[Cherry-pick] Layer norm fp16 and Nvidia optimize (#29169 #29434 #29522 #29576) (#30110)

* Layer norm fp16 (#29169)

* add fp16 for layer_norm op

* revert layernorm api

* fix forward

* fix forward

* fix backward for layernorm with fp16

* fix unit test for layernorm with fp16

* fix with_mkldnn compile error for layernorm with fp16

* 1. revert to PADDLE_ENFORCE_NOT_NULL, 2. change static_cast<float> to static_cast<U>

* fix with_mkldnn compile error for layernorm with fp16

* fix with_mkldnn compile error for layernorm with fp16
Co-authored-by: Nzhiqiu <chenqiuliang@baidu.com>

* fix layer_norm accuracy (#29434)

* Layernorm opt (#29522)

* layernorm fw opt

* layernorm bw opt

* fix typo, test=develop

* remove const dim3 for windows CI compatibility

* merge develop
Co-authored-by: Nzlsh80826 <zlsh80826@gmail.com>

* Fix compile problem when cuda_arch < 6000 (#29576)

* fix compile problem when cuda_arch < 6000

* refine code

* refine code
Co-authored-by: Nzhiqiu <chenqiuliang@baidu.com>
Co-authored-by: Nzlsh80826 <zlsh80826@gmail.com>

paddle/fluid/operators/layer_norm_op.cc

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/layer_norm_op.h"
 #include <memory>
+#include <string>
 
 #ifdef PADDLE_WITH_MKLDNN
 #include "paddle/fluid/platform/mkldnn_helper.h"
@@ -98,7 +99,26 @@ class LayerNormOp : public framework::OperatorWithKernel {
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext &ctx) const {
+      const framework::ExecutionContext &ctx) const override {
+    auto input_data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
+    // By default, the type of the scale, bias, mean,
+    // and var tensors should both be float. (For float or float16 input tensor)
+    // or double (For double input tensor).
+    auto ln_param_type = framework::proto::VarType::FP32;
+    if (input_data_type == framework::proto::VarType::FP64) {
+      ln_param_type = framework::proto::VarType::FP64;
+    }
+    if (ctx.HasInput("Scale")) {
+      PADDLE_ENFORCE_EQ(ln_param_type, ctx.Input<Tensor>("Scale")->type(),
+                        platform::errors::InvalidArgument(
+                            "Scale input should be of float type"));
+    }
+    if (ctx.HasInput("Bias")) {
+      PADDLE_ENFORCE_EQ(ln_param_type, ctx.Input<Tensor>("Bias")->type(),
+                        platform::errors::InvalidArgument(
+                            "Bias input should be of float type"));
+    }
+
     framework::LibraryType library = framework::LibraryType::kPlain;
     framework::DataLayout layout = framework::DataLayout::kAnyLayout;
 
@@ -110,9 +130,8 @@ class LayerNormOp : public framework::OperatorWithKernel {
     }
 #endif
 
-    return framework::OpKernelType(
-        OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace(),
-        layout, library);
+    return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout,
+                                   library);
   }
 };
 
@@ -224,7 +243,13 @@ class LayerNormGradOp : public framework::OperatorWithKernel {
     }
     PADDLE_ENFORCE_NOT_NULL(
         t, platform::errors::NotFound("Y@GRAD of LayerNorm Op is not found."));
-    return framework::OpKernelType(t->type(), ctx.GetPlace());
+
+    framework::LibraryType library = framework::LibraryType::kPlain;
+    framework::DataLayout layout = framework::DataLayout::kAnyLayout;
+
+    return framework::OpKernelType(
+        OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace(),
+        layout, library);
   }
 };
 

paddle/fluid/operators/layer_norm_op.cu

@@ -15,12 +15,22 @@ limitations under the License. */
 #include <cub/cub.cuh>
 #include <memory>
 #include <vector>
+
 #include "paddle/fluid/framework/ddim.h"
 #include "paddle/fluid/operators/layer_norm_op.h"
+#include "paddle/fluid/platform/cudnn_helper.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace paddle {
 namespace operators {
 
+using Tensor = framework::Tensor;
+using DataLayout = framework::DataLayout;
+template <typename T>
+using CudnnDataType = platform::CudnnDataType<T>;
+template <typename T>
+using LayerNormParamType = typename CudnnDataType<T>::BatchNormParamType;
+
 inline static int GetDesiredBlockDim(int block_dim) {
   const int kMaxBlockDim = 512;
   return block_dim >= kMaxBlockDim
@@ -97,59 +107,350 @@ struct PairForLayerNormAddFunctor {
   }
 };
 
-template <typename T, int BlockDim>
-__global__ void LayerNormForward(const T *x, const T *scale, const T *bias,
-                                 T *y, T *mean, T *var, float epsilon,
+template <typename T>
+__inline__ __device__ T rsqrt(const T val) {
+  return static_cast<T>(1) / sqrt(val);
+}
+
+template <>
+__inline__ __device__ float rsqrt(const float val) {
+  return rsqrtf(val);
+}
+
+template <>
+__inline__ __device__ double rsqrt(const double val) {
+  return rsqrt(val);
+}
+
+#if CUDA_ARCH_FP16_SUPPORTED(__CUDA_ARCH__)
+template <>
+__inline__ __device__ half rsqrt(const half val) {
+  return hrsqrt(val);
+}
+#endif
+
+template <typename T, typename U, int BlockDim>
+__global__ void LayerNormForward(const T *x, const U *scale, const U *bias,
+                                 T *y, U *mean, U *var, float epsilon,
                                  int feature_size) {
-  using BlockReduce = cub::BlockReduce<PairForLayerNorm<double>, BlockDim>;
+  using BlockReduce = cub::BlockReduce<PairForLayerNorm<U>, BlockDim>;
   __shared__ typename BlockReduce::TempStorage temp_storage;
+  __shared__ U mean_share;
+  __shared__ U var_share;
 
   int beg_idx = blockIdx.x * feature_size + threadIdx.x;
   int end_idx = (blockIdx.x + 1) * feature_size;
 
