-  framework::DDim dims({1, 16, 32, 64});
+TEST(test_softmax_noreuse_cache, cpu_place) {
+  framework::DDim dims({32, 64});
   platform::CPUPlace p;
   CacheTester ct;
-  RunOperator<float>(p, "conv2d", dims, "input_signal");
-  RunOperator<float>(p, "conv2d", dims, "input_signal2");
-  PADDLE_ENFORCE_EQ(ct.Analyze(18), true,
+  RunOperator<float>(p, "softmax", dims, "softmax_out");
+  RunOperator<float>(p, "softmax", dims, "softmax_out2");
+  PADDLE_ENFORCE_EQ(ct.Analyze(8), true,
                     platform::errors::InvalidArgument(
-                        "Invalid number of cached oneDNN objects"));
+                        "Wrong number of cached oneDNN objects"));
+}
+
+TEST(test_softmax_inplace_cache, cpu_place) {
+  framework::DDim dims({32, 64});
+  platform::CPUPlace p;
+  CacheTester ct;
+  RunOperator<float>(p, "softmax", dims, "softmax_out");
+  RunOperator<float>(p, "softmax", dims, "softmax_out", true);
+  PADDLE_ENFORCE_EQ(ct.Analyze(7), true,
+                    platform::errors::InvalidArgument(
+                        "Wrong number of cached oneDNN objects"));
+}
+
+TEST(test_relu_inplace_cache, cpu_place) {
+  framework::DDim dims({32, 64});
+  platform::CPUPlace p;
+  CacheTester ct;
+  RunOperator<float>(p, "relu", dims, "relu_out");
+  RunOperator<float>(p, "relu", dims, "relu_out", true);
+  PADDLE_ENFORCE_EQ(ct.Analyze(7), true,
+                    platform::errors::InvalidArgument(
+                        "Wrong number of cached oneDNN objects"));
+}
+
+TEST(test_elementwise_add_reuse_cache, cpu_place) {
+  framework::DDim dims({32, 64});
+  platform::CPUPlace p;
+  CacheTester ct;
+  RunOperator<float>(p, "elementwise_add", dims, "elementwise_add_out");
+  RunOperator<float>(p, "relu", dims, "elementwise_add_out", true);
+  PADDLE_ENFORCE_EQ(ct.Analyze(8), true,
+                    platform::errors::InvalidArgument(
+                        "Wrong number of cached oneDNN objects"));
 }
 
 }  // namespace operators
diff --git a/paddle/fluid/platform/mkldnn_reuse.h b/paddle/fluid/platform/mkldnn_reuse.h
index 95b8e0c610b1d4b3b8892c451258c07363e2609f..f63d45d7ff6ae611dc1633e94dac00c4f6db2339 100644
--- a/paddle/fluid/platform/mkldnn_reuse.h
+++ b/paddle/fluid/platform/mkldnn_reuse.h
@@ -34,211 +34,6 @@ using framework::Tensor;
 using user_function = std::function<std::shared_ptr<float>(const float*)>;
 using memory = mkldnn::memory;
 
