diff --git a/src/layer/riscv/layernorm_riscv.cpp b/src/layer/riscv/layernorm_riscv.cpp
new file mode 100644
index 000000000..de942b19e
--- /dev/null
+++ b/src/layer/riscv/layernorm_riscv.cpp
@@ -0,0 +1,585 @@
+// Xavier Hsinyuan is pleased to support the open source community by making ncnn available.
+//
+// Copyright (C) 2022 Xavier Hsinyuan <me@lstlx.com>. All rights reserved.
+//
+// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
+// in compliance with the License. You may obtain a copy of the License at
+//
+// https://opensource.org/licenses/BSD-3-Clause
+//
+// 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 "layernorm_riscv.h"
+#include <math.h>
+
+#if __riscv_vector
+#include <riscv_vector.h>
+#endif // __riscv_vector
+
+#include "riscv_usability.h"
+
+namespace ncnn {
+
+#if __riscv_vector && __riscv_zfh
+#include "layernorm_rvv_fp16.h"
+#endif
+
+LayerNorm_riscv::LayerNorm_riscv()
+{
+#if __riscv_vector
+    support_packing = true;
+#if __riscv_zfh
+    support_fp16_storage = true;
+#endif
+#endif // __riscv_vector
+}
+
+#if __riscv_vector
+static inline int layernorm_rvv_pack1_procedure(int size, float* ptr, const float* gamma_data, const float* beta_data, float eps, int affine)
+{
+    float sum = 0.f;
+    float sqsum = 0.f;
+    vfloat32m1_t _sum = vfmv_s_f_f32m1(vundefined_f32m1(), 0.f, vsetvlmax_e32m1());
+    vfloat32m1_t _sqsum = vfmv_s_f_f32m1(vundefined_f32m1(), 0.f, vsetvlmax_e32m1());
+    {
+        int n = size;
+        float* ptr_sum = ptr;
+        while (n > 0)
+        {
+            size_t vl = vsetvl_e32m8(n);
+            vfloat32m8_t _p = vle32_v_f32m8(ptr_sum, vl);
+            _sum = vfredusum_vs_f32m8_f32m1(_sum, _p, /* scalar */ _sum, vl);
+            // _sqsum = vfredusum_vs_f32m8_f32m1(_sqsum, vfmul_vv_f32m8(_p, _p, vl), /* scalar */ _sqsum, vl);
+            ptr_sum += vl;
+            n -= vl;
+        }
+    }
+    sum = vfmv_f_s_f32m1_f32(_sum);
+    float mean = sum / size;
+
+    {
+        int n = size;
+        float* ptr_sqsum = ptr;
+        while (n > 0)
+        {
+            size_t vl = vsetvl_e32m8(n);
+            vfloat32m8_t _p = vle32_v_f32m8(ptr_sqsum, vl);
+            _p = vfsub_vf_f32m8(_p, mean, vl);
+            _sqsum = vfredusum_vs_f32m8_f32m1(_sqsum, vfmul_vv_f32m8(_p, _p, vl), /* scalar */ _sqsum, vl);
+            n -= vl;
+            ptr_sqsum += vl;
+        }
+    }
+    sqsum = vfmv_f_s_f32m1_f32(_sqsum);
+    float var = sqsum / size;
+    // the var maybe minus due to accuracy
+    //float var = sqsum / size - mean * mean;
+    float a = static_cast<float>(1.f / (sqrt(var + eps)));
+    float b = -mean * a;
+
+    {
+        int n = size;
+        float* ptr_store = ptr;
+        const float* ptr_gamma = gamma_data;
+        const float* ptr_beta = beta_data;
+        if (affine)
+        {
+            while (n > 0)
+            {
+                size_t vl = vsetvl_e32m8(n);
+                vfloat32m8_t _p = vle32_v_f32m8(ptr_store, vl);
+                _p = vfmul_vf_f32m8(_p, a, vl);
+                vfloat32m8_t _gamma = vle32_v_f32m8(ptr_gamma, vl);
+                _p = vfadd_vf_f32m8(_p, b, vl);
+                vfloat32m8_t _beta = vle32_v_f32m8(ptr_beta, vl);
+                _p = vfmadd_vv_f32m8(_p, _gamma, _beta, vl);
+                vse32_v_f32m8(ptr_store, _p, vl);
+
+                n -= vl;
+                ptr_store += vl;
+                ptr_gamma += vl;
+                ptr_beta += vl;
+            }
+        }
+        else
+        {
+            while (n > 0)
+            {
+                size_t vl = vsetvl_e32m8(n);
+                vfloat32m8_t _p = vle32_v_f32m8(ptr_store, vl);
+                _p = vfmul_vf_f32m8(_p, a, vl);
+                _p = vfadd_vf_f32m8(_p, b, vl);
+                vse32_v_f32m8(ptr_store, _p, vl);
+                n -= vl;
+                ptr_store += vl;
+            }
+        }
+    }
+    return 0;
+}
+
+static inline int layernorm_rvv_packn_procedure(int size, float* ptr, const float* gamma_data, const float* beta_data, float eps, int affine, const size_t vl)
+{
+    // mean and var
+    vfloat32m1_t _sum = vfmv_v_f_f32m1(0.f, vl);