   // Step 1: Reduce to calculate mean and var
-  double mean_val = 0;
-  double var_val = 0;
+  U mean_val = 0;
+  U var_val = 0;
   for (int i = beg_idx; i < end_idx; i += BlockDim) {
-    T tmp = x[i];
+    U tmp = static_cast<U>(x[i]);
     mean_val += tmp;
     var_val += (tmp * tmp);
   }
   auto pair = BlockReduce(temp_storage)
-                  .Reduce(PairForLayerNorm<double>(mean_val, var_val),
-                          PairForLayerNormAddFunctor<double>());
+                  .Reduce(PairForLayerNorm<U>(mean_val, var_val),
+                          PairForLayerNormAddFunctor<U>());
   if (threadIdx.x == 0) {
     auto tmp = pair.first_ / feature_size;
-    mean[blockIdx.x] = static_cast<T>(tmp);
-    var[blockIdx.x] = static_cast<T>(pair.second_ / feature_size - tmp * tmp);
+    mean[blockIdx.x] = mean_share = static_cast<U>(tmp);
+    var[blockIdx.x] = var_share =
+        static_cast<U>(pair.second_ / feature_size - tmp * tmp);
   }
   __syncthreads();
-  mean_val = mean[blockIdx.x];
-  var_val = static_cast<T>(real_sqrt(var[blockIdx.x] + epsilon));
+
+  mean_val = mean_share;
+  U invvar = rsqrt<U>(var_share + static_cast<U>(epsilon));
 
