diff --git a/onnxruntime/contrib_ops/rocm/bert/layer_norm.cuh b/onnxruntime/contrib_ops/rocm/bert/layer_norm.cuh
new file mode 100644
index 0000000000..5ecf6815fb
--- /dev/null
+++ b/onnxruntime/contrib_ops/rocm/bert/layer_norm.cuh
@@ -0,0 +1,157 @@
+#include "hip/hip_runtime.h"
+/*
+ The implementation of this file is based on bert plugins in TensorRT demo:
+ https://github.com/NVIDIA/TensorRT/tree/release/5.1/demo/BERT/
+
+Copyright 2019 NVIDIA Corporation
+
+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.
+*/
+
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+#pragma once
+
+#include <hip/hip_fp16.h>
+#include <hipblas.h>
+#include <hipcub/hipcub.hpp>
+#include "core/providers/rocm/rocm_common.h"
+#include "core/providers/rocm/cu_inc/common.cuh"
+#include "core/providers/rocm/shared_inc/rocm_call.h"
+
+using namespace onnxruntime::rocm;
+
+namespace onnxruntime {
+namespace contrib {
+namespace rocm {
+
+template <typename T>
+__device__ inline T Rsqrt(const T& x);
+
+template <>
+__device__ inline float Rsqrt(const float& x) {
+  return rsqrtf(x);
+}
+
+template <>
+__device__ inline half Rsqrt(const half& x) {
+#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__)
+  return hrsqrt(x);
+#else
+  return half(rsqrtf(static_cast<float>(x)));
+#endif
+}
+
+__device__ inline half2 AddHalf2(const half2 a, const half2 b) {
+#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__)
+  return __hadd2(a, b);
+#else
+  return __halves2half2(__hadd(a.x, b.x), __hadd(a.y, b.y));
+#endif
+}
+
+struct KeyValuePairSum {
+  __device__ inline hipcub::KeyValuePair<float, float> operator()(const hipcub::KeyValuePair<float, float>& a,
+                                                                  const hipcub::KeyValuePair<float, float>& b) {
+    return hipcub::KeyValuePair<float, float>(a.key + b.key, a.value + b.value);
+  }
+
+  __device__ inline hipcub::KeyValuePair<half, half> operator()(const hipcub::KeyValuePair<half, half>& a,
+                                                                const hipcub::KeyValuePair<half, half>& b) {
+    const half2 a2 = __halves2half2(a.key, a.value);
+    const half2 b2 = __halves2half2(b.key, b.value);
+    const half2 res = AddHalf2(a2, b2);
+    return hipcub::KeyValuePair<half, half>(__low2half(res), __high2half(res));
+  }
+
+  __device__ inline hipcub::KeyValuePair<half2, half2> operator()(const hipcub::KeyValuePair<half2, half2>& a,
+                                                                  const hipcub::KeyValuePair<half2, half2>& b) {
+    return hipcub::KeyValuePair<half2, half2>(AddHalf2(a.key, b.key), AddHalf2(a.value, b.value));
+  }
+};
+
+template <typename T, int TPB>
+__device__ inline void LayerNorm(
+    const hipcub::KeyValuePair<T, T>& thread_data, const int ld, const int offset, const T* beta,
+    const T* gamma, const T epsilon, T* output) {
+  // Assuming thread_data is already divided by ld
+
+  using BlockReduce = hipcub::BlockReduce<hipcub::KeyValuePair<T, T>, TPB>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  __shared__ T mu;      // mean
+  __shared__ T rsigma;  // 1 / std.dev.
