diff --git a/paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc
index a71fd3fa02d047ad497e4053d7120e5979629079..4909fdc32ba6fc96be6d900cdb4ba0cb735a44f7 100644
--- a/paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc
+++ b/paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc
@@ -42,51 +42,6 @@ class MKLDNNActivationKernel
   }
 };
 
-template <typename Functor>
-class MKLDNNActivationGradKernel
-    : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
- public:
-  void Compute(const framework::ExecutionContext &ctx) const override {
-    Functor functor;
-    functor(ctx);
-  }
-};
-
-template <typename T>
-void eltwise_grad(const framework::ExecutionContext &ctx,
-                  dnnl::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 *dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
-  auto *dx = ctx.Output<Tensor>(framework::GradVarName("X"));
-
-  platform::ActivationMKLDNNHandler<T> handler(
-      algorithm, ctx, mkldnn_engine, ctx.GetPlace(), x, dout);
-
-  auto src_memory_p = handler.AcquireBackwardSrcMemory(x);
-  auto diff_dst_memory_p = handler.AcquireDiffDstMemory(dout);
-  auto diff_src_memory_p = handler.AcquireDiffSrcMemory(dx);
-  auto activation_backward_p = handler.AcquireBackwardPrimitive();
-
-  auto &astream = paddle::platform::MKLDNNDeviceContext::tls().get_stream();
-  activation_backward_p->execute(astream,
-                                 {{DNNL_ARG_SRC, *src_memory_p},
-                                  {DNNL_ARG_DIFF_DST, *diff_dst_memory_p},
-                                  {DNNL_ARG_DIFF_SRC, *diff_src_memory_p}});
-  astream.wait();
-
-  dx->set_mem_desc(diff_src_memory_p->get_desc());
-}
-
-template <typename T, dnnl::algorithm algorithm>
-struct MKLDNNActivationGradFunc : public BaseActivationFunctor<T> {
-  void operator()(const framework::ExecutionContext &ctx) const {
-    eltwise_grad<T>(ctx, algorithm);
-  }
-};
-
 template <typename T>
 struct SoftplusMKLDNNFunctor : public BaseActivationFunctor<T> {
   void operator()(const framework::ExecutionContext &ctx) const {
@@ -94,10 +49,6 @@ struct SoftplusMKLDNNFunctor : public BaseActivationFunctor<T> {
   }
 };
 
-template <typename T>
-using Relu6MKLDNNGradFunctor =
-    MKLDNNActivationGradFunc<T, dnnl::algorithm::eltwise_bounded_relu>;
-
 }  // namespace operators
 }  // namespace paddle
 
@@ -111,14 +62,4 @@ namespace ops = paddle::operators;
       ops::MKLDNNActivationKernel<ops::functor<float>>,          \
       ops::MKLDNNActivationKernel<ops::functor<paddle::platform::bfloat16>>);
 
-#define REGISTER_GRAD_ACTIVATION_MKLDNN_KERNEL(act_type, grad_functor) \
-  REGISTER_OP_KERNEL(                                                  \
-      act_type##_grad,                                                 \
-      MKLDNN,                                                          \
-      ::paddle::platform::CPUPlace,                                    \
-      ops::MKLDNNActivationGradKernel<ops::grad_functor<float>>,       \
-      ops::MKLDNNActivationGradKernel<                                 \
-          ops::grad_functor<paddle::platform::bfloat16>>);
-
 REGISTER_FWD_ACTIVATION_MKLDNN_KERNEL(softplus, SoftplusMKLDNNFunctor);
-REGISTER_GRAD_ACTIVATION_MKLDNN_KERNEL(relu6, Relu6MKLDNNGradFunctor);
diff --git a/paddle/fluid/platform/mkldnn_reuse.h b/paddle/fluid/platform/mkldnn_reuse.h
index ca099cb65d67c04188055dfb1805c916932ae58b..8e10429b9dc813c814c4c464c17b2ee9efe0cb94 100644
--- a/paddle/fluid/platform/mkldnn_reuse.h
+++ b/paddle/fluid/platform/mkldnn_reuse.h
@@ -294,103 +294,6 @@ class MatMulV2MKLDNNHandler
   }
 };
 