   // Step 2: Calculate y
   if (scale != nullptr) {
     if (bias != nullptr) {
       for (int i = beg_idx, j = threadIdx.x; i < end_idx;
            i += BlockDim, j += BlockDim) {
-        y[i] = scale[j] * (x[i] - mean_val) / var_val + bias[j];
+        y[i] = static_cast<T>(
+            scale[j] * (static_cast<U>(x[i]) - mean_val) * invvar + bias[j]);
       }
     } else {
       for (int i = beg_idx, j = threadIdx.x; i < end_idx;
            i += BlockDim, j += BlockDim) {
-        y[i] = scale[j] * (x[i] - mean_val) / var_val;
+        y[i] = static_cast<T>(scale[j] * (static_cast<U>(x[i]) - mean_val) *
+                              invvar);
       }
     }
   } else {  // scale == nullptr
     if (bias != nullptr) {
       for (int i = beg_idx, j = threadIdx.x; i < end_idx;
            i += BlockDim, j += BlockDim) {
-        y[i] = (x[i] - mean_val) / var_val + bias[j];
+        y[i] = static_cast<T>((static_cast<U>(x[i]) - mean_val) * invvar +
+                              bias[j]);
       }
     } else {
       for (int i = beg_idx, j = threadIdx.x; i < end_idx;
            i += BlockDim, j += BlockDim) {
-        y[i] = (x[i] - mean_val) / var_val;
+        y[i] = static_cast<T>((static_cast<U>(x[i]) - mean_val) * invvar);
+      }
+    }
+  }
+}
+
+template <typename T, typename U, int VPT>
+__inline__ __device__ void cuLoadAddStridedInputs(
+    const int i1_block, const int thr_load_row_off, const int thr_load_col_off,
+    const int i2_off, const int row_stride, U *warp_buf1, U *warp_buf2,
+    const T *input, const T *dout, const int i1_end, const int n2,
+    const U *__restrict__ mean, const U *__restrict__ var,
+    const float epsilon) {
+  const int i1 = i1_block + thr_load_row_off;
+  if (i1 >= i1_end) return;
+  U curr_mean = mean[i1];
+  U curr_invvar = rsqrt<U>(var[i1] + epsilon);
+  for (int k = 0; k < VPT; ++k) {
+    const int i2 = i2_off + k;
+    const int load_idx = i1 * n2 + i2;
+    const int write_idx = thr_load_row_off * row_stride + thr_load_col_off + k;
+    if (i2 < n2) {
+      U curr_input = static_cast<U>(input[load_idx]);
+      U curr_dout = static_cast<U>(dout[load_idx]);
+      warp_buf1[write_idx] += curr_dout;
+      warp_buf2[write_idx] +=
+          curr_dout * (curr_input - curr_mean) * curr_invvar;
+    }
+  }
+}
+
+template <typename T, typename U, int BDIMX, int BDIMY, int VPTX>
+__global__ void LayerNormBackwardPartGradGammaBeta(
+    const T *__restrict__ dout, const T *__restrict__ input, const int n1,
+    const int n2, const U *__restrict__ mean, const U *__restrict__ var,
+    float epsilon, U *part_grad_gamma, U *part_grad_beta) {
+  // VPTX -> value per thread.x, BDIMX -> blockDim.x, BDIMY -> blockDim.y, BDIMX
+  // -> blockDim.x
+  // template for compile time optimizations
+
+  constexpr int row_stride = BDIMX + 1;
+  const int thr_load_col_off = (threadIdx.x * VPTX) & (BDIMX - 1);
+  const int thr_load_row_off =
+      (threadIdx.x * VPTX) / BDIMX + threadIdx.y * BDIMY;
+  const int i2_off = blockIdx.x * BDIMX + thr_load_col_off;
+
+  constexpr int shared_cap = (BDIMX * BDIMY > 2 * VPTX * BDIMY * row_stride)
+                                 ? BDIMX * BDIMY
+                                 : 2 * VPTX * BDIMY * row_stride;
+  __shared__ U buf[shared_cap];
+
+  U *warp_buf1 = reinterpret_cast<U *>(buf);
+  U *warp_buf2 = warp_buf1 + VPTX * BDIMY * row_stride;
+
+  for (int idx = threadIdx.y * blockDim.x + threadIdx.x;
+       idx < 2 * VPTX * BDIMY * row_stride; idx += BDIMX * BDIMY) {
+    buf[idx] = U(0);
+  }
+  __syncthreads();
+
+  for (int i1_block = blockIdx.y * BDIMY * VPTX; i1_block < n1;
+       i1_block += VPTX * BDIMY * gridDim.y) {
+    cuLoadAddStridedInputs<T, U, VPTX>(
+        i1_block, thr_load_row_off, thr_load_col_off, i2_off, row_stride,
+        warp_buf1, warp_buf2, input, dout, n1, n2, mean, var, epsilon);
+  }
+  __syncthreads();
+
+  // inter-warp reductions
+  // sum within each warp
+  U acc1 = U(0);
+  U acc2 = U(0);
+  for (int k = 0; k < VPTX; ++k) {
+    int row1 = threadIdx.y + k * VPTX;
+    int idx1 = row1 * row_stride + threadIdx.x;
+    acc1 += warp_buf1[idx1];
+    acc2 += warp_buf2[idx1];
+  }
+  warp_buf1[threadIdx.y * row_stride + threadIdx.x] = acc1;
+  warp_buf2[threadIdx.y * row_stride + threadIdx.x] = acc2;
+  __syncthreads();
+  // sum all warps
+  for (int offset = VPTX >> 1; offset > 1; offset >>= 1) {
+    if (threadIdx.y < offset) {
+      int row1 = threadIdx.y;
+      int row2 = threadIdx.y + offset;
+      int idx1 = row1 * row_stride + threadIdx.x;
+      int idx2 = row2 * row_stride + threadIdx.x;
+      warp_buf1[idx1] += warp_buf1[idx2];
+      warp_buf2[idx1] += warp_buf2[idx2];
+    }
+    __syncthreads();
+  }
+  int i2 = blockIdx.x * blockDim.x + threadIdx.x;
+  if (threadIdx.y == 0 && i2 < n2) {
+    int row1 = threadIdx.y;
+    int row2 = threadIdx.y + 1;
+    int idx1 = row1 * row_stride + threadIdx.x;
+    int idx2 = row2 * row_stride + threadIdx.x;
+    part_grad_beta[blockIdx.y * n2 + i2] = warp_buf1[idx1] + warp_buf1[idx2];
+    part_grad_gamma[blockIdx.y * n2 + i2] = warp_buf2[idx1] + warp_buf2[idx2];
+  }
+}
+
+template <typename T, typename U, int BDIMX, int BDIMY>
+__global__ void LayerNormBackwardSumGradGammaBeta(
+    const U *part_grad_gamma, const U *part_grad_beta, const int part_size,
+    // const int n1, const int n2, T* grad_gamma, T* grad_beta) {
+    const int n1, const int n2, U *grad_gamma, U *grad_beta) {
+  // sum partial gradients for gamma and beta
+  __shared__ U buf[BDIMX * BDIMY];
+  int i2 = blockIdx.x * BDIMX + threadIdx.x;
+  if (i2 < n2) {
+    // each warp does sequential reductions until reduced part_size is num_warps
+    int num_warp_reductions = part_size / BDIMY;
+    U sum_gamma = U(0);
+    U sum_beta = U(0);
+    const U *part_grad_gamma_ptr =
+        part_grad_gamma + threadIdx.y * num_warp_reductions * n2 + i2;
+    const U *part_grad_beta_ptr =
+        part_grad_beta + threadIdx.y * num_warp_reductions * n2 + i2;
+    for (int warp_offset = 0; warp_offset < num_warp_reductions;
+         ++warp_offset) {
+      sum_gamma += part_grad_gamma_ptr[warp_offset * n2];
+      sum_beta += part_grad_beta_ptr[warp_offset * n2];
+    }
+    // inter-warp reductions
+    constexpr int nbsize3 = BDIMX * BDIMY / 2;
+    for (int offset = BDIMY / 2; offset >= 1; offset /= 2) {
+      // top half write to shared memory
+      if (threadIdx.y >= offset && threadIdx.y < 2 * offset) {
+        const int write_idx = (threadIdx.y - offset) * blockDim.x + threadIdx.x;
+        buf[write_idx] = sum_gamma;
+        buf[write_idx + nbsize3] = sum_beta;
+      }
+      __syncthreads();
+      // bottom half sums
+      if (threadIdx.y < offset) {
+        const int read_idx = threadIdx.y * BDIMX + threadIdx.x;
+        sum_gamma += buf[read_idx];
+        sum_beta += buf[read_idx + nbsize3];
+      }
+      __syncthreads();
+    }
+    // write out fully summed gradients
+    if (threadIdx.y == 0) {
+      grad_gamma[i2] = sum_gamma;
+      grad_beta[i2] = sum_beta;
+    }
+  }
+}
+
+template <typename T, typename U, int BDIMX, int BDIMY>
+__global__ void LayerNormBackwardComputeGradInput(
+    const T *__restrict__ dout, const T *__restrict__ input, const int n1,
+    const int n2,
+    // const U* __restrict__ mean, const U* __restrict__ var, const float
+    // epsilon, const T* gamma,
+    const U *__restrict__ mean, const U *__restrict__ var, const float epsilon,
+    const U *gamma, T *grad_input) {
+  for (auto i1 = blockIdx.y; i1 < n1; i1 += gridDim.y) {
+    U sum_loss1 = U(0);
+    U sum_loss2 = U(0);
+    const U c_mean = mean[i1];
+    const U c_invvar = rsqrt<U>(var[i1] + epsilon);
+    const T *k_input = input + i1 * n2;
+    const T *k_dout = dout + i1 * n2;
+    constexpr int numx = BDIMX * BDIMY;
+    const int thrx = threadIdx.x + threadIdx.y * BDIMX;
+    if (gamma != NULL) {
+      int l = 4 * thrx;
+      for (; l + 3 < n2; l += 4 * numx) {
+        for (int k = 0; k < 4; ++k) {
+          const U c_h = static_cast<U>(k_input[l + k]);
+          const U c_loss = static_cast<U>(k_dout[l + k]);
+          sum_loss1 += c_loss * gamma[l + k];
+          sum_loss2 += c_loss * gamma[l + k] * (c_h - c_mean) * c_invvar;
+        }
+      }
+      for (; l < n2; ++l) {
+        const U c_h = static_cast<U>(k_input[l]);
+        const U c_loss = static_cast<U>(k_dout[l]);
+        sum_loss1 += c_loss * gamma[l];
+        sum_loss2 += c_loss * gamma[l] * (c_h - c_mean) * c_invvar;
+      }
+    } else {
+      int l = 4 * thrx;
+      for (; l + 3 < n2; l += 4 * numx) {
+        for (int k = 0; k < 4; ++k) {
+          const U c_h = static_cast<U>(k_input[l + k]);
+          const U c_loss = static_cast<U>(k_dout[l + k]);
+          sum_loss1 += c_loss;
+          sum_loss2 += c_loss * (c_h - c_mean) * c_invvar;
+        }
+      }
+      for (; l < n2; ++l) {
+        const U c_h = static_cast<U>(k_input[l]);
+        const U c_loss = static_cast<U>(k_dout[l]);
+        sum_loss1 += c_loss;
+        sum_loss2 += c_loss * (c_h - c_mean) * c_invvar;
+      }
+    }
+    // intra-warp reductions
+    for (int mask = BDIMX / 2; mask > 0; mask /= 2) {
+      sum_loss1 +=
+          __shfl_xor_sync(0xffffffff, sum_loss1, mask,
+                          warpSize);  // WARP_SHFL_XOR(sum_loss1, mask);
+      sum_loss2 +=
+          __shfl_xor_sync(0xffffffff, sum_loss2, mask,
+                          warpSize);  // WARP_SHFL_XOR(sum_loss2, mask);
+    }
+    // inter-warp reductions
+    if (BDIMY > 1) {
+      __shared__ U buf[BDIMX * BDIMY];
+      for (int offset = BDIMY / 2; offset > 0; offset /= 2) {
+        // upper half of warps write to shared
+        if (threadIdx.y >= offset && threadIdx.y < 2 * offset) {
+          const int wrt_i = (threadIdx.y - offset) * BDIMX + threadIdx.x;
+          buf[2 * wrt_i] = sum_loss1;
+          buf[2 * wrt_i + 1] = sum_loss2;
+        }
+        __syncthreads();
+        // lower half merges
+        if (threadIdx.y < offset) {
+          const int read_i = threadIdx.y * blockDim.x + threadIdx.x;
+          sum_loss1 += buf[2 * read_i];
+          sum_loss2 += buf[2 * read_i + 1];
+        }
+        __syncthreads();
+      }
+      if (threadIdx.y == 0) {
+        buf[2 * threadIdx.x] = sum_loss1;
+        buf[2 * threadIdx.x + 1] = sum_loss2;
+      }
+      __syncthreads();
+      if (threadIdx.y != 0) {
+        sum_loss1 = buf[2 * threadIdx.x];
+        sum_loss2 = buf[2 * threadIdx.x + 1];
+      }
+    }
+    // all threads now have the two sums over l
+    U fH = (U)n2;
+    U term1 = (U(1) / fH) * c_invvar;
+    T *k_grad_input = grad_input + i1 * n2;
+    if (gamma != NULL) {
+      for (int l = thrx; l < n2; l += numx) {
+        const U c_h = static_cast<U>(k_input[l]);
+        const U c_loss = static_cast<U>(k_dout[l]);
+        U f_grad_input = fH * c_loss * gamma[l];
+        f_grad_input -= sum_loss1;
+        f_grad_input -= (c_h - c_mean) * c_invvar * sum_loss2;
+        f_grad_input *= term1;
+        k_grad_input[l] = static_cast<T>(f_grad_input);
+      }
+    } else {
+      for (int l = thrx; l < n2; l += numx) {
+        const U c_h = static_cast<U>(k_input[l]);
+        const U c_loss = static_cast<U>(k_dout[l]);
+        U f_grad_input = fH * c_loss;
+        f_grad_input -= sum_loss1;
+        f_grad_input -= (c_h - c_mean) * c_invvar * sum_loss2;
+        f_grad_input *= term1;
+        k_grad_input[l] = static_cast<T>(f_grad_input);
       }
     }
   }
@@ -157,35 +458,37 @@ __global__ void LayerNormForward(const T *x, const T *scale, const T *bias,
 