-template <typename T, typename TForward,
-          typename TBackward = mkldnn_dummy_primitive,
-          typename TBackward_params = mkldnn_dummy_primitive>
-class MKLDNNHandlerNoCachingT {
- public:
-  MKLDNNHandlerNoCachingT(mkldnn::engine engine, platform::Place cpu_place)
-      : engine_(engine), place_(cpu_place), fwd_pd_(nullptr), bwd_pd_(nullptr) {
-    platform::MKLDNNDeviceContext::tls().log_lib_version();
-  }
-
-  std::shared_ptr<TForward> AcquireForwardPrimitive() {
-    return std::make_shared<TForward>(*fwd_pd_);
-  }
-
-  std::shared_ptr<TBackward> AcquireBackwardPrimitive() {
-    return std::make_shared<TBackward>(*bwd_pd_);
-  }
-
-  std::shared_ptr<TBackward_params> AcquireBackwardWeightsPrimitive() {
-    PADDLE_ENFORCE_NOT_NULL(
-        bwd_w_pd_, platform::errors::Unavailable("BWD_PD should be set when "
-                                                 "getting BWD prim ."));
-    return std::make_shared<TBackward_params>(*bwd_w_pd_);
-  }
-
-  std::shared_ptr<mkldnn::memory> AcquireSrcMemory(
-      const framework::Tensor* input) {
-    const T* input_data = input->data<T>();
-    return this->AcquireMemoryFromPrimitive(fwd_pd_->src_desc(),
-                                            to_void_cast<T>(input_data));
-  }
-
-  template <typename T_out = T>
-  std::shared_ptr<mkldnn::memory> AcquireDstMemory(framework::Tensor* output) {
-    T_out* ptr =
-        output->mutable_data<T_out>(place_, fwd_pd_->dst_desc().get_size());
-    return this->AcquireMemoryFromPrimitive(fwd_pd_->dst_desc(), ptr);
-  }
-
-  template <typename T_out = T>
-  std::shared_ptr<mkldnn::memory> AcquireDstMemory(void) {
-    return this->AcquireMemoryFromPrimitive(fwd_pd_->dst_desc());
-  }
-
-  template <typename T_out = T>
-  std::shared_ptr<mkldnn::memory> AcquireDstMemory(
-      const framework::Tensor* output) {
-    const T_out* output_data = output->data<T_out>();
-    return this->AcquireMemoryFromPrimitive(bwd_pd_->dst_desc(),
-                                            to_void_cast<T_out>(output_data));
-  }
-
-  std::shared_ptr<mkldnn::memory> AcquireDiffDstMemory(
-      const framework::Tensor* diffdst) {
-    const T* ptr = diffdst->data<T>();
-    return this->AcquireMemoryFromPrimitive(bwd_pd_->diff_dst_desc(),
-                                            to_void_cast<T>(ptr));
-  }
-
-  std::shared_ptr<mkldnn::memory> AcquireDiffSrcMemory(
-      framework::Tensor* diffsrc) {
-    T* ptr =
-        diffsrc->mutable_data<T>(place_, bwd_pd_->diff_src_desc().get_size());
-    return this->AcquireMemoryFromPrimitive(bwd_pd_->diff_src_desc(), ptr);
-  }
-
-  // Buffer of given Tensor is used for oneDNN computation
-  std::shared_ptr<mkldnn::memory> AcquireDiffWeightsMemory(
-      framework::Tensor* diff_weights) {
-    PADDLE_ENFORCE_NOT_NULL(
-        bwd_w_pd_,
-        platform::errors::Unavailable(
-            "BWD_W_PD should be set when getting BWD grad of weights."));
-    T* ptr = diff_weights->mutable_data<T>(
-        place_, bwd_w_pd_->diff_weights_desc().get_size());
-    return this->AcquireMemoryFromPrimitive(bwd_w_pd_->diff_weights_desc(),
-                                            ptr);
-  }
-
-  // Buffer is allocated by oneDNN to store computation results
-  std::shared_ptr<mkldnn::memory> AcquireDiffWeightsMemory(void) {
-    PADDLE_ENFORCE_NOT_NULL(
-        bwd_w_pd_,
-        platform::errors::Unavailable(
-            "BWD_W_PD should be set when getting BWD grad of weights."));