-template <typename T>
-class ActivationMKLDNNHandler
-    : public MKLDNNHandlerNoCachingT<T,
-                                     dnnl::eltwise_forward,
-                                     dnnl::eltwise_backward> {
- public:
-  ActivationMKLDNNHandler(dnnl::algorithm algorithm,
-                          const framework::ExecutionContext& ctx,
-                          const dnnl::engine engine,
-                          Place cpu_place,
-                          const framework::Tensor* x)
-      : platform::MKLDNNHandlerNoCachingT<T,
-                                          dnnl::eltwise_forward,
-                                          dnnl::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;
-
-    if (ctx.Type() == "scale") {
-      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")
-                  : static_cast<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 if (ctx.Type() == "clip") {
-      alpha = ctx.HasInput("Min") ? ctx.Input<Tensor>("Min")->data<float>()[0]
-                                  : ctx.Attr<float>("min");
-      beta = ctx.HasInput("Max") ? ctx.Input<Tensor>("Max")->data<float>()[0]
-                                 : ctx.Attr<float>("max");
-    } else {
-      // paddle uses beta but mkldnn uses alpha for swish
-      if (algorithm == dnnl::algorithm::eltwise_swish) {
-        std::swap(alpha, beta);
-      } else if (algorithm == dnnl::algorithm::eltwise_bounded_relu) {
-        alpha = ctx.Attr<float>("threshold");
-      }
-    }
-
-    this->AcquireForwardPrimitiveDescriptor(dnnl::prop_kind::forward_training,
-                                            algorithm,
-                                            x->mem_desc(),
-                                            alpha,
-                                            beta);
-  }
-
-  ActivationMKLDNNHandler(dnnl::algorithm algorithm,
-                          const framework::ExecutionContext& ctx,
-                          const dnnl::engine engine,
-                          Place cpu_place,
-                          const framework::Tensor* x,
-                          const Tensor* dout)
-      : platform::MKLDNNHandlerNoCachingT<T,
-                                          dnnl::eltwise_forward,
-                                          dnnl::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 == dnnl::algorithm::eltwise_swish) {
-      std::swap(alpha, beta);
-    } else if (algorithm == dnnl::algorithm::eltwise_bounded_relu) {
-      alpha = ctx.Attr<float>("threshold");
-    }
-
-    if (ctx.Type() == "clip_grad") {
-      alpha = ctx.HasInput("Min") ? ctx.Input<Tensor>("Min")->data<float>()[0]
-                                  : ctx.Attr<float>("min");
-      beta = ctx.HasInput("Max") ? ctx.Input<Tensor>("Max")->data<float>()[0]
-                                 : ctx.Attr<float>("max");
-    }
-
-    this->AcquireForwardPrimitiveDescriptor(dnnl::prop_kind::forward_training,
-                                            algorithm,
-                                            x->mem_desc(),
-                                            alpha,
-                                            beta);
-    this->AcquireBackwardPrimitiveDescriptor(
-        algorithm, dout->mem_desc(), x->mem_desc(), alpha, beta);
-  }
-
-  std::shared_ptr<dnnl::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));
-  }
-};
-
 static std::unordered_map<std::string, std::string> GetAttributeMap(
     std::string act_type) {
   std::unordered_map<std::string, std::string> attr_map;
diff --git a/paddle/phi/kernels/onednn/activation_grad_kernel.cc b/paddle/phi/kernels/onednn/activation_grad_kernel.cc
index a8c988a69d3f94459219a62359786b325bd74a76..9e183abf0287fa595c6905f1a44665eef62ead4a 100644
--- a/paddle/phi/kernels/onednn/activation_grad_kernel.cc
+++ b/paddle/phi/kernels/onednn/activation_grad_kernel.cc
@@ -166,6 +166,11 @@ template <typename T>
 using ReluOneDNNGradFunctor =
     OneDNNActivationGradFunc<T, dnnl::algorithm::eltwise_relu>;
 