+    vfloat32m1_t _sqsum = vfmv_v_f_f32m1(0.f, vl);
+    for (int i = 0; i < size; i++)
+    {
+        vfloat32m1_t _p = vle32_v_f32m1(ptr + vl * i, vl);
+        _sum = vfadd_vv_f32m1(_p, _sum, vl);
+        // _sqsum = vfmadd_vv_f32m1(_p,_p,_sqsum,vl);
+    }
+    vfloat32m1_t _mean = vfdiv_vf_f32m1(_sum, size, vl);
+    for (int i = 0; i < size; i++)
+    {
+        vfloat32m1_t _p = vle32_v_f32m1(ptr + vl * i, vl);
+        _p = vfsub_vv_f32m1(_p, _mean, vl);
+        _sqsum = vfmacc_vv_f32m1(_sqsum, _p, _p, vl);
+    }
+    vfloat32m1_t _var = vfdiv_vf_f32m1(_sqsum, size, vl);
+
+    // the var maybe minus due to accuracy
+    //float var = sqsum / size - mean * mean;
+    vfloat32m1_t _a = vfrdiv_vf_f32m1(vfsqrt_v_f32m1(vfadd_vf_f32m1(_var, eps, vl), vl), 1.f, vl);
+    // how about vfrsqrt7.v?
+    vfloat32m1_t _b = vfmul_vv_f32m1(vfsgnjn_vv_f32m1(_mean, _mean, vl), _a, vl);
+    if (affine)
+    {
+        for (int i = 0; i < size; i++)
+        {
+            const int offset = vl * i;
+            vfloat32m1_t _p = vle32_v_f32m1(ptr + offset, vl);
+            _p = vfmadd_vv_f32m1(_p, _a, _b, vl);
+            _p = vfmul_vf_f32m1(_p, gamma_data[i], vl);
+            _p = vfadd_vf_f32m1(_p, beta_data[i], vl);
+            vse32_v_f32m1(ptr + offset, _p, vl);
+        }
+    }
+    else
+    {
+        for (int i = 0; i < size; i++)
+        {
+            const int offset = vl * i;
+            vfloat32m1_t _p = vle32_v_f32m1(ptr + offset, vl);
+            _p = vfmadd_vv_f32m1(_p, _a, _b, vl);
+            vse32_v_f32m1(ptr + offset, _p, vl);
+        }
+    }
+
+    return 0;
+}
+#else
+static inline int layernorm_scalar_procedure(int size, float* ptr, const float* gamma_data, const float* beta_data, float eps, int affine)
+{
+    // mean and var
+    float sum = 0.f;
+    float sqsum = 0.f;
+    for (int i = 0; i < size; i++)
+    {
+        sum += ptr[i];
+        //sqsum += ptr[i] * ptr[i];
+    }
+    float mean = sum / size;
+    float tmp = 0.f;
+    for (int i = 0; i < size; i++)
+    {
+        tmp = ptr[i] - mean;
+        sqsum += tmp * tmp;
+    }
+    float var = sqsum / size;
+    // the var maybe minus due to accuracy
+    //float var = sqsum / size - mean * mean;
+
+    float a = static_cast<float>(1.f / (sqrt(var + eps)));
+    float b = -mean * a;
+
+    if (affine)
+    {
+        for (int i = 0; i < size; i++)
+        {
+            ptr[i] = (ptr[i] * a + b) * gamma_data[i] + beta_data[i];
+        }
+    }
+    else
+    {
+        for (int i = 0; i < size; i++)
+        {
+            ptr[i] = ptr[i] * a + b;
+        }
+    }
+    return 0;
+}
+#endif // __riscv_vector
+
+int LayerNorm_riscv::forward_inplace(Mat& bottom_top_blob, const Option& opt) const
+{
+// x = (x - mean) / sqrt(var + eps) * gamma + beta
+#if __riscv_vector
+    int elembits = bottom_top_blob.elembits();
+#if __riscv_zfh
+    if (opt.use_fp16_storage && elembits == 16)
+    {
+        if (opt.use_fp16_arithmetic)
+            return forward_inplace_fp16sa(bottom_top_blob, opt);
+        else
+            return forward_inplace_fp16s(bottom_top_blob, opt);
+    }
+#endif // __riscv_zfh
+
+    int elempack = bottom_top_blob.elempack;
+    const int packn = csrr_vlenb() / 4;
+#endif // __riscv_vector
+    int dims = bottom_top_blob.dims;
+    int w = bottom_top_blob.w;
+
+    if (dims == 1)
+    {
+        float* ptr = bottom_top_blob;
+#if __riscv_vector
+        return layernorm_rvv_pack1_procedure(w * elempack, ptr, gamma_data, beta_data, eps, affine);
+#else
+        return layernorm_scalar_procedure(w, ptr, gamma_data, beta_data, eps, affine);
+#endif // __riscv_vector
+    }
+#if __riscv_vector
+    if (elempack == 1)
+#endif
+    {
+        if (dims == 2)
+        {
+            int w = bottom_top_blob.w;
+            int h = bottom_top_blob.h;
+            // assert affine_size == w
+            #pragma omp parallel for num_threads(opt.num_threads)
+            for (int i = 0; i < h; i++)
+            {
+                float* ptr = bottom_top_blob.row(i);
+#if __riscv_vector
+                layernorm_rvv_pack1_procedure(w, ptr, gamma_data, beta_data, eps, affine);
+#else