+
+  KeyValuePairSum pair_sum;
+  const auto sum_kv = BlockReduce(temp_storage).Reduce(thread_data, pair_sum);
+
+  if (threadIdx.x == 0) {
+    mu = sum_kv.key;
+    rsigma = Rsqrt(sum_kv.value - mu * mu + epsilon);
+  }
+  __syncthreads();
+
+  for (int i = threadIdx.x; i < ld; i += TPB) {
+    const int idx = offset + i;
+    const T val = output[idx];
+    const T g(gamma[i]);
+    const T b = (nullptr == beta) ? (T)0 : beta[i];
+    output[idx] = g * (val - mu) * rsigma + b;
+  }
+}
+
+template <typename T, int TPB, int ILP>
+__device__ inline void LayerNormSmall(const T* input_v, const hipcub::KeyValuePair<T, T>& thread_data,
+                                      const int ld, const int idx, const T* beta, const T* gamma,
+                                      const T epsilon, T* output) {
+  // Assuming thread_data is already divided by ld
+  // Small settings: the block covers the leading dimension TPB >= ld. The input
+  // value is available in a register
+  using VecT = aligned_vector<T, ILP>;
+  using BlockReduce = hipcub::BlockReduce<hipcub::KeyValuePair<T, T>, TPB>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  __shared__ T mu;      // mean
+  __shared__ T rsigma;  // 1 / std.dev.
+  T beta_v[ILP], gamma_v[ILP], output_v[ILP];
+
+  if (beta != nullptr) {
+    VecT* beta_val = reinterpret_cast<VecT*>(&beta_v);
+    *beta_val = *reinterpret_cast<const VecT*>(&beta[threadIdx.x * ILP]);
+  }
+  VecT* gamma_val = reinterpret_cast<VecT*>(&gamma_v);
+  *gamma_val = *reinterpret_cast<const VecT*>(&gamma[threadIdx.x * ILP]);
+
+  VecT* output_val = reinterpret_cast<VecT*>(&output_v);
+
+  KeyValuePairSum pair_sum;
+  const hipcub::KeyValuePair<T, T> sum_kv = BlockReduce(temp_storage).Reduce(thread_data, pair_sum);
+
+  if (threadIdx.x == 0) {
+    mu = sum_kv.key;
+    rsigma = Rsqrt(sum_kv.value - mu * mu + epsilon);
+  }
+  __syncthreads();
+
+  if (ILP * threadIdx.x < ld) {
+    #pragma unroll
+    for (int i = 0; i < ILP; i++) {
+      output_v[i] = (beta != nullptr) ? gamma_v[i] * (input_v[i] - mu) * rsigma + beta_v[i] :
+                                        gamma_v[i] * (input_v[i] - mu) * rsigma;
+    }
+    *(reinterpret_cast<VecT*>(&output[idx])) = *reinterpret_cast<VecT*>(&output_v[0]);
+  }
+}
+
+}  // namespace rocm
+}  // namespace contrib
+}  // namespace onnxruntime
+
diff --git a/onnxruntime/contrib_ops/rocm/bert/skip_layer_norm.cc b/onnxruntime/contrib_ops/rocm/bert/skip_layer_norm.cc
new file mode 100644
index 0000000000..b71521f0c1
--- /dev/null
+++ b/onnxruntime/contrib_ops/rocm/bert/skip_layer_norm.cc
@@ -0,0 +1,122 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#include "core/providers/rocm/rocm_common.h"
+#include "contrib_ops/rocm/bert/skip_layer_norm.h"
+#include "contrib_ops/rocm/bert/skip_layer_norm_impl.h"
+
+namespace onnxruntime {
+namespace contrib {
+namespace rocm {
+
+#define REGISTER_KERNEL_TYPED(T)                                  \
+  ONNX_OPERATOR_TYPED_KERNEL_EX(                                  \
+      SkipLayerNormalization,                                     \
+      kMSDomain,                                                  \
+      1,                                                          \
+      T,                                                          \
+      kRocmExecutionProvider,                                     \
+      (*KernelDefBuilder::Create())                               \
+          .TypeConstraint("T", DataTypeImpl::GetTensorType<T>()), \
+      SkipLayerNorm<T>);
+
+REGISTER_KERNEL_TYPED(float)
+REGISTER_KERNEL_TYPED(MLFloat16)
+
+using namespace ONNX_NAMESPACE;
+
+template <typename T>
+SkipLayerNorm<T>::SkipLayerNorm(const OpKernelInfo& op_kernel_info) : RocmKernel(op_kernel_info) {
+  ORT_ENFORCE(op_kernel_info.GetAttr<float>("epsilon", &epsilon_).IsOK());
+  ORT_ENFORCE(epsilon_ >= 0);
+}
+
+template <typename T>
+Status SkipLayerNorm<T>::ComputeInternal(OpKernelContext* ctx) const {
+  const Tensor* input = ctx->Input<Tensor>(0);
+  const Tensor* skip = ctx->Input<Tensor>(1);
+  const Tensor* gamma = ctx->Input<Tensor>(2);
+  const Tensor* beta = ctx->Input<Tensor>(3);
+  const Tensor* bias = ctx->Input<Tensor>(4);
+
+  Tensor* output = ctx->Output(0, input->Shape());
+