+template <typename T>
+using Relu6OneDNNGradUseOutFunctor = OneDNNActivationGradUseOutFunc<
+    T,
+    dnnl::algorithm::eltwise_clip_v2_use_dst_for_bwd>;
+
 template <typename T>
 using SigmoidOneDNNGradUseOutFunctor = OneDNNActivationGradUseOutFunc<
     T,
@@ -241,6 +246,16 @@ void HardSwishGradKernel(const Context& dev_ctx,
   functor(dev_ctx, x, dout, threshold, 0, dx);
 }
 
+template <typename T, typename Context>
+void Relu6GradKernel(const Context& dev_ctx,
+                     const DenseTensor& out,
+                     const DenseTensor& dout,
+                     float threshold,
+                     DenseTensor* dx) {
+  Relu6OneDNNGradUseOutFunctor<T> functor;
+  functor(dev_ctx, out, dout, 0, threshold, dx);
+}
+
 }  // namespace phi
 
 PD_REGISTER_KERNEL(relu_grad,
@@ -261,6 +276,7 @@ PD_REGISTER_ACTIVATION_GRAD_KERNEL(gelu_grad, GeluGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(hard_swish_grad, HardSwishGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(leaky_relu_grad, LeakyReluGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(mish_grad, MishGradKernel)
+PD_REGISTER_ACTIVATION_GRAD_KERNEL(relu6_grad, Relu6GradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(sigmoid_grad, SigmoidGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(sqrt_grad, SqrtGradKernel)
 PD_REGISTER_ACTIVATION_GRAD_KERNEL(swish_grad, SwishGradKernel)
diff --git a/paddle/phi/kernels/onednn/activation_kernel.cc b/paddle/phi/kernels/onednn/activation_kernel.cc
index b90ea1c61b416f49240319505fbabdad564589f5..36ba1be724ccf103ff47f65a8873992289fe9f44 100644
--- a/paddle/phi/kernels/onednn/activation_kernel.cc
+++ b/paddle/phi/kernels/onednn/activation_kernel.cc
@@ -119,7 +119,7 @@ using ReluOneDNNFunctor =
 
 template <typename T>
 using Relu6OneDNNFunctor =
-    OneDNNActivationFunc<T, dnnl::algorithm::eltwise_bounded_relu>;
+    OneDNNActivationFunc<T, dnnl::algorithm::eltwise_clip_v2>;
 
 template <typename T>
 using RoundOneDNNFunctor =
@@ -154,7 +154,6 @@ DEFINE_ONEDNN_ACTIVATION_KERNEL(Round, RoundOneDNNFunctor)
 DEFINE_ONEDNN_ACT_KERNEL_WITH_ONE_ATTRS(Elu, EluOneDNNFunctor, alpha)
 DEFINE_ONEDNN_ACT_KERNEL_WITH_ONE_ATTRS(LeakyRelu, ReluOneDNNFunctor, alpha)
 DEFINE_ONEDNN_ACT_KERNEL_WITH_ONE_ATTRS(Mish, MishOneDNNFunctor, threshold)
-DEFINE_ONEDNN_ACT_KERNEL_WITH_ONE_ATTRS(Relu6, Relu6OneDNNFunctor, threshold)
 DEFINE_ONEDNN_ACT_KERNEL_WITH_ONE_ATTRS(Swish, SwishOneDNNFunctor, beta)
 
 template <typename T, typename Context>
@@ -182,6 +181,15 @@ void GeluKernel(const Context& dev_ctx,
   }
 }
 
+template <typename T, typename Context>
+void Relu6Kernel(const Context& dev_ctx,
+                 const DenseTensor& x,
+                 float threshold,
+                 DenseTensor* out) {
+  Relu6OneDNNFunctor<T> functor;
+  functor(dev_ctx, x, 0, threshold, out);
+}
+
 }  // namespace phi
 
 PD_REGISTER_KERNEL(round, OneDNN, ALL_LAYOUT, phi::RoundKernel, float) {}