+                layernorm_scalar_procedure(w, ptr, gamma_data, beta_data, eps, affine);
+#endif // __riscv_vector
+            }
+        }
+
+        if (dims == 3)
+        {
+            int w = bottom_top_blob.w;
+            int h = bottom_top_blob.h;
+            int channels = bottom_top_blob.c;
+            int size = w * h;
+            if (affine_size == w)
+            {
+                #pragma omp parallel for num_threads(opt.num_threads)
+                for (int q = 0; q < channels; q++)
+                {
+                    for (int i = 0; i < h; i++)
+                    {
+                        float* ptr = bottom_top_blob.channel(q).row(i);
+#if __riscv_vector
+                        layernorm_rvv_pack1_procedure(w, ptr, gamma_data, beta_data, eps, affine);
+#else
+                        layernorm_scalar_procedure(w, ptr, gamma_data, beta_data, eps, affine);
+#endif // __riscv_vector
+                    }
+                }
+            }
+            else // if (affine_size == size)
+            {
+                #pragma omp parallel for num_threads(opt.num_threads)
+                for (int q = 0; q < channels; q++)
+                {
+                    float* ptr = bottom_top_blob.channel(q);
+#if __riscv_vector
+                    layernorm_rvv_pack1_procedure(size, ptr, gamma_data, beta_data, eps, affine);
+#else
+                    layernorm_scalar_procedure(size, ptr, gamma_data, beta_data, eps, affine);
+#endif // __riscv_vector
+                }
+            }
+        }
+    }
+
+#if __riscv_vector
+    if (elempack == packn)
+    {
+        const size_t vl = vsetvl_e32m1(packn);
+        if (dims == 2)
+        {
+            int w = bottom_top_blob.w;
+            int h = bottom_top_blob.h;
+            // assert affine_size == w
+
+            #pragma omp parallel for num_threads(opt.num_threads)
+            for (int i = 0; i < h; i++)
+            {
+                float* ptr = bottom_top_blob.row(i);
+                layernorm_rvv_packn_procedure(w, ptr, gamma_data, beta_data, eps, affine, vl);
+            }
+        }
+        if (dims == 3)
+        {
+            int w = bottom_top_blob.w;
+            int h = bottom_top_blob.h;
+            int channels = bottom_top_blob.c;
+            int size = w * h;
+
+            if (affine_size == w)
+            {
+                #pragma omp parallel for num_threads(opt.num_threads)
+                for (int q = 0; q < channels; q++)
+                {
+                    for (int i = 0; i < h; i++)
+                    {
+                        float* ptr = bottom_top_blob.channel(q).row(i);
+
+                        layernorm_rvv_packn_procedure(w, ptr, gamma_data, beta_data, eps, affine, vl);
+                    }
+                }
+            }
+            else // if (affine_size == size)
+            {
+                #pragma omp parallel for num_threads(opt.num_threads)
+                for (int q = 0; q < channels; q++)
+                {
+                    float* ptr = bottom_top_blob.channel(q);
+                    layernorm_rvv_packn_procedure(size, ptr, gamma_data, beta_data, eps, affine, vl);
+                }
+            }
+        }
+    }
+#endif // __riscv_vector
+    return 0;
+}
+
+#if __riscv_vector && __riscv_zfh
+int LayerNorm_riscv::forward_inplace_fp16s(Mat& bottom_top_blob, const Option& opt) const
+{
+    // x = (x - mean) / sqrt(var + eps) * gamma + beta
+    int elempack = bottom_top_blob.elempack;
+    int dims = bottom_top_blob.dims;
+    int w = bottom_top_blob.w;
+
+    if (dims == 1)
+    {
+        __fp16* ptr = bottom_top_blob;
+
+        return layernorm_rvv_pack1_fp16s_procedure(w * elempack, ptr, gamma_data, beta_data, eps, affine);
+    }
+    if (elempack == 1)
+    {
+        if (dims == 2)
+        {
+            int w = bottom_top_blob.w;
+            int h = bottom_top_blob.h;
+            // assert affine_size == w
+            #pragma omp parallel for num_threads(opt.num_threads)
+            for (int i = 0; i < h; i++)
+            {
+                __fp16* ptr = bottom_top_blob.row<__fp16>(i);
+                layernorm_rvv_pack1_fp16s_procedure(w, ptr, gamma_data, beta_data, eps, affine);