+  if (input->Shape() != skip->Shape()) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "skip is expected to have same shape as input");
+  }
+
+  if (input->Shape().Size() == 0) {
+    return Status::OK();
+  }
+
+  const auto& input_dims = input->Shape().GetDims();
+  if (input_dims.size() != 3) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "input is expected to have 3 dimensions, got ", input_dims.size());
+  }
+
+  const auto& gamma_dims = gamma->Shape().GetDims();
+  if (gamma_dims.size() != 1) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "gamma is expected to have 1 dimension, got ", gamma_dims.size());
+  }
+  if (gamma_dims[0] != input_dims[2]) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Last dimension of gamma and input does not match");
+  }
+
+  if (nullptr != beta) {
+    const auto& beta_dims = beta->Shape().GetDims();
+    if (beta_dims.size() != 1) {
+      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                             "beta is expected to have 1 dimension, got ", beta_dims.size());
+    }
+    if (beta_dims[0] != input_dims[2]) {
+      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                             "Last dimension of beta and input does not match");
+    }
+  }
+
+  if (nullptr != bias) {
+    const auto& bias_dims = bias->Shape().GetDims();
+    if (bias_dims.size() != 1) {
+      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                             "bias is expected to have 1 dimension, got ", bias_dims.size());
+    }
+    if (bias_dims[0] != input_dims[2]) {
+      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                             "Last dimension of bias and input does not match");
+    }
+  }
+
+  int sequence_length = static_cast<int>(input_dims[1]);
+  int hidden_size = static_cast<int>(input_dims[2]);
+  int64_t element_count = input_dims[0] * sequence_length * hidden_size;
+  size_t element_size = sizeof(T);
+  typedef typename ToHipType<T>::MappedType HipT;
+
+  if (!LaunchSkipLayerNormKernel<HipT>(
+          Stream(),
+          reinterpret_cast<HipT*>(output->template MutableData<T>()),
+          reinterpret_cast<const HipT*>(input->template Data<T>()),
+          reinterpret_cast<const HipT*>(skip->template Data<T>()),
+          reinterpret_cast<const HipT*>(gamma->template Data<T>()),
+          (beta != nullptr) ? reinterpret_cast<const HipT*>(beta->template Data<T>()) : nullptr,
+          (bias != nullptr) ? reinterpret_cast<const HipT*>(bias->template Data<T>()) : nullptr,
+          epsilon_,
+          hidden_size,
+          static_cast<int>(element_count),
+          element_size)) {
+    // Get last error to reset it to hipSuccess.
+    HIP_CALL(hipGetLastError());
+    return Status(common::ONNXRUNTIME, common::FAIL);
+  }
+
+  return Status::OK();
+}
+
+}  // namespace rocm
+}  // namespace contrib
+}  // namespace onnxruntime
+
diff --git a/onnxruntime/contrib_ops/rocm/bert/skip_layer_norm.h b/onnxruntime/contrib_ops/rocm/bert/skip_layer_norm.h
new file mode 100644
index 0000000000..07d7037227
--- /dev/null
+++ b/onnxruntime/contrib_ops/rocm/bert/skip_layer_norm.h
@@ -0,0 +1,26 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#pragma once
+#include "core/common/common.h"
+#include "core/providers/rocm/rocm_kernel.h"
+
+namespace onnxruntime {
+namespace contrib {
+namespace rocm {
+
+using namespace onnxruntime::rocm;
+
+template <typename T>
+class SkipLayerNorm final : public RocmKernel {
+ public:
+  SkipLayerNorm(const OpKernelInfo& op_kernel_info);
+  Status ComputeInternal(OpKernelContext* context) const override;
+
+ private:
+  float epsilon_;
+};
+
+}  // namespace rocm
+}  // namespace contrib
+}  // namespace onnxruntime
diff --git a/onnxruntime/contrib_ops/rocm/bert/skip_layer_norm_impl.cu b/onnxruntime/contrib_ops/rocm/bert/skip_layer_norm_impl.cu
new file mode 100644
index 0000000000..4b99e0e527
--- /dev/null
+++ b/onnxruntime/contrib_ops/rocm/bert/skip_layer_norm_impl.cu
@@ -0,0 +1,197 @@
+#include "hip/hip_runtime.h"
+/*
+ The implementation of this file is based on skipLayerNorm plugin in TensorRT demo:
+ https://github.com/NVIDIA/TensorRT/tree/release/5.1/demo/BERT/
+
+Copyright 2019 NVIDIA Corporation
+
+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.