-    return this->AcquireMemoryFromPrimitive(bwd_w_pd_->diff_weights_desc());
-  }
-
- protected:
-  // If your primitive descriptor requires attributes, pass them as a
-  // first argument and paramters to descriptor constructor in the following
-  // arguments. Otherwise, all arguments will be forwarded to descriptor
-  // constructor, including the first one.
-  template <typename Arg, typename... Args>
-  void AcquireForwardPrimitiveDescriptor(Arg&& first_arg, Args&&... args) {
-    CreateForwardPrimitiveDescriptor(first_arg, std::forward<Args>(args)...);
-  }
-
-  // Using sfinae to specialise variadic function. Workaround for not having
-  // if constexpr in C++ 11.
-  template <class First, class... Args>
-  typename std::enable_if<std::is_same<typename std::decay<First>::type,
-                                       dnnl::primitive_attr>::value>::type
-  CreateForwardPrimitiveDescriptor(First&& first, Args&&... args) {
-    auto fwd_desc = typename TForward::desc(std::forward<Args>(args)...);
-    fwd_pd_ = std::make_shared<typename TForward::primitive_desc>(
-        fwd_desc, first, engine_);
-  }
-
-  template <class First, class... Args>
-  typename std::enable_if<!std::is_same<typename std::decay<First>::type,
-                                        dnnl::primitive_attr>::value>::type
-  CreateForwardPrimitiveDescriptor(First&& first, Args&&... args) {
-    auto fwd_desc = typename TForward::desc(std::forward<First>(first),
-                                            std::forward<Args>(args)...);
-    fwd_pd_ =
-        std::make_shared<typename TForward::primitive_desc>(fwd_desc, engine_);
-  }
-
-  template <typename... Args>
-  void AcquireBackwardPrimitiveDescriptor(Args&&... args) {
-    // fwd_pd_ is set during grad by calling
-    // AcquireForwardPrimitiveDescriptor
-    PADDLE_ENFORCE_NOT_NULL(fwd_pd_,
-                            platform::errors::Unavailable(
-                                "Get MKLDNN Forward primitive %s failed."));
-    auto bwd_desc = typename TBackward::desc(std::forward<Args>(args)...);
-    bwd_pd_ = std::make_shared<typename TBackward::primitive_desc>(
-        bwd_desc, engine_, *fwd_pd_);
-  }
-
-  template <typename... Args>
-  void AcquireBackwardWeightsPrimitiveDescriptor(Args&&... args) {
-    // fwd_pd_ is set during grad by calling
-    // AcquireForwardPrimitiveDescriptor
-    PADDLE_ENFORCE_NOT_NULL(fwd_pd_,
-                            platform::errors::Unavailable(
-                                "Get MKLDNN Forward primitive %s failed."));
-    auto bwd_desc =
-        typename TBackward_params::desc(std::forward<Args>(args)...);
-    bwd_w_pd_ = std::make_shared<typename TBackward_params::primitive_desc>(
-        bwd_desc, engine_, *fwd_pd_);
-  }
-
-  std::shared_ptr<mkldnn::memory> AcquireMemoryFromPrimitive(
-      mkldnn::memory::desc md, void* ptr) {
-    return std::make_shared<mkldnn::memory>(md, engine_, ptr);
-  }
-
-  std::shared_ptr<mkldnn::memory> AcquireMemoryFromPrimitive(
-      mkldnn::memory::desc md) {
-    return std::make_shared<mkldnn::memory>(md, engine_);
-  }
-
-  void AcquireReorder(const std::shared_ptr<mkldnn::memory>& user_memory_p,
-                      const std::shared_ptr<mkldnn::memory>& target_memory_p) {
-    auto reorder_p =
-        std::make_shared<mkldnn::reorder>(*user_memory_p, *target_memory_p);
-
-    auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
-
-    platform::RecordEvent record_reorder("int_reorder",
-                                         platform::EventRole::kUniqueOp);