+            }
+        }
+
+        if (dims == 3)
+        {
+            int w = bottom_top_blob.w;
+            int h = bottom_top_blob.h;
+            int channels = bottom_top_blob.c;
+            int size = w * h;
+            if (affine_size == w)
+            {
+                #pragma omp parallel for num_threads(opt.num_threads)
+                for (int q = 0; q < channels; q++)
+                {
+                    for (int i = 0; i < h; i++)
+                    {
+                        __fp16* ptr = bottom_top_blob.channel(q).row<__fp16>(i);
+
+                        layernorm_rvv_pack1_fp16s_procedure(w, ptr, gamma_data, beta_data, eps, affine);
+                    }
+                }
+            }
+            else // if (affine_size == size)
+            {
+                #pragma omp parallel for num_threads(opt.num_threads)
+                for (int q = 0; q < channels; q++)
+                {
+                    __fp16* ptr = bottom_top_blob.channel(q);
+                    layernorm_rvv_pack1_fp16s_procedure(size, ptr, gamma_data, beta_data, eps, affine);
+                }
+            }
+        }
+
+        return 0;
+    }
+
+    const int packn = csrr_vlenb() / 2; // fp16
+    if (elempack == packn)
+    {
+        const size_t vl = vsetvl_e16m1(packn);
+        if (dims == 2)
+        {
+            int w = bottom_top_blob.w;
+            int h = bottom_top_blob.h;
+            // assert affine_size == w
+
+            #pragma omp parallel for num_threads(opt.num_threads)
+            for (int i = 0; i < h; i++)
+            {
+                __fp16* ptr = bottom_top_blob.row<__fp16>(i);
+                layernorm_rvv_packn_fp16s_procedure(w, ptr, gamma_data, beta_data, eps, affine, vl);
+            }
+        }
+        if (dims == 3)
+        {
+            int w = bottom_top_blob.w;
+            int h = bottom_top_blob.h;
+            int channels = bottom_top_blob.c;
+            int size = w * h;
+
+            if (affine_size == w)
+            {
+                #pragma omp parallel for num_threads(opt.num_threads)
+                for (int q = 0; q < channels; q++)
+                {
+                    for (int i = 0; i < h; i++)
+                    {
+                        __fp16* ptr = bottom_top_blob.channel(q).row<__fp16>(i);
+
+                        layernorm_rvv_packn_fp16s_procedure(w, ptr, gamma_data, beta_data, eps, affine, vl);
+                    }
+                }
+            }
+            else // if (affine_size == size)
+            {
+                #pragma omp parallel for num_threads(opt.num_threads)
+                for (int q = 0; q < channels; q++)
+                {
+                    __fp16* ptr = bottom_top_blob.channel(q);
+                    layernorm_rvv_packn_fp16s_procedure(size, ptr, gamma_data, beta_data, eps, affine, vl);
+                }
+            }
+        }
+    }
+
+    return 0;
+}
+
+int LayerNorm_riscv::forward_inplace_fp16sa(Mat& bottom_top_blob, const Option& opt) const
+{
+    // x = (x - mean) / sqrt(var + eps) * gamma + beta
+    int elempack = bottom_top_blob.elempack;
+    int dims = bottom_top_blob.dims;
+    int w = bottom_top_blob.w;
+
+    if (dims == 1)
+    {
+        __fp16* ptr = bottom_top_blob;
+
+        return layernorm_rvv_pack1_fp16sa_procedure(w * elempack, ptr, gamma_data, beta_data, eps, affine);
+    }
+    if (elempack == 1)
+    {
+        if (dims == 2)
+        {
+            int w = bottom_top_blob.w;
+            int h = bottom_top_blob.h;
+            // assert affine_size == w
+            #pragma omp parallel for num_threads(opt.num_threads)
+            for (int i = 0; i < h; i++)
+            {
+                __fp16* ptr = bottom_top_blob.row<__fp16>(i);
+                layernorm_rvv_pack1_fp16sa_procedure(w, ptr, gamma_data, beta_data, eps, affine);
+            }
+        }
+
+        if (dims == 3)
+        {
+            int w = bottom_top_blob.w;
+            int h = bottom_top_blob.h;
+            int channels = bottom_top_blob.c;
+            int size = w * h;
+            if (affine_size == w)
+            {
+                #pragma omp parallel for num_threads(opt.num_threads)