+*/
+
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+// Modifications: Add SkipLayerNormKernelVec to
+//                leverage vectorized load/write.
+//                and templatize ComputeSkipLayerNorm for different
+//                data types.
+// Copyright (c) Advanced Micro Devices, Inc. All rights reserved.
+// Licensed under the MIT License.
+
+#include <hip/hip_fp16.h>
+#include "contrib_ops/rocm/bert/layer_norm.cuh"
+#include "contrib_ops/rocm/bert/skip_layer_norm_impl.h"
+
+namespace onnxruntime {
+namespace contrib {
+namespace rocm {
+
+template<typename T>
+T maybe2half(float x);
+
+template<>
+float maybe2half(float x) {
+  return x;
+}
+
+template<>
+half maybe2half(float x) {
+  return __float2half_rn(x);
+}
+
+template <typename T, unsigned TPB>
+__global__ void SkipLayerNormKernel(
+    const int ld, const T* input, const T* skip, const T* beta, const T* gamma, const T* bias,
+    const T epsilon, T* output) {
+  const T reverse_ld = T(1.f / ld);
+  const int offset = blockIdx.x * ld;
+
+  KeyValuePairSum pair_sum;
+  // reduce x and x^2
+  hipcub::KeyValuePair<T, T> thread_data(0, 0);
+
+  for (int i = threadIdx.x; i < ld; i += TPB) {
+    const int idx = offset + i;
+    const T val = (bias == nullptr) ? input[idx] + skip[idx] : input[idx] + skip[idx] + bias[i];
+    const T rldval = reverse_ld * val;
+    thread_data = pair_sum(thread_data, hipcub::KeyValuePair<T, T>(rldval, rldval * val));
+    output[idx] = val;
+  }
+
+  LayerNorm<T, TPB>(thread_data, ld, offset, beta, gamma, epsilon, output);
+}
+
+// Vectorized kernel
+template <typename T, unsigned TPB, int ILP>
+__global__ void SkipLayerNormKernelSmall(
+    const int ld, const T* input, const T* skip, const T* beta, const T* gamma,
+    const T* bias, const T epsilon, T* output, bool hasBias) {
+  const T rld = T(1.f / ld);
+  const int idx = blockIdx.x * ld + threadIdx.x * ILP;  // grid_size = n / ld
+
+  using VecT = aligned_vector<T, ILP>;
+  __shared__ T mu;      // mean
+  __shared__ T rsigma;  // 1 / std.dev.