-    reorder_p->execute(astream, {{MKLDNN_ARG_FROM, *user_memory_p},
-                                 {MKLDNN_ARG_TO, *target_memory_p}});
-    astream.wait();
-  }
-
-  template <typename F = T>
-  std::shared_ptr<mkldnn::memory> AcquireMemoryWithReorder(
-      const mkldnn::memory::desc& user_md,
-      const mkldnn::memory::desc& target_md, void* ptr,
-      const std::string& suffix, bool is_persistent = false,
-      std::function<std::shared_ptr<F>(const F*)> custom_reorder_func = {}) {
-    std::shared_ptr<mkldnn::memory> target_memory_p;
-    if (custom_reorder_func) {
-      auto reordered_data =
-          custom_reorder_func(reinterpret_cast<const F*>(ptr));
-      ptr = reinterpret_cast<void*>(reordered_data.get());
-    }
-    auto user_memory_p = std::make_shared<dnnl::memory>(user_md, engine_, ptr);
-    if (user_md != target_md) {
-      target_memory_p = std::make_shared<mkldnn::memory>(target_md, engine_);
-      auto reorder_p =
-          std::make_shared<dnnl::reorder>(*user_memory_p, *target_memory_p);
-
-      auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
-      platform::RecordEvent record_reorder("int_reorder",
-                                           platform::EventRole::kUniqueOp);
-      reorder_p->execute(astream, {{MKLDNN_ARG_FROM, *user_memory_p},
-                                   {MKLDNN_ARG_TO, *target_memory_p}});
-      astream.wait();
-    } else {
-      target_memory_p = user_memory_p;
-    }
-    return target_memory_p;
-  }
-
-  mkldnn::engine engine_;
-  platform::Place place_;
-  std::shared_ptr<typename TForward::primitive_desc> fwd_pd_;
-  std::shared_ptr<typename TBackward::primitive_desc> bwd_pd_;
-  std::shared_ptr<typename TBackward_params::primitive_desc> bwd_w_pd_;
-};
-
 template <typename T, typename TForward,
           typename TBackward = mkldnn_dummy_primitive,
           typename TBackward_params = mkldnn_dummy_primitive>
@@ -284,7 +79,7 @@ class MKLDNNHandlerT {
         std::static_pointer_cast<TBackward_params>(dev_ctx_.GetBlob(key_p));
     if (backward_p == nullptr) {
       PADDLE_ENFORCE_NOT_NULL(bwd_w_pd_, platform::errors::Unavailable(
-                                             "BWD_PD should be set when "
+                                             "Error: BWD_PD should be set when "
                                              "getting BWD prim witk key: %s .",
                                              key_p));
       backward_p = std::make_shared<TBackward_params>(*bwd_w_pd_);
@@ -343,7 +138,7 @@ class MKLDNNHandlerT {
     PADDLE_ENFORCE_NOT_NULL(
         bwd_w_pd_,
         platform::errors::Unavailable(
-            "BWD_W_PD should be set when getting BWD grad of weights."));
+            "Error: BWD_W_PD should be set when getting BWD grad of weights."));
     T* ptr = diff_weights->mutable_data<T>(
         place_, bwd_w_pd_->diff_weights_desc().get_size());
     return this->AcquireMemoryFromPrimitive(bwd_w_pd_->diff_weights_desc(), ptr,
@@ -355,7 +150,7 @@ class MKLDNNHandlerT {
     PADDLE_ENFORCE_NOT_NULL(
         bwd_w_pd_,
         platform::errors::Unavailable(
-            "BWD_W_PD should be set when getting BWD grad of weights."));
+            "Error: BWD_W_PD should be set when getting BWD grad of weights."));
     return this->AcquireMemoryFromPrimitive(bwd_w_pd_->diff_weights_desc(),
                                             "@diff_wei_mem_p");
   }
@@ -794,70 +589,70 @@ class MKLDNNHandler {
 };
 