+                for (int q = 0; q < channels; q++)
+                {
+                    for (int i = 0; i < h; i++)
+                    {
+                        __fp16* ptr = bottom_top_blob.channel(q).row<__fp16>(i);
+
+                        layernorm_rvv_pack1_fp16sa_procedure(w, ptr, gamma_data, beta_data, eps, affine);
+                    }
+                }
+            }
+            else // if (affine_size == size)
+            {
+                #pragma omp parallel for num_threads(opt.num_threads)
+                for (int q = 0; q < channels; q++)
+                {
+                    __fp16* ptr = bottom_top_blob.channel(q);
+                    layernorm_rvv_pack1_fp16sa_procedure(size, ptr, gamma_data, beta_data, eps, affine);
+                }
+            }
+        }
+
+        return 0;
+    }
+
+    const int packn = csrr_vlenb() / 2; // fp16
+    if (elempack == packn)
+    {
+        const size_t vl = vsetvl_e16m1(packn);
+        if (dims == 2)
+        {
+            int w = bottom_top_blob.w;
+            int h = bottom_top_blob.h;
+            // assert affine_size == w
+
+            #pragma omp parallel for num_threads(opt.num_threads)
+            for (int i = 0; i < h; i++)
+            {
+                __fp16* ptr = bottom_top_blob.row<__fp16>(i);
+                layernorm_rvv_packn_fp16sa_procedure(w, ptr, gamma_data, beta_data, eps, affine, vl);
+            }
+        }
+        if (dims == 3)
+        {
+            int w = bottom_top_blob.w;
+            int h = bottom_top_blob.h;
+            int channels = bottom_top_blob.c;
+            int size = w * h;
+
+            if (affine_size == w)
+            {
+                #pragma omp parallel for num_threads(opt.num_threads)
+                for (int q = 0; q < channels; q++)
+                {
+                    for (int i = 0; i < h; i++)
+                    {
+                        __fp16* ptr = bottom_top_blob.channel(q).row<__fp16>(i);
+
+                        layernorm_rvv_packn_fp16sa_procedure(w, ptr, gamma_data, beta_data, eps, affine, vl);
+                    }
+                }
+            }
+            else // if (affine_size == size)
+            {
+                #pragma omp parallel for num_threads(opt.num_threads)
+                for (int q = 0; q < channels; q++)
+                {
+                    __fp16* ptr = bottom_top_blob.channel(q);
+                    layernorm_rvv_packn_fp16sa_procedure(size, ptr, gamma_data, beta_data, eps, affine, vl);
+                }
+            }
+        }
+    }
+
+    return 0;
+}
+#endif
+
+} // namespace ncnn
\ No newline at end of file
diff --git a/src/layer/riscv/layernorm_riscv.h b/src/layer/riscv/layernorm_riscv.h
new file mode 100644
index 000000000..6d2a1748f
--- /dev/null
+++ b/src/layer/riscv/layernorm_riscv.h
@@ -0,0 +1,37 @@
+// Xavier Hsinyuan is pleased to support the open source community by making ncnn available.
+//
+// Copyright (C) 2022 Xavier Hsinyuan <me@lstlx.com>. All rights reserved.
+//
+// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
+// in compliance with the License. You may obtain a copy of the License at
+//
+// https://opensource.org/licenses/BSD-3-Clause
+//
+// 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.
+
+#ifndef LAYER_LAYERNORM_RISCV_H
+#define LAYER_LAYERNORM_RISCV_H
+
+#include "layernorm.h"
+
+namespace ncnn {
+class LayerNorm_riscv : virtual public LayerNorm
+{
+public:
+    LayerNorm_riscv();
+
+    virtual int forward_inplace(Mat& bottom_top_blob, const Option& opt) const;
+
+protected:
+#if __riscv_vector && __riscv_zfh
+    int forward_inplace_fp16s(Mat& bottom_top_blob, const Option& opt) const;
+    int forward_inplace_fp16sa(Mat& bottom_top_blob, const Option& opt) const;
+#endif
+};
+
+} // namespace ncnn
+
+#endif // LAYER_LAYERNORM_RISCV_H
diff --git a/src/layer/riscv/layernorm_rvv_fp16.h b/src/layer/riscv/layernorm_rvv_fp16.h
new file mode 100644
index 000000000..cf22025ac
--- /dev/null
+++ b/src/layer/riscv/layernorm_rvv_fp16.h
@@ -0,0 +1,284 @@
+// Xavier Hsinyuan is pleased to support the open source community by making ncnn available.
