diff --git a/paddle/phi/backends/xpu/xpu2_op_list.cc b/paddle/phi/backends/xpu/xpu2_op_list.cc
index 55aae9f24c1a61..993cad302e2f21 100644
--- a/paddle/phi/backends/xpu/xpu2_op_list.cc
+++ b/paddle/phi/backends/xpu/xpu2_op_list.cc
@@ -1178,6 +1178,8 @@ XPUOpMap& get_kl2_ops() {
        XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
       {"fused_attention_grad",
        XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
+      {"fused_bias_act",
+       XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
       {"fused_feedforward",
        XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
       {"fused_feedforward_grad",
diff --git a/paddle/phi/kernels/fusion/xpu/fused_bias_act_kernel.cc b/paddle/phi/kernels/fusion/xpu/fused_bias_act_kernel.cc
new file mode 100644
index 00000000000000..d36d7416a023ae
--- /dev/null
+++ b/paddle/phi/kernels/fusion/xpu/fused_bias_act_kernel.cc
@@ -0,0 +1,138 @@
+// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+//
+// 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 "paddle/phi/backends/xpu/enforce_xpu.h"
+#include "paddle/phi/backends/xpu/xpu_context.h"
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+namespace fusion {
+
+template <typename T>
+static void DispatchComputeImpl(const phi::XPUContext *xpu_ctx,
+                                const DenseTensor &x,
+                                const DenseTensor *bias,
+                                const DenseTensor &dequant_scales,
+                                const DenseTensor &shift,
+                                const DenseTensor &smooth,
+                                const std::string &act_method,
+                                const float quant_scale,
+                                const int quant_round_type,
+                                const float quant_max_bound,
+                                const float quant_min_bound,
+                                DenseTensor *out) {
+  PADDLE_THROW(
+      phi::errors::Unimplemented("fused_bias_act with smooth "
+                                 "quant on xpu is not implemented yet."));
+}
+
+template <typename T>
+static void ComputeImpl(const phi::XPUContext *xpu_ctx,
+                        const DenseTensor &x,
+                        const paddle::optional<DenseTensor> &bias,
+                        const std::string &act_method,
+                        DenseTensor *out) {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+  int rows = x.dims()[0];
+  int cols = x.dims()[1];
+  int r = 0;
+  if (bias) {
+    r = baidu::xpu::api::broadcast_add<XPUType>(
+        xpu_ctx->x_context(),
+        reinterpret_cast<const XPUType *>(x.data<T>()),
+        reinterpret_cast<const XPUType *>(bias.get().data<T>()),
+        reinterpret_cast<XPUType *>(const_cast<T *>(x.data<T>())),
+        {rows, cols},
+        {1, cols});
+    PD_CHECK(r == 0, "baidu::xpu::api::broadcast_add failed.");
+  }
+  if (act_method == "geglu") {
+    PD_THROW(
+        "NOT supported GeGLU. "
+        "Currently Only Support SwiGLU, GeLU, ReLU");
+  } else if (act_method == "swiglu") {
+    r = baidu::xpu::api::swiglu<XPUType>(
+        xpu_ctx->x_context(),
+        reinterpret_cast<const XPUType *>(x.data<T>()),
+        reinterpret_cast<XPUType *>(out->data<T>()),
+        {rows, cols},
+        1,
+        true);
+    PD_CHECK(r == 0, "baidu::xpu::api::swiglu failed.");
+  } else if (act_method == "gelu") {
+    r = baidu::xpu::api::gelu<XPUType>(
+        xpu_ctx->x_context(),
+        reinterpret_cast<const XPUType *>(x.data<T>()),
+        reinterpret_cast<XPUType *>(out->data<T>()),
+        rows * cols);
+    PD_CHECK(r == 0, "baidu::xpu::api::gelu failed.");
+  } else if (act_method == "relu") {
+    r = baidu::xpu::api::relu<XPUType>(
+        xpu_ctx->x_context(),
+        reinterpret_cast<const XPUType *>(x.data<T>()),
+        reinterpret_cast<XPUType *>(out->data<T>()),
+        rows * cols);
+    PD_CHECK(r == 0, "baidu::xpu::api::relu failed.");
+  } else {
+    PD_THROW(
+        "NOT supported. "
+        "Currently Only Support SwiGLU, GeLU, ReLU");
+  }
+}
+
+template <typename T, typename Context>
+void FusedBiasActKernel(const Context &dev_ctx,
+                        const DenseTensor &x,
+                        const paddle::optional<DenseTensor> &bias,
+                        const paddle::optional<DenseTensor> &dequant_scales,
+                        const paddle::optional<DenseTensor> &shift,
+                        const paddle::optional<DenseTensor> &smooth,
+                        const std::string &act_method,
+                        const std::string &compute_dtype,
+                        float quant_scale,
+                        int quant_round_type,
+                        float quant_max_bound,
+                        float quant_min_bound,
+                        DenseTensor *out) {
+  auto xpu_ctx = static_cast<const phi::XPUContext *>(&dev_ctx);
+  dev_ctx.template Alloc<T>(out);
+
+  if (dequant_scales && dequant_scales.get().numel() > 0) {
+    return DispatchComputeImpl<T>(xpu_ctx,
+                                  x,
+                                  bias ? &(bias.get()) : nullptr,
+                                  dequant_scales.get(),
+                                  shift.get(),
+                                  smooth.get(),
+                                  act_method,
+                                  quant_scale,
+                                  quant_round_type,
+                                  quant_max_bound,
+                                  quant_min_bound,
+                                  out);
+  } else {
+    return ComputeImpl<T>(xpu_ctx, x, bias, act_method, out);
+  }
+}
+
+}  // namespace fusion
+}  // namespace phi
+
+PD_REGISTER_KERNEL(fused_bias_act,
+                   XPU,
+                   ALL_LAYOUT,
+                   phi::fusion::FusedBiasActKernel,
+                   float,
+                   phi::dtype::float16) {}