 // Make sure that d_scale != nullptr && d_bias != nullptr
 // Since d_scale != nullptr, scale would not be nullptr
-template <typename T, int BlockDim, bool HasDx>
+template <typename T, typename U, int BlockDim, bool HasDx>
 __global__ void LayerNormBackwardGradientAll(const T *x, const T *d_y,
-                                             T *d_scale, T *d_bias, T *d_x,
-                                             const T *mean, const T *var,
-                                             const T *scale, float epsilon,
+                                             U *d_scale, U *d_bias, T *d_x,
+                                             const U *mean, const U *var,
+                                             const U *scale, float epsilon,
                                              int batch_size, int feature_size,
                                              int col_offset) {
-  using BlockReduce = cub::BlockReduce<PairForLayerNorm<T>, BlockDim>;
+  using BlockReduce = cub::BlockReduce<PairForLayerNorm<U>, BlockDim>;
   __shared__ typename BlockReduce::TempStorage temp_storage;
 
   int beg_idx = threadIdx.x * feature_size + (blockIdx.x + col_offset);
   int end_idx = batch_size * feature_size + (blockIdx.x + col_offset);
   int stride = BlockDim * feature_size;
 
-  T d_scale_partial = 0, d_bias_partial = 0;
+  U d_scale_partial = static_cast<U>(0), d_bias_partial = static_cast<U>(0);
 
   for (int i = beg_idx; i < end_idx; i += stride) {
     int row_idx = i / feature_size;
-    auto var_val = static_cast<T>(real_sqrt(var[row_idx] + epsilon));
-    d_scale_partial += d_y[i] * (x[i] - mean[row_idx]) / var_val;
-    d_bias_partial += d_y[i];
+    auto var_val = real_sqrt(static_cast<U>(var[row_idx]) + epsilon);
+    d_scale_partial += static_cast<U>(d_y[i]) *
+                       (static_cast<U>(x[i]) - mean[row_idx]) / var_val;
+    d_bias_partial += static_cast<U>(d_y[i]);
     if (HasDx) {
-      d_x[i] = d_y[i] * scale[blockIdx.x + col_offset] / var_val;
+      d_x[i] = static_cast<T>(static_cast<U>(d_y[i]) *
+                              scale[blockIdx.x + col_offset] / var_val);
     }
   }
 
   auto pair = BlockReduce(temp_storage)
-                  .Reduce(PairForLayerNorm<T>(d_scale_partial, d_bias_partial),
-                          PairForLayerNormAddFunctor<T>());
+                  .Reduce(PairForLayerNorm<U>(d_scale_partial, d_bias_partial),
+                          PairForLayerNormAddFunctor<U>());
 
   if (threadIdx.x == 0) {
     d_scale[blockIdx.x + col_offset] = pair.first_;
@@ -196,32 +499,36 @@ __global__ void LayerNormBackwardGradientAll(const T *x, const T *d_y,
 // Make sure that there is only one true expression: d_scale != nullptr
 // or d_bias != nullptr
 // Notice: scale may be nullptr
-template <typename T, int BlockDim, bool HasDx, bool HasDScale>
+template <typename T, typename U, int BlockDim, bool HasDx, bool HasDScale>
 __global__ void LayerNormBackwardGradientScaleOrBias(
-    const T *x, const T *d_y, T *d_scale, T *d_bias, T *d_x, const T *mean,
-    const T *var, const T *scale, float epsilon, int batch_size,
+    const T *x, const T *d_y, U *d_scale, U *d_bias, T *d_x, const U *mean,
+    const U *var, const U *scale, float epsilon, int batch_size,
     int feature_size, int col_offset) {
-  using BlockReduce = cub::BlockReduce<T, BlockDim>;
+  using BlockReduce = cub::BlockReduce<U, BlockDim>;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   int beg_idx = threadIdx.x * feature_size + blockIdx.x + col_offset;
   int end_idx = batch_size * feature_size + blockIdx.x + col_offset;
   int stride = BlockDim * feature_size;
-  T d_scale_or_d_bias_partial = 0;
+  U d_scale_or_d_bias_partial = static_cast<U>(0);
 