+//
+// Copyright (C) 2022 Xavier Hsinyuan <me@lstlx.com>. All rights reserved.
+//
+// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
+// in compliance with the License. You may obtain a copy of the License at
+//
+// https://opensource.org/licenses/BSD-3-Clause
+//
+// 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.
+
+// fp16s
+static inline int layernorm_rvv_pack1_fp16s_procedure(int size, __fp16* ptr, const float* gamma_data, const float* beta_data, float eps, int affine)
+{
+    float sum = 0.f;
+    float sqsum = 0.f;
+    vfloat32m1_t _sum = vfmv_s_f_f32m1(vundefined_f32m1(), 0.f, vsetvlmax_e32m1());
+    vfloat32m1_t _sqsum = vfmv_s_f_f32m1(vundefined_f32m1(), 0.f, vsetvlmax_e32m1());
+    {
+        int n = size;
+        __fp16* ptr_sum = ptr;
+        while (n > 0)
+        {
+            size_t vl = vsetvl_e16m4(n);
+            vfloat32m8_t _p = vfwcvt_f_f_v_f32m8(vle16_v_f16m4(ptr_sum, vl), vl);
+            _sum = vfredusum_vs_f32m8_f32m1(_sum, _p, /* scalar */ _sum, vl);
+            // _sqsum = vfredusum_vs_f32m8_f32m1(_sqsum, vfmul_vv_f32m8(_p, _p, vl), /* scalar */ _sqsum, vl);
+            ptr_sum += vl;
+            n -= vl;
+        }
+    }
+    sum = vfmv_f_s_f32m1_f32(_sum);
+    float mean = sum / size;
+
+    {
+        int n = size;
+        __fp16* ptr_sqsum = ptr;
+        while (n > 0)
+        {
+            size_t vl = vsetvl_e16m4(n);
+            vfloat32m8_t _p = vfwcvt_f_f_v_f32m8(vle16_v_f16m4(ptr_sqsum, vl), vl);
+            _p = vfsub_vf_f32m8(_p, mean, vl);
+            _sqsum = vfredusum_vs_f32m8_f32m1(_sqsum, vfmul_vv_f32m8(_p, _p, vl), /* scalar */ _sqsum, vl);
+            n -= vl;
+            ptr_sqsum += vl;
+        }
+    }
+    sqsum = vfmv_f_s_f32m1_f32(_sqsum);
+    float var = sqsum / size;
+    // the var maybe minus due to accuracy
+    //float var = sqsum / size - mean * mean;
+    float a = static_cast<float>(1.f / (sqrt(var + eps)));
+    float b = -mean * a;
+
+    {
+        int n = size;
+        __fp16* ptr_store = ptr;
+        const float* ptr_gamma = gamma_data;
+        const float* ptr_beta = beta_data;
+        if (affine)
+        {
+            while (n > 0)
+            {
+                size_t vl = vsetvl_e16m4(n);
+                vfloat32m8_t _p = vfwcvt_f_f_v_f32m8(vle16_v_f16m4(ptr_store, vl), vl);
+                _p = vfmul_vf_f32m8(_p, a, vl);
+                vfloat32m8_t _gamma = vle32_v_f32m8(ptr_gamma, vl);
+                _p = vfadd_vf_f32m8(_p, b, vl);
+                vfloat32m8_t _beta = vle32_v_f32m8(ptr_beta, vl);
+                _p = vfmadd_vv_f32m8(_p, _gamma, _beta, vl);
+                vse16_v_f16m4(ptr_store, vfncvt_f_f_w_f16m4(_p, vl), vl);
+
+                n -= vl;
+                ptr_store += vl;
+                ptr_gamma += vl;
+                ptr_beta += vl;
+            }
+        }
+        else
+        {
+            while (n > 0)
+            {
+                size_t vl = vsetvl_e16m4(n);
+                vfloat32m8_t _p = vfwcvt_f_f_v_f32m8(vle16_v_f16m4(ptr_store, vl), vl);
+                _p = vfmul_vf_f32m8(_p, a, vl);
+                _p = vfadd_vf_f32m8(_p, b, vl);
+                vse16_v_f16m4(ptr_store, vfncvt_f_f_w_f16m4(_p, vl), vl);
+                n -= vl;
+                ptr_store += vl;
+            }
+        }
+    }
+    return 0;
+}
+