 template <typename T>
-class BinaryMKLDNNHandler
-    : public platform::MKLDNNHandlerNoCachingT<T, dnnl::binary> {
+class BinaryMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::binary> {
  public:
   BinaryMKLDNNHandler(const dnnl::algorithm algo, const int axis,
+                      const MKLDNNDeviceContext& dev_ctx,
                       const mkldnn::engine engine, platform::Place cpu_place,
                       const Tensor* x, const Tensor* y, Tensor* z,
-                      float scale_x, float scale_y, float scale_z)
-      : platform::MKLDNNHandlerNoCachingT<T, dnnl::binary>(engine, cpu_place) {
-    PADDLE_ENFORCE_EQ(
-        x->layout(), DataLayout::kMKLDNN,
-        platform::errors::InvalidArgument(
-            "Wrong layout set for X tensor. Expected: %d (kMKLDNN), Actual: %d",
-            DataLayout::kMKLDNN, x->layout()));
-    PADDLE_ENFORCE_NE(x->format(), MKLDNNMemoryFormat::undef,
-                      platform::errors::InvalidArgument(
-                          "Wrong format set for X tensor : %d (undef)",
-                          static_cast<unsigned int>(x->format())));
-
-    PADDLE_ENFORCE_EQ(
-        y->layout(), DataLayout::kMKLDNN,
-        platform::errors::InvalidArgument(
-            "Wrong layout set for Y tensor. Expected: %d (kMKLDNN), Actual: %d",
-            DataLayout::kMKLDNN, y->layout()));
-    PADDLE_ENFORCE_NE(y->format(), MKLDNNMemoryFormat::undef,
-                      platform::errors::InvalidArgument(
-                          "Wrong format set for Y tensor : %d (undef)",
-                          static_cast<unsigned int>(y->format())));
-
-    const auto src_x_tz = framework::vectorize(x->dims());
-    const auto src_y_tz = framework::vectorize(y->dims());
-    // if output tensor(z) is nullptr then we are computing into oneDNN
-    // managed buffer
-    auto rankdiff = x->dims().size() - y->dims().size();
-    const auto dst_tz = (z == nullptr) ? (rankdiff > 0 ? src_x_tz : src_y_tz)
-                                       : framework::vectorize(z->dims());
-
-    auto src0_md = dnnl::memory::desc(
-        src_x_tz, platform::MKLDNNGetDataType<T>(), x->format());
-    auto src1_md = dnnl::memory::desc(
-        src_y_tz, platform::MKLDNNGetDataType<T>(), y->format());
-    if (rankdiff > 0) {  // Second input is of smaller rank than first
-      std::vector<int64_t> dims1_ex(rankdiff, 1);
-      dims1_ex.insert(next(dims1_ex.begin(), (axis == -1 ? rankdiff : axis)),
-                      src_y_tz.begin(), src_y_tz.end());
-      src1_md = src1_md.reshape(dims1_ex);
-    } else if (rankdiff < 0) {  // First input is of smaller than second
-      std::vector<int64_t> dims0_ex(-rankdiff, 1);
-      dims0_ex.insert(next(dims0_ex.begin(), (axis == -1 ? -rankdiff : axis)),
-                      src_x_tz.begin(), src_x_tz.end());
-      src0_md = src0_md.reshape(dims0_ex);
-    }
-    const auto dst_md = memory::desc(dst_tz, platform::MKLDNNGetDataType<T>(),
-                                     MKLDNNMemoryFormat::any);
+                      float scale_x, float scale_y, float scale_z,
+                      const std::string& uniq_name)
+      : platform::MKLDNNHandlerT<T, dnnl::binary>(
+            dev_ctx, engine, cpu_place,
+            platform::CreateKey(dev_ctx, framework::vectorize(x->dims()),
+                                uniq_name)) {
+    if (!this->isCached()) {
+      PADDLE_ENFORCE_EQ(
+          x->layout(), DataLayout::kMKLDNN,
+          platform::errors::InvalidArgument("Wrong layout set for X tensor."));
+      PADDLE_ENFORCE_NE(
+          x->format(), MKLDNNMemoryFormat::undef,
+          platform::errors::InvalidArgument("Wrong format set for X tensor."));
+
+      PADDLE_ENFORCE_EQ(
+          y->layout(), DataLayout::kMKLDNN,
+          platform::errors::InvalidArgument("Wrong layout set for Y tensor."));
+      PADDLE_ENFORCE_NE(
+          y->format(), MKLDNNMemoryFormat::undef,
+          platform::errors::InvalidArgument("Wrong format set for Y tensor."));
+
+      const auto src_x_tz = framework::vectorize(x->dims());
+      const auto src_y_tz = framework::vectorize(y->dims());
+      // if output tensor(z) is nullptr then we are computing into oneDNN
+      // managed buffer
+      auto rankdiff = x->dims().size() - y->dims().size();
+      const auto dst_tz = (z == nullptr) ? (rankdiff > 0 ? src_x_tz : src_y_tz)
+                                         : framework::vectorize(z->dims());
+
+      auto src0_md = dnnl::memory::desc(
+          src_x_tz, platform::MKLDNNGetDataType<T>(), x->format());
+      auto src1_md = dnnl::memory::desc(
+          src_y_tz, platform::MKLDNNGetDataType<T>(), y->format());
+      if (rankdiff > 0) {  // Second input is of smaller rank than first
+        std::vector<int64_t> dims1_ex(rankdiff, 1);
+        dims1_ex.insert(next(dims1_ex.begin(), (axis == -1 ? rankdiff : axis)),
+                        src_y_tz.begin(), src_y_tz.end());
+        src1_md = src1_md.reshape(dims1_ex);
+      } else if (rankdiff < 0) {  // First input is of smaller than second
+        std::vector<int64_t> dims0_ex(-rankdiff, 1);
+        dims0_ex.insert(next(dims0_ex.begin(), (axis == -1 ? -rankdiff : axis)),
+                        src_x_tz.begin(), src_x_tz.end());
+        src0_md = src0_md.reshape(dims0_ex);
+      }
+      const auto dst_md = memory::desc(dst_tz, platform::MKLDNNGetDataType<T>(),
+                                       MKLDNNMemoryFormat::any);
 