+
+  T input_v[ILP], skip_v[ILP], bias_v[ILP], output_v[ILP];
+
+  VecT* input_val = reinterpret_cast<VecT*>(&input_v);
+  *input_val = *reinterpret_cast<const VecT*>(&input[idx]);
+
+  VecT* skip_val = reinterpret_cast<VecT*>(&skip_v);
+  *skip_val = *reinterpret_cast<const VecT*>(&skip[idx]);
+
+  if (hasBias) {
+    VecT* bias_val = reinterpret_cast<VecT*>(&bias_v);
+    *bias_val = *reinterpret_cast<const VecT*>(&bias[threadIdx.x * ILP]);
+  }
+
+  hipcub::KeyValuePair<T, T> thread_data(T(0.f), T(0.f));
+
+  if (ILP * threadIdx.x < ld) {
+    T rldval_sum = T(0.f);
+    T rldvalsq_sum = T(0.f);
+    #pragma unroll
+    for (int i = 0; i < ILP; i++) {
+      input_v[i] += hasBias ? skip_v[i] + bias_v[i]: skip_v[i];
+      const T rldval = rld * input_v[i];
+      rldval_sum += rldval;
+      rldvalsq_sum += rldval * input_v[i];
+    }
+    thread_data = hipcub::KeyValuePair<T, T>(rldval_sum, rldvalsq_sum);
+  }
+  LayerNormSmall<T, TPB, ILP>(input_v, thread_data, ld, idx, beta, gamma, epsilon, output);
+}
+
+template <typename T>
+bool LaunchSkipLayerNormKernel(
+    hipStream_t stream, T* output, const T* input, const T* skip, const T* gamma,
+    const T* beta, const T* bias, float epsilon, const int ld, const int element_count,
+    size_t element_size) {
+
+  // this must be true because n is the total size of the tensor
+  assert(element_count % ld == 0);
+  bool hasBias = (bias == nullptr) ? false : true;
+  if (0 == (ld % 4)) {
+    const int grid_size = element_count / ld;
+    if (ld <= 32) {
+      constexpr int block_size = 32;
+      hipLaunchKernelGGL(HIP_KERNEL_NAME(SkipLayerNormKernelSmall<T, block_size, 1>), grid_size, block_size,
+          0, stream, ld, input, skip, beta, gamma, bias, maybe2half<T>(epsilon), output, hasBias);
+    } else if (ld <= 64) {
+      constexpr int block_size = 64 / 2;
+      hipLaunchKernelGGL(HIP_KERNEL_NAME(SkipLayerNormKernelSmall<T, block_size, 2>), grid_size, block_size,
+          0, stream, ld, input, skip, beta, gamma, bias, maybe2half<T>(epsilon), output, hasBias);
+    } else if (ld <= 128) {
+      constexpr int block_size = 128 / 4;
+      hipLaunchKernelGGL(HIP_KERNEL_NAME(SkipLayerNormKernelSmall<T, block_size, 4>), grid_size, block_size,
+          0, stream, ld, input, skip, beta, gamma, bias, maybe2half<T>(epsilon), output, hasBias);
+    } else if (ld <= 384) {
+      constexpr int block_size = 384 / 4;
+      hipLaunchKernelGGL(HIP_KERNEL_NAME(SkipLayerNormKernelSmall<T, block_size, 4>), grid_size, block_size,
+          0, stream, ld, input, skip, beta, gamma, bias, maybe2half<T>(epsilon), output, hasBias);
+    } else if (ld <= 768) {
+      constexpr int block_size = 768 / 4;
+      hipLaunchKernelGGL(HIP_KERNEL_NAME(SkipLayerNormKernelSmall<T, block_size, 4>), grid_size, block_size,
+          0, stream, ld, input, skip, beta, gamma, bias, maybe2half<T>(epsilon), output, hasBias);
+    } else if (ld <= 1024) {
+      constexpr int block_size = 1024 / 4;
+      hipLaunchKernelGGL(HIP_KERNEL_NAME(SkipLayerNormKernelSmall<T, block_size, 4>), grid_size, block_size,
+          0, stream, ld, input, skip, beta, gamma, bias, maybe2half<T>(epsilon), output, hasBias);
+    } else {
+      constexpr int block_size = 256;
+      hipLaunchKernelGGL(HIP_KERNEL_NAME(SkipLayerNormKernel<T, block_size>), grid_size, block_size,
+          0, stream, ld, input, skip, beta, gamma, bias, maybe2half<T>(epsilon), output);
+    }
+  } else {
+    const int grid_size = element_count / ld;
+    if (ld <= 32) {
+      constexpr int block_size = 32;
+      hipLaunchKernelGGL(HIP_KERNEL_NAME(SkipLayerNormKernelSmall<T, block_size, 1>), grid_size, block_size,
+          0, stream, ld, input, skip, beta, gamma, bias, maybe2half<T>(epsilon), output, hasBias);