+static inline int layernorm_rvv_packn_fp16s_procedure(int size, __fp16* ptr, const float* gamma_data, const float* beta_data, float eps, int affine, const size_t vl)
+{
+    // mean and var
+    // f16m1 => f32m2
+    vfloat32m2_t _sum = vfmv_v_f_f32m2(0.f, vl);
+    vfloat32m2_t _sqsum = vfmv_v_f_f32m2(0.f, vl);
+    for (int i = 0; i < size; i++)
+    {
+        vfloat32m2_t _p = vfwcvt_f_f_v_f32m2(vle16_v_f16m1(ptr + vl * i, vl), vl);
+        _sum = vfadd_vv_f32m2(_p, _sum, vl);
+    }
+    vfloat32m2_t _mean = vfdiv_vf_f32m2(_sum, (float)size, vl);
+    for (int i = 0; i < size; i++)
+    {
+        vfloat32m2_t _p = vfwcvt_f_f_v_f32m2(vle16_v_f16m1(ptr + vl * i, vl), vl);
+        _p = vfsub_vv_f32m2(_p, _mean, vl);
+        _sqsum = vfmacc_vv_f32m2(_sqsum, _p, _p, vl);
+    }
+    vfloat32m2_t _var = vfdiv_vf_f32m2(_sqsum, (float)size, vl);
+
+    // the var maybe minus due to accuracy
+    //float var = sqsum / size - mean * mean;
+    vfloat32m2_t _a = vfrdiv_vf_f32m2(vfsqrt_v_f32m2(vfadd_vf_f32m2(_var, eps, vl), vl), 1.f, vl);
+    // how about vfrsqrt7.v?
+    vfloat32m2_t _b = vfmul_vv_f32m2(vfsgnjn_vv_f32m2(_mean, _mean, vl), _a, vl);
+    if (affine)
+    {
+        for (int i = 0; i < size; i++)
+        {
+            const int offset = vl * i;
+            vfloat32m2_t _p = vfwcvt_f_f_v_f32m2(vle16_v_f16m1(ptr + offset, vl), vl);
+            _p = vfmadd_vv_f32m2(_p, _a, _b, vl);
+            _p = vfmul_vf_f32m2(_p, gamma_data[i], vl);
+            _p = vfadd_vf_f32m2(_p, beta_data[i], vl);
+            vse16_v_f16m1(ptr + offset, vfncvt_f_f_w_f16m1(_p, vl), vl);
+        }
+    }
+    else
+    {
+        for (int i = 0; i < size; i++)
+        {
+            const int offset = vl * i;
+            vfloat32m2_t _p = vfwcvt_f_f_v_f32m2(vle16_v_f16m1(ptr + offset, vl), vl);
+            _p = vfmadd_vv_f32m2(_p, _a, _b, vl);
+            vse16_v_f16m1(ptr + offset, vfncvt_f_f_w_f16m1(_p, vl), vl);
+        }
+    }
+
+    return 0;
+}
+
+// fp16sa
+
+static inline int layernorm_rvv_pack1_fp16sa_procedure(int size, __fp16* ptr, const float* gamma_data, const float* beta_data, float eps, int affine)
+{
+    __fp16 sum = 0.f;
+    __fp16 sqsum = 0.f;
+    vfloat16m1_t _sum = vfmv_s_f_f16m1(vundefined_f16m1(), 0.f, vsetvlmax_e32m1());
+    vfloat16m1_t _sqsum = vfmv_s_f_f16m1(vundefined_f16m1(), 0.f, vsetvlmax_e32m1());
+    {
+        int n = size;
+        __fp16* ptr_sum = ptr;
+        while (n > 0)
+        {
+            size_t vl = vsetvl_e16m8(n);
+            vfloat16m8_t _p = vle16_v_f16m8(ptr_sum, vl);
+            _sum = vfredusum_vs_f16m8_f16m1(_sum, _p, /* scalar */ _sum, vl);
+            // _sqsum = vfredusum_vs_f32m8_f32m1(_sqsum, vfmul_vv_f32m8(_p, _p, vl), /* scalar */ _sqsum, vl);
+            ptr_sum += vl;
+            n -= vl;
+        }
+    }
+    sum = vfmv_f_s_f16m1_f16(_sum);
+    __fp16 mean = sum / size;
+
+    {
+        int n = size;
+        __fp16* ptr_sqsum = ptr;
+        while (n > 0)
+        {
+            size_t vl = vsetvl_e16m8(n);
+            vfloat16m8_t _p = vle16_v_f16m8(ptr_sqsum, vl);
+            _p = vfsub_vf_f16m8(_p, mean, vl);
+            _sqsum = vfredusum_vs_f16m8_f16m1(_sqsum, vfmul_vv_f16m8(_p, _p, vl), /* scalar */ _sqsum, vl);
+            n -= vl;
+            ptr_sqsum += vl;
+        }
+    }
+    sqsum = vfmv_f_s_f16m1_f16(_sqsum);
+    __fp16 var = sqsum / size;
