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diff --git a/‎paddle/phi/api/yaml/fused_ops.yaml
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+11Lines changed: 11 additions & 0 deletions b/‎paddle/phi/api/yaml/fused_ops.yaml
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+11Lines changed: 11 additions & 0 deletions
diff --git a/‎paddle/phi/infermeta/multiary.cc
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+130Lines changed: 130 additions & 0 deletions b/‎paddle/phi/infermeta/multiary.cc
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+130Lines changed: 130 additions & 0 deletions
diff --git a/‎paddle/phi/infermeta/multiary.h
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+15Lines changed: 15 additions & 0 deletions b/‎paddle/phi/infermeta/multiary.h
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diff --git a/‎paddle/phi/kernels/funcs/activation_functor.h
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+1-1Lines changed: 1 addition & 1 deletion b/‎paddle/phi/kernels/funcs/activation_functor.h
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+1-1Lines changed: 1 addition & 1 deletion
@@ -63,6 +63,17 @@
     data_type : x
   optional : bias, x_max
 
+- op : fused_bias_act
+  args : (Tensor x, Tensor bias, Tensor dequant_scales, Tensor shift, Tensor smooth, str act_method = "gelu", str compute_dtype = "default", int rows = -1, int cols = -1, float quant_scale = -1, int quant_round_type = 1, float quant_max_bound = 127.0, float quant_min_bound = -127.0)
+  output : Tensor(out)
+  infer_meta :
+    func: FusedBiasActInferMeta
+  kernel :
+    func : fused_bias_act
+    data_type : x
+  optional : bias, dequant_scales, shift, smooth
+  support_dygraph_mode : true
+
 - op : fused_dropout_add
   args : (Tensor x, Tensor y, Tensor seed_tensor, Scalar p, bool is_test, str mode, int seed = 0, bool fix_seed = false)
   optional : seed_tensor
 
@@ -1335,6 +1335,136 @@ void EditDistanceInferMeta(const MetaTensor& hyps,
   sequencenum->set_dtype(DataType::FLOAT32);
 }
 