-    auto attributes = CreateAttributes(algo, scale_x, scale_y, scale_z);
-    this->AcquireForwardPrimitiveDescriptor(attributes, algo, src0_md, src1_md,
-                                            dst_md);
+      auto attributes = CreateAttributes(algo, scale_x, scale_y, scale_z);
+      this->AcquireForwardPrimitiveDescriptor(attributes, algo, src0_md,
+                                              src1_md, dst_md);
+    }
   }
 
   std::shared_ptr<mkldnn::memory> AcquireSecondSrcMemory(
       const framework::Tensor* input) {
     const T* input_data = input->data<T>();
-    return this->AcquireMemoryFromPrimitive(this->fwd_pd_->src1_desc(),
-                                            to_void_cast<T>(input_data));
+    return this->AcquireMemoryFromPrimitive(
+        this->fwd_pd_->src1_desc(), to_void_cast<T>(input_data), "@src1_mem_p");
   }
 
  private:
@@ -980,95 +775,111 @@ class ReductionMKLDNNHandler
 
 template <typename T>
 class ActivationMKLDNNHandler
-    : public MKLDNNHandlerNoCachingT<T, mkldnn::eltwise_forward,
-                                     mkldnn::eltwise_backward> {
+    : public MKLDNNHandlerT<T, mkldnn::eltwise_forward,
+                            mkldnn::eltwise_backward> {
  public:
   ActivationMKLDNNHandler(mkldnn::algorithm algorithm,
                           const framework::ExecutionContext& ctx,
-                          const mkldnn::engine engine, Place cpu_place,
-                          const framework::Tensor* in_x)
-      : platform::MKLDNNHandlerNoCachingT<T, mkldnn::eltwise_forward,
-                                          mkldnn::eltwise_backward>(engine,
-                                                                    cpu_place) {
-    float alpha = ctx.HasAttr("alpha") ? ctx.Attr<float>("alpha") : 0;
-    float beta = ctx.HasAttr("beta") ? ctx.Attr<float>("beta") : 0;
-    // eltwise_linear means we are in scale op
-    if (algorithm == mkldnn::algorithm::eltwise_linear) {
-      bool bias_after_scale = ctx.Attr<bool>("bias_after_scale");
-      auto* scale_tensor = ctx.Input<Tensor>("ScaleTensor");
-      alpha = (scale_tensor == nullptr) ? ctx.Attr<float>("scale")
-                                        : (float)*(scale_tensor->data<T>());
-      beta = ctx.Attr<float>("bias");
-      // if bias_after_scale == true
-      //   out = scale*X + bias
-      // else
-      //   out = scale*(X + bias) = scale*X + scale*bias
-      if (!bias_after_scale) beta *= alpha;
-    } else {
-      // paddle uses beta but mkldnn uses alpha for swish
-      if (algorithm == mkldnn::algorithm::eltwise_swish) {
-        std::swap(alpha, beta);
-      } else if (algorithm == dnnl::algorithm::eltwise_bounded_relu) {
-        alpha = ctx.Attr<float>("threshold");
+                          const MKLDNNDeviceContext& dev_ctx, Place cpu_place,
+                          const framework::Tensor* in_x,
+                          const std::string& unique_name, bool is_inplaced)
+      : platform::MKLDNNHandlerT<T, mkldnn::eltwise_forward,
+                                 mkldnn::eltwise_backward>(
+            dev_ctx, dev_ctx.GetEngine(), cpu_place,
+            is_inplaced ? platform::CreateKey(
+                              dev_ctx, framework::vectorize(in_x->dims()), "a",
+                              algorithm, unique_name)
+                        : platform::CreateKey(
+                              dev_ctx, framework::vectorize(in_x->dims()), "a",
+                              unique_name)) {
+    if (!this->isCached()) {
+      float alpha = ctx.HasAttr("alpha") ? ctx.Attr<float>("alpha") : 0;
+      float beta = ctx.HasAttr("beta") ? ctx.Attr<float>("beta") : 0;
+      // eltwise_linear means we are in scale op
+      if (algorithm == mkldnn::algorithm::eltwise_linear) {
+        bool bias_after_scale = ctx.Attr<bool>("bias_after_scale");
+        auto* scale_tensor = ctx.Input<Tensor>("ScaleTensor");
+        alpha = (scale_tensor == nullptr) ? ctx.Attr<float>("scale")
+                                          : (float)*(scale_tensor->data<T>());
+        beta = ctx.Attr<float>("bias");
+        // if bias_after_scale == true
+        //   out = scale*X + bias
+        // else
+        //   out = scale*(X + bias) = scale*X + scale*bias
+        if (!bias_after_scale) beta *= alpha;
+      } else {
+        // paddle uses beta but mkldnn uses alpha for swish
+        if (algorithm == mkldnn::algorithm::eltwise_swish) {
+          std::swap(alpha, beta);
+        } else if (algorithm == dnnl::algorithm::eltwise_bounded_relu) {
+          alpha = ctx.Attr<float>("threshold");
+        }
       }
-    }
 