   for (int i = beg_idx; i < end_idx; i += stride) {
     int row_idx = i / feature_size;
-    auto var_val = static_cast<T>(real_sqrt(var[row_idx] + epsilon));
+    auto var_val =
+        static_cast<U>(real_sqrt(static_cast<float>(var[row_idx]) + epsilon));
     if (HasDScale) {
-      d_scale_or_d_bias_partial += d_y[i] * (x[i] - mean[row_idx]) / var_val;
+      d_scale_or_d_bias_partial += static_cast<U>(d_y[i]) *
+                                   (static_cast<U>(x[i]) - mean[row_idx]) /
+                                   var_val;
     } else {  // d_bias != nullptr
-      d_scale_or_d_bias_partial += d_y[i];
+      d_scale_or_d_bias_partial += static_cast<U>(d_y[i]);
     }
 
     if (HasDx) {
       if (scale != nullptr) {
-        d_x[i] = d_y[i] * scale[blockIdx.x + col_offset] / var_val;
+        d_x[i] = static_cast<T>(static_cast<U>(d_y[i]) *
+                                scale[blockIdx.x + col_offset] / var_val);
       } else {
-        d_x[i] = d_y[i] / var_val;
+        d_x[i] = static_cast<T>(static_cast<U>(d_y[i]) / var_val);
       }
     }
   }
@@ -238,121 +545,138 @@ __global__ void LayerNormBackwardGradientScaleOrBias(
   }
 }
 
-template <typename T, int BlockDim>
+template <typename T, typename U, int BlockDim>
 __global__ void LayerNormBackwardPostProcessToCalculateDX(const T *x, T *d_x,
-                                                          const T *mean,
-                                                          const T *var,
+                                                          const U *mean,
+                                                          const U *var,
                                                           float epsilon,
                                                           int feature_size) {
-  using BlockReduce = cub::BlockReduce<PairForLayerNorm<T>, BlockDim>;
+  using BlockReduce = cub::BlockReduce<PairForLayerNorm<U>, BlockDim>;
   __shared__ typename BlockReduce::TempStorage temp_storage;
-  __shared__ T d_x_reduce_tmp[2];
+  __shared__ U d_x_reduce_tmp[2];
 
   int beg_idx = blockIdx.x * feature_size + threadIdx.x;
   int end_idx = (blockIdx.x + 1) * feature_size;
 
-  T block_mean = mean[blockIdx.x];
-  T block_var = var[blockIdx.x];
-  T d_x_mean_partial = 0, d_x_var_partial = 0;
+  U block_mean = mean[blockIdx.x];
+  U block_var = var[blockIdx.x];
+  U d_x_mean_partial = static_cast<U>(0), d_x_var_partial = static_cast<U>(0);
   for (int i = beg_idx; i < end_idx; i += BlockDim) {
-    d_x_mean_partial += d_x[i];
-    d_x_var_partial += d_x[i] * (x[i] - block_mean);
+    d_x_mean_partial += static_cast<U>(d_x[i]);
+    d_x_var_partial +=
+        static_cast<U>(d_x[i]) * (static_cast<U>(x[i]) - block_mean);
   }
 
   auto pair =
       BlockReduce(temp_storage)
-          .Reduce(PairForLayerNorm<T>(d_x_mean_partial, d_x_var_partial),
-                  PairForLayerNormAddFunctor<T>());
+          .Reduce(PairForLayerNorm<U>(d_x_mean_partial, d_x_var_partial),
+                  PairForLayerNormAddFunctor<U>());
 
   if (threadIdx.x == 0) {
-    d_x_reduce_tmp[0] = pair.first_ / feature_size;
-    d_x_reduce_tmp[1] = pair.second_ / (feature_size * (block_var + epsilon));
+    d_x_reduce_tmp[0] = static_cast<float>(pair.first_) / feature_size;
+    d_x_reduce_tmp[1] =
+        static_cast<float>(pair.second_) /
+        (feature_size * (static_cast<float>(block_var) + epsilon));
   }
   __syncthreads();
 
   d_x_mean_partial = d_x_reduce_tmp[0];
   d_x_var_partial = d_x_reduce_tmp[1];
   for (int i = beg_idx; i < end_idx; i += BlockDim) {
-    d_x[i] -= d_x_mean_partial;
-    d_x[i] -= (x[i] - block_mean) * d_x_var_partial;
+    d_x[i] -= static_cast<T>(d_x_mean_partial);
+    d_x[i] -=
+        static_cast<T>((static_cast<U>(x[i]) - block_mean) * d_x_var_partial);
   }
 }
 
 // Here, we only calculate d_x
-template <typename T, int BlockDim>
+template <typename T, typename U, int BlockDim>
 __global__ void LayerNormBackwardGradientOnlyDX(const T *x, const T *d_y,
-                                                T *d_x, const T *mean,
-                                                const T *var, const T *scale,
+                                                T *d_x, const U *mean,
+                                                const U *var, const U *scale,
                                                 float epsilon,
                                                 int feature_size) {
-  using BlockReduce = cub::BlockReduce<PairForLayerNorm<T>, BlockDim>;
+  using BlockReduce = cub::BlockReduce<PairForLayerNorm<U>, BlockDim>;
   __shared__ typename BlockReduce::TempStorage temp_storage;
-  __shared__ T d_x_reduce_tmp[2];
+  __shared__ U d_x_reduce_tmp[2];
 
   int beg_idx = blockIdx.x * feature_size + threadIdx.x;
   int end_idx = (blockIdx.x + 1) * feature_size;
 