+    } else if (ld <= 64) {
+      constexpr int block_size = 64;
+      hipLaunchKernelGGL(HIP_KERNEL_NAME(SkipLayerNormKernelSmall<T, block_size, 1>), grid_size, block_size,
+          0, stream, ld, input, skip, beta, gamma, bias, maybe2half<T>(epsilon), output, hasBias);
+    } else if (ld <= 128) {
+      constexpr int block_size = 128;
+      hipLaunchKernelGGL(HIP_KERNEL_NAME(SkipLayerNormKernelSmall<T, block_size, 1>), grid_size, block_size,
+          0, stream, ld, input, skip, beta, gamma, bias, maybe2half<T>(epsilon), output, hasBias);
+    } else if (ld == 384) {
+      constexpr int block_size = 384;
+      hipLaunchKernelGGL(HIP_KERNEL_NAME(SkipLayerNormKernelSmall<T, block_size, 1>), grid_size, block_size,
+          0, stream, ld, input, skip, beta, gamma, bias, maybe2half<T>(epsilon), output, hasBias);
+    } else {
+      constexpr int block_size = 256;
+      hipLaunchKernelGGL(HIP_KERNEL_NAME(SkipLayerNormKernel<T, block_size>), grid_size, block_size,
+          0, stream, ld, input, skip, beta, gamma, bias, maybe2half<T>(epsilon), output);
+    }
+  }
+  return HIP_CALL(hipPeekAtLastError());
+}
+
+template bool LaunchSkipLayerNormKernel<float>(hipStream_t stream, float* output, const float* input,
+                                               const float* skip, const float* gamma, const float* beta,
+                                               const float* bias, float epsilon, const int ld,
+                                               const int element_count, size_t element_size);
+
+template bool LaunchSkipLayerNormKernel<half>(hipStream_t stream, half* output, const half* input,
+                                               const half* skip, const half* gamma, const half* beta,
+                                               const half* bias, float epsilon, const int ld,
+                                               const int element_count, size_t element_size);
+
+}  // namespace rocm
+}  // namespace contrib
+}  // namespace onnxruntime
+
+
diff --git a/onnxruntime/contrib_ops/rocm/bert/skip_layer_norm_impl.h b/onnxruntime/contrib_ops/rocm/bert/skip_layer_norm_impl.h
new file mode 100644
index 0000000000..08ab0a5f06
--- /dev/null
+++ b/onnxruntime/contrib_ops/rocm/bert/skip_layer_norm_impl.h
@@ -0,0 +1,28 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#pragma once
+
+namespace onnxruntime {
+namespace contrib {
+namespace rocm {
+
+template <typename T>
+bool LaunchSkipLayerNormKernel(
+    hipStream_t stream,
+    T* output,        // output tensor
+    const T* input,   // input tensor
+    const T* skip,    // skip tensor
+    const T* gamma,   // Layer normalization gamma tensor
+    const T* beta,    // Layer normalization beta tensor
+    const T* bias,    // Layer normalization beta tensor
+    float epsilon,      // Layer normalization epsilon
+    int hidden_size,     // hidden size, it is the leading dimension (ld)
+    int element_count,   // number of elements in input tensor
+    size_t element_size
+);
+
+}  // namespace rocm
+}  // namespace contrib
+}  // namespace onnxruntime
+
diff --git a/tools/ci_build/amd_hipify.py b/tools/ci_build/amd_hipify.py
index bbf0dcc242..650747a155 100644
--- a/tools/ci_build/amd_hipify.py
+++ b/tools/ci_build/amd_hipify.py
@@ -29,7 +29,11 @@ contrib_ops_excluded_files = [
     "bert/fast_gelu_impl.h",
     "bert/fast_gelu.cc",
     "bert/fast_gelu.h",
-    # 'bert/layer_norm.cuh',
+    "bert/layer_norm.cuh",
+    "bert/skip_layer_norm.cc",
+    "bert/skip_layer_norm.h",
+    "bert/skip_layer_norm_impl.cu",
+    "bert/skip_layer_norm_impl.h",
     "bert/longformer_attention.cc",
     "bert/longformer_attention.h",
     "bert/longformer_attention_softmax.cu",