-    PADDLE_ENFORCE(in_x->dims().size() >= 1 || in_x->dims().size() <= 6,
-                   platform::errors::Unimplemented(
-                       "Input dimension size can be 1, 2, 3, 4, "
-                       "5, or 6, but now the dimension size is",
-                       in_x->dims().size()));
+      PADDLE_ENFORCE(in_x->dims().size() >= 1 || in_x->dims().size() <= 6,
+                     platform::errors::Unimplemented(
+                         "Input dimension size can be 1, 2, 3, 4, "
+                         "5, or 6, but now the dimension size is",
+                         in_x->dims().size()));
 
-    auto src_tz = framework::vectorize<int64_t>(in_x->dims());
-    auto src_fmt = src_tz.size() == 2 ? MKLDNNMemoryFormat::nc : in_x->format();
-    auto md =
-        mkldnn::memory::desc(src_tz, platform::MKLDNNGetDataType<T>(), src_fmt);
+      auto src_tz = framework::vectorize<int64_t>(in_x->dims());
+      auto src_fmt =
+          src_tz.size() == 2 ? MKLDNNMemoryFormat::nc : in_x->format();
+      auto md = mkldnn::memory::desc(src_tz, platform::MKLDNNGetDataType<T>(),
+                                     src_fmt);
 
-    this->AcquireForwardPrimitiveDescriptor(mkldnn::prop_kind::forward_training,
-                                            algorithm, md, alpha, beta);
+      this->AcquireForwardPrimitiveDescriptor(
+          mkldnn::prop_kind::forward_training, algorithm, md, alpha, beta);
+    }
   }
 