-  T block_mean = mean[blockIdx.x], block_var = var[blockIdx.x];
-  T d_x_mean_partial = 0, d_x_var_partial = 0;
+  U block_mean = mean[blockIdx.x], block_var = var[blockIdx.x];
+  U d_x_mean_partial = static_cast<U>(0), d_x_var_partial = static_cast<U>(0);
   for (int i = beg_idx; i < end_idx; i += BlockDim) {
-    auto var_val = static_cast<T>(real_sqrt(block_var + epsilon));
+    auto var_val =
+        static_cast<U>(real_sqrt(static_cast<float>(block_var) + epsilon));
     if (scale != nullptr) {
       int col_idx = i % feature_size;
-      d_x[i] = d_y[i] * scale[col_idx] / var_val;
+      d_x[i] =
+          static_cast<T>(static_cast<U>(d_y[i]) * scale[col_idx] / var_val);
     } else {
-      d_x[i] = d_y[i] / var_val;
+      d_x[i] = static_cast<T>(static_cast<U>(d_y[i]) / var_val);
     }
-    d_x_mean_partial += d_x[i];
-    d_x_var_partial += d_x[i] * (x[i] - block_mean);
+    d_x_mean_partial += static_cast<U>(d_x[i]);
+    d_x_var_partial +=
+        static_cast<U>(d_x[i]) * (static_cast<U>(x[i]) - block_mean);
   }
 
   auto pair =
       BlockReduce(temp_storage)
-          .Reduce(PairForLayerNorm<T>(d_x_mean_partial, d_x_var_partial),
-                  PairForLayerNormAddFunctor<T>());
+          .Reduce(PairForLayerNorm<U>(d_x_mean_partial, d_x_var_partial),
+                  PairForLayerNormAddFunctor<U>());
 
   if (threadIdx.x == 0) {
-    d_x_reduce_tmp[0] = pair.first_ / feature_size;
-    d_x_reduce_tmp[1] = pair.second_ / (feature_size * (block_var + epsilon));
+    d_x_reduce_tmp[0] = static_cast<float>(pair.first_) / feature_size;
+    d_x_reduce_tmp[1] =
+        static_cast<float>(pair.second_) /
+        (feature_size * (static_cast<float>(block_var) + epsilon));
   }
   __syncthreads();
 
   d_x_mean_partial = d_x_reduce_tmp[0];
   d_x_var_partial = d_x_reduce_tmp[1];
   for (int i = beg_idx; i < end_idx; i += BlockDim) {
-    d_x[i] -= d_x_mean_partial;
-    d_x[i] -= (x[i] - block_mean) * d_x_var_partial;
+    d_x[i] -= static_cast<T>(d_x_mean_partial);
+    d_x[i] -=
+        static_cast<T>((static_cast<U>(x[i]) - block_mean) * d_x_var_partial);
   }
 }
 
-template <typename T>
+template <typename T, typename U>
 __global__ void LayerNormBackwardWhenBatchSizeIsOne(
-    const T *x, const T *d_y, T *d_x, T *d_scale, T *d_bias, const T *mean,
-    const T *var, const T *scale, float epsilon, int feature_size) {
+    const T *x, const T *d_y, T *d_x, U *d_scale, U *d_bias, const U *mean,
+    const U *var, const U *scale, float epsilon, int feature_size) {
   int idx = threadIdx.x + blockIdx.x * blockDim.x;
   if (idx < feature_size) {
-    auto var_val = static_cast<T>(real_sqrt(var[idx] + epsilon));
+    auto var_val =
+        static_cast<U>(real_sqrt(static_cast<float>(var[idx]) + epsilon));
     if (d_x != nullptr) {
       if (d_scale == nullptr) {
-        d_x[idx] = d_y[idx] / var_val;
+        d_x[idx] = static_cast<T>(static_cast<U>(d_y[idx]) / var_val);
       } else {
-        d_x[idx] = d_y[idx] * scale[idx] / var_val;
+        d_x[idx] =
+            static_cast<T>(static_cast<U>(d_y[idx]) * scale[idx] / var_val);
       }
     }
 
     if (d_scale != nullptr) {
-      d_scale[idx] = d_y[idx] * (x[idx] - mean[idx]) / var_val;
+      d_scale[idx] = static_cast<U>(d_y[idx]) *
+                     (static_cast<U>(x[idx]) - mean[idx]) / var_val;
     }
 
-    if (d_bias != nullptr) d_bias[idx] = d_y[idx];
+    if (d_bias != nullptr) d_bias[idx] = static_cast<U>(d_y[idx]);
   }
 }
 
-template <typename T>
-static void LayerNormBackward(const T *x, const T *d_y, const T *scale,
-                              const T *mean, const T *var, T *d_x, T *d_scale,
-                              T *d_bias, float epsilon, int batch_size,
-                              int feature_size, cudaStream_t stream) {
+template <typename T, typename U>
+static void LayerNormBackward(const T *x, const T *d_y, const U *scale,
+                              const U *mean, const U *var, T *d_x, U *d_scale,
+                              U *d_bias, float epsilon, int batch_size,
+                              int feature_size,
+                              const framework::ExecutionContext &ctx) {
+  auto &dev_ctx = ctx.cuda_device_context();
+  auto stream = dev_ctx.stream();
+
   const int kMaxBlockDim = 512;
   const int kMaxBlockNum = 128;
   int gradient_flag = ((d_x != nullptr ? 1 : 0) << 2) |
@@ -362,14 +686,14 @@ static void LayerNormBackward(const T *x, const T *d_y, const T *scale,
 
   if (batch_size == 1) {
     LayerNormBackwardWhenBatchSizeIsOne<
-        T><<<(feature_size + kMaxBlockDim - 1) / kMaxBlockDim, kMaxBlockDim, 0,
-             stream>>>(x, d_y, d_x, d_scale, d_bias, mean, var, scale, epsilon,
-                       feature_size);
+        T, U><<<(feature_size + kMaxBlockDim - 1) / kMaxBlockDim, kMaxBlockDim,
+                0, stream>>>(x, d_y, d_x, d_scale, d_bias, mean, var, scale,
+                             epsilon, feature_size);
 