+    // the var maybe minus due to accuracy
+    //float var = sqsum / size - mean * mean;
+    __fp16 a = static_cast<__fp16>(1.f / (sqrt(var + eps)));
+    __fp16 b = static_cast<__fp16>(-mean * a);
+
+    {
+        int n = size;
+        __fp16* ptr_store = ptr;
+        const float* ptr_gamma = gamma_data;
+        const float* ptr_beta = beta_data;
+        if (affine)
+        {
+            while (n > 0)
+            {
+                size_t vl = vsetvl_e16m4(n);
+                vfloat16m4_t _p = vle16_v_f16m4(ptr_store, vl);
+                _p = vfmul_vf_f16m4(_p, a, vl);
+                vfloat16m4_t _gamma = vfncvt_f_f_w_f16m4(vle32_v_f32m8(ptr_gamma, vl), vl);
+                _p = vfadd_vf_f16m4(_p, b, vl);
+                vfloat16m4_t _beta = vfncvt_f_f_w_f16m4(vle32_v_f32m8(ptr_beta, vl), vl);
+                _p = vfmadd_vv_f16m4(_p, _gamma, _beta, vl);
+                vse16_v_f16m4(ptr_store, _p, vl);
+
+                n -= vl;
+                ptr_store += vl;
+                ptr_gamma += vl;
+                ptr_beta += vl;
+            }
+        }
+        else
+        {
+            while (n > 0)
+            {
+                size_t vl = vsetvl_e16m8(n);
+                vfloat16m8_t _p = vle16_v_f16m8(ptr_store, vl);
+                _p = vfmul_vf_f16m8(_p, a, vl);
+                _p = vfadd_vf_f16m8(_p, b, vl);
+                vse16_v_f16m8(ptr_store, _p, vl);
+                n -= vl;
+                ptr_store += vl;
+            }
+        }
+    }
+    return 0;
+}
+
+static inline int layernorm_rvv_packn_fp16sa_procedure(int size, __fp16* ptr, const float* gamma_data, const float* beta_data, float eps, int affine, const size_t vl)
+{
+    // mean and var
+    vfloat16m1_t _sum = vfmv_v_f_f16m1(0.f, vl);
+    vfloat16m1_t _sqsum = vfmv_v_f_f16m1(0.f, vl);
+    for (int i = 0; i < size; i++)
+    {
+        vfloat16m1_t _p = vle16_v_f16m1(ptr + vl * i, vl);
+        _sum = vfadd_vv_f16m1(_p, _sum, vl);
+        // _sqsum = vfmadd_vv_f16m1(_p,_p,_sqsum,vl);
+    }
+    vfloat16m1_t _mean = vfdiv_vf_f16m1(_sum, size, vl);
+    for (int i = 0; i < size; i++)
+    {
+        vfloat16m1_t _p = vle16_v_f16m1(ptr + vl * i, vl);
+        _p = vfsub_vv_f16m1(_p, _mean, vl);
+        _sqsum = vfmacc_vv_f16m1(_sqsum, _p, _p, vl);
+    }
+    vfloat16m1_t _var = vfdiv_vf_f16m1(_sqsum, size, vl);
+
+    // the var maybe minus due to accuracy
+    //float var = sqsum / size - mean * mean;
+    vfloat16m1_t _a = vfrdiv_vf_f16m1(vfsqrt_v_f16m1(vfadd_vf_f16m1(_var, eps, vl), vl), 1.f, vl);
+    // how about vfrsqrt7.v?
+    vfloat16m1_t _b = vfmul_vv_f16m1(vfsgnjn_vv_f16m1(_mean, _mean, vl), _a, vl);
+    if (affine)
+    {
+        for (int i = 0; i < size; i++)
+        {
+            const int offset = vl * i;
+            vfloat16m1_t _p = vle16_v_f16m1(ptr + offset, vl);
+            _p = vfmadd_vv_f16m1(_p, _a, _b, vl);
+            _p = vfmul_vf_f16m1(_p, gamma_data[i], vl);
+            _p = vfadd_vf_f16m1(_p, beta_data[i], vl);
+            vse16_v_f16m1(ptr + offset, _p, vl);
+        }
+    }
+    else
+    {
+        for (int i = 0; i < size; i++)
+        {
+            const int offset = vl * i;
+            vfloat16m1_t _p = vle16_v_f16m1(ptr + offset, vl);
+            _p = vfmadd_vv_f16m1(_p, _a, _b, vl);
+            vse16_v_f16m1(ptr + offset, _p, vl);
+        }
+    }
+
+    return 0;
+}