   ActivationMKLDNNHandler(mkldnn::algorithm algorithm,
                           const framework::ExecutionContext& ctx,
-                          const mkldnn::engine engine, Place cpu_place,
-                          const framework::Tensor* in_x, const Tensor* out_grad)
-      : platform::MKLDNNHandlerNoCachingT<T, mkldnn::eltwise_forward,
-                                          mkldnn::eltwise_backward>(engine,
-                                                                    cpu_place) {
-    float alpha = ctx.HasAttr("alpha") ? ctx.Attr<float>("alpha") : 0;
-    float beta = ctx.HasAttr("beta") ? ctx.Attr<float>("beta") : 0;
-
-    // paddle uses beta but mkldnn uses alpha for swish
-    if (algorithm == mkldnn::algorithm::eltwise_swish) {
-      std::swap(alpha, beta);
-    } else if (algorithm == dnnl::algorithm::eltwise_bounded_relu) {
-      alpha = ctx.Attr<float>("threshold");
-    }
+                          const MKLDNNDeviceContext& dev_ctx, Place cpu_place,
+                          const framework::Tensor* in_x, const Tensor* out_grad,
+                          const std::string& unique_name)
+      : platform::MKLDNNHandlerT<T, mkldnn::eltwise_forward,
+                                 mkldnn::eltwise_backward>(
+            dev_ctx, dev_ctx.GetEngine(), cpu_place,
+            platform::CreateKey(dev_ctx, framework::vectorize(in_x->dims()),
+                                "a", unique_name)) {
+    if (!this->isBwdCached()) {
+      float alpha = ctx.HasAttr("alpha") ? ctx.Attr<float>("alpha") : 0;
+      float beta = ctx.HasAttr("beta") ? ctx.Attr<float>("beta") : 0;
+
+      // paddle uses beta but mkldnn uses alpha for swish
+      if (algorithm == mkldnn::algorithm::eltwise_swish) {
+        std::swap(alpha, beta);
+      } else if (algorithm == dnnl::algorithm::eltwise_bounded_relu) {
+        alpha = ctx.Attr<float>("threshold");
+      }
 
-    auto diff_dst_tz = framework::vectorize<int64_t>(out_grad->dims());
+      auto diff_dst_tz = framework::vectorize<int64_t>(out_grad->dims());
 
-    auto src_fmt =
-        diff_dst_tz.size() == 2 ? MKLDNNMemoryFormat::nc : in_x->format();
-    auto diff_fmt =
-        diff_dst_tz.size() == 2 ? MKLDNNMemoryFormat::nc : out_grad->format();
+      auto src_fmt =
+          diff_dst_tz.size() == 2 ? MKLDNNMemoryFormat::nc : in_x->format();
+      auto diff_fmt =
+          diff_dst_tz.size() == 2 ? MKLDNNMemoryFormat::nc : out_grad->format();
 
-    auto dims = framework::vectorize(in_x->dims());
-    auto diff_dst_md = platform::MKLDNNMemDesc(
-        dims, platform::MKLDNNGetDataType<T>(), diff_fmt);
-    auto src_md = platform::MKLDNNMemDesc(
-        dims, platform::MKLDNNGetDataType<T>(), src_fmt);
+      auto dims = framework::vectorize(in_x->dims());
+      auto diff_dst_md = platform::MKLDNNMemDesc(
+          dims, platform::MKLDNNGetDataType<T>(), diff_fmt);
+      auto src_md = platform::MKLDNNMemDesc(
+          dims, platform::MKLDNNGetDataType<T>(), src_fmt);
 
-    this->AcquireForwardPrimitiveDescriptor(mkldnn::prop_kind::forward_training,
-                                            algorithm, src_md, alpha, beta);
-    this->AcquireBackwardPrimitiveDescriptor(algorithm, diff_dst_md, src_md,
-                                             alpha, beta);
+      this->AcquireForwardPrimitiveDescriptor(
+          mkldnn::prop_kind::forward_training, algorithm, src_md, alpha, beta);
+      this->AcquireBackwardPrimitiveDescriptor(algorithm, diff_dst_md, src_md,
+                                               alpha, beta);
+    }
   }
 
   std::shared_ptr<mkldnn::memory> AcquireBackwardSrcMemory(
       const framework::Tensor* input) {
     const T* input_data = input->data<T>();
     return this->AcquireMemoryFromPrimitive(this->bwd_pd_->src_desc(),
-                                            to_void_cast<T>(input_data));
+                                            to_void_cast<T>(input_data),
+                                            "@bwd-src_mem_p");
   }
 };
 
@@ -1619,6 +1430,11 @@ using ConvMKLDNNHandler =
                               mkldnn::convolution_backward_data,
                               mkldnn::convolution_backward_weights>;
 
+using ConvTransposeMKLDNNHandler =
+    ConvMKLDNNTemplateHandler<mkldnn::deconvolution_forward,
+                              mkldnn::deconvolution_backward_data,
+                              mkldnn::deconvolution_backward_weights>;
+
 template <typename T>
 static std::shared_ptr<mkldnn::memory> SetDstMemory(
     const framework::ExecutionContext& ctx, framework::Tensor* output,