+void FusedBiasActInferMeta(const MetaTensor& x,
+                           const MetaTensor& bias,
+                           const MetaTensor& dequant_scales,
+                           const MetaTensor& shift,
+                           const MetaTensor& smooth,
+                           const std::string& act_method,
+                           const std::string& compute_dtype,
+                           int rows,
+                           int cols,
+                           float quant_scale,
+                           int quant_round_type,
+                           float quant_max_bound,
+                           float quant_min_bound,
+                           MetaTensor* out) {
+  auto x_dims = x.dims();
+  PADDLE_ENFORCE_EQ(x_dims.size(),
+                    2,
+                    phi::errors::InvalidArgument(
+                        "The size of Input(x) must be 2: %s", x_dims));
+  auto token_num = x_dims[0];
+  auto dim = x_dims[1];
+
+  PADDLE_ENFORCE_GT(
+      rows, 0, phi::errors::InvalidArgument("The size of Attr(rows) must > 0"));
+
+  PADDLE_ENFORCE_GT(
+      cols, 0, phi::errors::InvalidArgument("The size of Attr(cols) must > 0"));
+
+  if (act_method == "geglu" || act_method == "swiglu") {
+    PADDLE_ENFORCE_EQ(
+        dim % 2,
+        0,
+        phi::errors::InvalidArgument(
+            "The seconde dimension of x must be even, but receive %d", dim));
+    dim /= 2;
+    out->set_dims(phi::make_ddim({token_num, dim}));
+  } else if (act_method == "gelu") {
+    out->set_dims(phi::make_ddim({token_num, dim}));
+  } else {
+    PADDLE_THROW(
+        errors::InvalidArgument("act_method must be geglu, swiglu or gelu, "
+                                "but get act_method (%s)",
+                                act_method));
+  }
+
+  auto FBADtypeCheck = [](const MetaTensor& check_tensor,
+                          const std::string& tensor_name,
+                          const std::string& compute_dtype) {
+    if (compute_dtype == "bf16") {
+      PADDLE_ENFORCE_EQ(
+          check_tensor.dtype(),
+          phi::DataType::BFLOAT16,
+          phi::errors::InvalidArgument(
+              "Input(%s) dtype must be the same with Attr(compute_dtype)",
+              tensor_name));
+    } else if (compute_dtype == "fp16") {
+      PADDLE_ENFORCE_EQ(
+          check_tensor.dtype(),
+          phi::DataType::FLOAT16,
+          phi::errors::InvalidArgument(
+              "Input(%s) dtype must be the same with Attr(compute_dtype)",
+              tensor_name));
+    } else if (compute_dtype == "fp32") {
+      PADDLE_ENFORCE_EQ(
+          check_tensor.dtype(),
+          phi::DataType::FLOAT32,
+          phi::errors::InvalidArgument(
+              "Input(%s) dtype must be the same with Attr(compute_dtype)",
+              tensor_name));
+    }
+  };
+
+  // In the case of quantization enabled, the dtype for computation is
+  // determined based on compute_dtype.
+  if (x.dtype() == phi::DataType::INT32) {
+    PADDLE_ENFORCE_NE(
+        compute_dtype,
+        "default",
+        phi::errors::InvalidArgument(
+            "If Input(x) dtype is INT32, Attr(compute_dtype) must be set."));
+
+    if (bias) {
+      FBADtypeCheck(bias, "bias", compute_dtype);
+    }
+
+    if (quant_scale > 0) {
+      out->set_dtype(phi::DataType::INT8);
+    } else {
+      if (compute_dtype == "bf16") {
+        out->set_dtype(phi::DataType::BFLOAT16);
+      } else if (compute_dtype == "fp16") {
+        out->set_dtype(phi::DataType::FLOAT16);
+      } else if (compute_dtype == "fp32") {
+        out->set_dtype(phi::DataType::FLOAT32);
+      } else {
+        PADDLE_THROW(phi::errors::InvalidArgument(
+            "In the case of quantization enabled with Input(x) INT32, "
+            "Attr(compute_dtype) must be set in (bf16, fp16, fp32), "
+            "but get compute_dtype (%s)",
+            compute_dtype));
+      }
+    }
+  } else {
+    // x.dtype() != phi::DataType::INT32
+    if (bias) {
+      if (compute_dtype != "default") {
+        FBADtypeCheck(bias, "bias", compute_dtype);
+        FBADtypeCheck(x, "x", compute_dtype);
+      } else {
+        PADDLE_ENFORCE_EQ(
+            x.dtype(),
+            bias.dtype(),
+            phi::errors::InvalidArgument("Input(x) and Input(bias) must be the "
+                                         "same dtype in this situation"));
+      }
+    } else {
+      // bias not exist
+      if (compute_dtype != "default") {
+        FBADtypeCheck(x, "x", compute_dtype);
+      }
+    }
+    if (quant_scale > 0) {
+      out->set_dtype(phi::DataType::INT8);
+    } else {
+      out->set_dtype(x.dtype());
+    }
+  }
+  out->set_layout(x.layout());
+}
+
 void FusedLinearParamGradAddInferMeta(const MetaTensor& x,
                                       const MetaTensor& dout,
                                       const MetaTensor& dweight,
 
@@ -279,6 +279,21 @@ void EditDistanceInferMeta(const MetaTensor& hyps,
                            MetaTensor* sequencenum,
                            MetaTensor* out);
 
+void FusedBiasActInferMeta(const MetaTensor& x,
+                           const MetaTensor& bias,
+                           const MetaTensor& dequant_scales,
+                           const MetaTensor& shift,
+                           const MetaTensor& smooth,
+                           const std::string& act_method,
+                           const std::string& compute_dtype,
+                           int rows,
+                           int cols,
+                           float quant_scale,
+                           int quant_round_type,
+                           float quant_max_bound,
+                           float quant_min_bound,
+                           MetaTensor* out);
+
 void FusedLinearParamGradAddInferMeta(const MetaTensor& x,
                                       const MetaTensor& dout,
                                       const MetaTensor& dweight,
 
@@ -3923,7 +3923,7 @@ template <typename T>
 struct CudaSwishFunctor : public BaseActivationFunctor<T> {
   using MPType = typename phi::dtype::MPTypeTrait<T>::Type;
   MPType one = static_cast<MPType>(1.0f);
-  float beta;
+  float beta = 1.0;
 
   typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
     return {{"beta", &beta}};