     if (d_x != nullptr) {
       switch (GetDesiredBlockDim(feature_size)) {
         FIXED_BLOCK_DIM_CASE(LayerNormBackwardPostProcessToCalculateDX<
-                             T, kBlockDim><<<1, kBlockDim, 0, stream>>>(
+                             T, U, kBlockDim><<<1, kBlockDim, 0, stream>>>(
             x, d_x, mean, var, epsilon, feature_size));
       }
     }
@@ -383,7 +707,7 @@ static void LayerNormBackward(const T *x, const T *d_y, const T *scale,
         FIXED_BLOCK_DIM_FIXED_BLOCK_NUM_CASE(
             feature_size, kMaxBlockNum,
             LayerNormBackwardGradientScaleOrBias<
-                T, kBlockDim, false,
+                T, U, kBlockDim, false,
                 false><<<block_num, kBlockDim, 0, stream>>>(
                 x, d_y, d_scale, d_bias, d_x, mean, var, scale, epsilon,
                 batch_size, feature_size, col_offset));
@@ -394,7 +718,8 @@ static void LayerNormBackward(const T *x, const T *d_y, const T *scale,
         FIXED_BLOCK_DIM_FIXED_BLOCK_NUM_CASE(
             feature_size, kMaxBlockNum,
             LayerNormBackwardGradientScaleOrBias<
-                T, kBlockDim, false, true><<<block_num, kBlockDim, 0, stream>>>(
+                T, U, kBlockDim, false,
+                true><<<block_num, kBlockDim, 0, stream>>>(
                 x, d_y, d_scale, d_bias, d_x, mean, var, scale, epsilon,
                 batch_size, feature_size, col_offset));
       }
@@ -404,7 +729,7 @@ static void LayerNormBackward(const T *x, const T *d_y, const T *scale,
         FIXED_BLOCK_DIM_FIXED_BLOCK_NUM_CASE(
             feature_size, kMaxBlockNum,
             LayerNormBackwardGradientAll<
-                T, kBlockDim, false><<<block_num, kBlockDim, 0, stream>>>(
+                T, U, kBlockDim, false><<<block_num, kBlockDim, 0, stream>>>(
                 x, d_y, d_scale, d_bias, d_x, mean, var, scale, epsilon,
                 batch_size, feature_size, col_offset));
       }
@@ -413,7 +738,7 @@ static void LayerNormBackward(const T *x, const T *d_y, const T *scale,
       switch (GetDesiredBlockDim(feature_size)) {
         FIXED_BLOCK_DIM_CASE(
             LayerNormBackwardGradientOnlyDX<
-                T, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
+                T, U, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
                 x, d_y, d_x, mean, var, scale, epsilon, feature_size));
       }
       break;
@@ -422,14 +747,15 @@ static void LayerNormBackward(const T *x, const T *d_y, const T *scale,
         FIXED_BLOCK_DIM_FIXED_BLOCK_NUM_CASE(
             feature_size, kMaxBlockNum,
             LayerNormBackwardGradientScaleOrBias<
-                T, kBlockDim, true, false><<<block_num, kBlockDim, 0, stream>>>(
+                T, U, kBlockDim, true,
+                false><<<block_num, kBlockDim, 0, stream>>>(
                 x, d_y, d_scale, d_bias, d_x, mean, var, scale, epsilon,
                 batch_size, feature_size, col_offset));
       }
       switch (GetDesiredBlockDim(feature_size)) {
         FIXED_BLOCK_DIM_CASE(
             LayerNormBackwardPostProcessToCalculateDX<
-                T, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
+                T, U, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
                 x, d_x, mean, var, epsilon, feature_size));
       }
       break;
@@ -438,33 +764,57 @@ static void LayerNormBackward(const T *x, const T *d_y, const T *scale,
         FIXED_BLOCK_DIM_FIXED_BLOCK_NUM_CASE(
             feature_size, kMaxBlockNum,
             LayerNormBackwardGradientScaleOrBias<
-                T, kBlockDim, true, true><<<block_num, kBlockDim, 0, stream>>>(
+                T, U, kBlockDim, true,
+                true><<<block_num, kBlockDim, 0, stream>>>(
                 x, d_y, d_scale, d_bias, d_x, mean, var, scale, epsilon,
                 batch_size, feature_size, col_offset));
       }
       switch (GetDesiredBlockDim(feature_size)) {
         FIXED_BLOCK_DIM_CASE(
             LayerNormBackwardPostProcessToCalculateDX<
-                T, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
+                T, U, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
                 x, d_x, mean, var, epsilon, feature_size));
       }
       break;
     case 7:  // d_x != nullptr, d_scale != nullptr, d_bias != nullptr
-      switch (block_dim) {
-        FIXED_BLOCK_DIM_FIXED_BLOCK_NUM_CASE(
-            feature_size, kMaxBlockNum,
-            LayerNormBackwardGradientAll<
-                T, kBlockDim, true><<<block_num, kBlockDim, 0, stream>>>(
-                x, d_y, d_scale, d_bias, d_x, mean, var, scale, epsilon,
-                batch_size, feature_size, col_offset));
-      }
-      switch (GetDesiredBlockDim(feature_size)) {
-        FIXED_BLOCK_DIM_CASE(
-            LayerNormBackwardPostProcessToCalculateDX<
-                T, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
-                x, d_x, mean, var, epsilon, feature_size));
-      }
+    {
+      constexpr int VPT = 4;
+      constexpr int BDIMX2 = 32;
+      constexpr int BDIMY2 = 4;
+      dim3 threads2(BDIMX2, BDIMY2, 1);
+      constexpr int part_size = BDIMY2 * VPT;
+      const dim3 blocks2((feature_size + BDIMX2 - 1) / BDIMX2, part_size, 1);
+
+      auto part_grad_gamma_ptr =
+          memory::Alloc(dev_ctx, part_size * feature_size * sizeof(U));
+      auto part_grad_beta_ptr =
+          memory::Alloc(dev_ctx, part_size * feature_size * sizeof(U));
+      U *part_grad_gamma = reinterpret_cast<U *>(part_grad_gamma_ptr->ptr());
+      U *part_grad_beta = reinterpret_cast<U *>(part_grad_beta_ptr->ptr());
+
+      LayerNormBackwardPartGradGammaBeta<T, U, BDIMX2, BDIMY2,
+                                         VPT><<<blocks2, threads2, 0, stream>>>(
+          d_y, x, batch_size, feature_size, mean, var, epsilon, part_grad_gamma,
+          part_grad_beta);  // compute part_grad_gamma, beta
+
+      constexpr int BDIMX3 = 32;
+      constexpr int BDIMY3 = 8;
+      dim3 threads3(BDIMX3, BDIMY3, 1);
+      const dim3 blocks3((feature_size + BDIMX2 - 1) / BDIMX2, 1, 1);
+      LayerNormBackwardSumGradGammaBeta<
+          T, U, BDIMX3, BDIMY3><<<blocks3, threads3, 0, stream>>>(
+          part_grad_gamma, part_grad_beta, part_size, batch_size, feature_size,
+          d_scale, d_bias);
+
+      constexpr int BDIMX1 = 32;
+      constexpr int BDIMY1 = 4;
+      dim3 threads1(BDIMX1, BDIMY1, 1);
+      const dim3 blocks1(1, batch_size, 1);
+      LayerNormBackwardComputeGradInput<
+          T, U, BDIMX1, BDIMY1><<<blocks1, threads1, 0, stream>>>(
+          d_y, x, batch_size, feature_size, mean, var, epsilon, scale, d_x);
       break;
+    }
     default:
       break;
   }
@@ -483,7 +833,7 @@ void LayerNormDirectCUDAFunctor<T>::operator()(cudaStream_t stream,
   int feature_size = static_cast<int>(matrix_dim[1]);
   switch (GetDesiredBlockDim(feature_size)) {
     FIXED_BLOCK_DIM_CASE(
-        LayerNormForward<T, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
+        LayerNormForward<T, T, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
             input, scale, bias, output, mean, variance, eps, feature_size));
     default:
       PADDLE_THROW(platform::errors::InvalidArgument(
@@ -498,6 +848,7 @@ class LayerNormKernel<platform::CUDADeviceContext, T>
     : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext &ctx) const override {
+    using U = LayerNormParamType<T>;
     const float epsilon = ctx.Attr<float>("epsilon");
     auto *scale = ctx.Input<Tensor>("Scale");
     auto *bias = ctx.Input<Tensor>("Bias");
@@ -511,10 +862,10 @@ class LayerNormKernel<platform::CUDADeviceContext, T>
     const auto x_dims = x->dims();
     auto *x_data = x->data<T>();
     auto *y_data = y->mutable_data<T>(ctx.GetPlace());
-    auto *mean_data = mean->mutable_data<T>(ctx.GetPlace());
-    auto *var_data = var->mutable_data<T>(ctx.GetPlace());
-    auto *scale_data = (scale == nullptr ? nullptr : scale->data<T>());
-    auto *bias_data = (bias == nullptr ? nullptr : bias->data<T>());
+    auto *mean_data = mean->mutable_data<U>(ctx.GetPlace());
+    auto *var_data = var->mutable_data<U>(ctx.GetPlace());
+    auto *scale_data = (scale == nullptr ? nullptr : scale->data<U>());
+    auto *bias_data = (bias == nullptr ? nullptr : bias->data<U>());
 
     auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
     int batch_size = static_cast<int>(matrix_dim[0]);
@@ -524,7 +875,8 @@ class LayerNormKernel<platform::CUDADeviceContext, T>
 
     switch (GetDesiredBlockDim(feature_size)) {
       FIXED_BLOCK_DIM_CASE(
-          LayerNormForward<T, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
+          LayerNormForward<T, U,
+                           kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
               x_data, scale_data, bias_data, y_data, mean_data, var_data,
               epsilon, feature_size));
       default:
@@ -540,6 +892,7 @@ class LayerNormGradKernel<platform::CUDADeviceContext, T>
     : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext &ctx) const override {
+    using U = LayerNormParamType<T>;
     const float epsilon = ctx.Attr<float>("epsilon");
     // d_x, d_scale, d_bias may be nullptr
     auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
@@ -554,14 +907,15 @@ class LayerNormGradKernel<platform::CUDADeviceContext, T>
 
     auto *x_data = x->data<T>();
     auto *d_y_data = d_y->data<T>();
-    auto *mean_data = mean->data<T>();
-    auto *var_data = var->data<T>();
-    auto *scale_data = (scale == nullptr ? nullptr : scale->data<T>());
+    auto *mean_data = mean->data<U>();
+    auto *var_data = var->data<U>();
+
+    auto *scale_data = (scale == nullptr ? nullptr : scale->data<U>());
     auto *d_scale_data =
         (d_scale == nullptr ? nullptr
-                            : d_scale->mutable_data<T>(ctx.GetPlace()));
+                            : d_scale->mutable_data<U>(ctx.GetPlace()));
     auto *d_bias_data =
-        (d_bias == nullptr ? nullptr : d_bias->mutable_data<T>(ctx.GetPlace()));
+        (d_bias == nullptr ? nullptr : d_bias->mutable_data<U>(ctx.GetPlace()));
     auto *d_x_data =
         (d_x == nullptr ? nullptr : d_x->mutable_data<T>(ctx.GetPlace()));
 
@@ -571,14 +925,14 @@ class LayerNormGradKernel<platform::CUDADeviceContext, T>
     int batch_size = static_cast<int>(matrix_dim[0]);
     int feature_size = static_cast<int>(matrix_dim[1]);
 
-    auto stream = ctx.cuda_device_context().stream();
-
-    LayerNormBackward<T>(x_data, d_y_data, scale_data, mean_data, var_data,
+    LayerNormBackward<T, U>(x_data, d_y_data, scale_data, mean_data, var_data,
                             d_x_data, d_scale_data, d_bias_data, epsilon,
-                         batch_size, feature_size, stream);
+                            batch_size, feature_size, ctx);
   }
 };
+
 template class LayerNormDirectCUDAFunctor<float>;
+
 #undef FIXED_BLOCK_DIM_FIXED_BLOCK_NUM_CASE_BASE
 #undef FIXED_BLOCK_DIM_FIXED_BLOCK_NUM_CASE
 #undef FIXED_BLOCK_DIM_CASE_BASE
@@ -587,11 +941,15 @@ template class LayerNormDirectCUDAFunctor<float>;
 }  // namespace paddle
 
 namespace ops = paddle::operators;
+namespace plat = paddle::platform;
 REGISTER_OP_CUDA_KERNEL(
     layer_norm,
     ops::LayerNormKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::LayerNormKernel<paddle::platform::CUDADeviceContext, double>);
+    ops::LayerNormKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::LayerNormKernel<paddle::platform::CUDADeviceContext, plat::float16>);
 REGISTER_OP_CUDA_KERNEL(
     layer_norm_grad,
     ops::LayerNormGradKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::LayerNormGradKernel<paddle::platform::CUDADeviceContext, double>);
+    ops::LayerNormGradKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::LayerNormGradKernel<paddle::platform::CUDADeviceContext,
+                             plat::float16>);

python/paddle/fluid/contrib/mixed_precision/fp16_lists.py

@@ -109,9 +109,11 @@ gray_list = {
     'elementwise_mod',
     'elementwise_floordiv',
     'batch_norm',
+    'layer_norm',
     'tanh',
     'sigmoid',
     'lookup_table',
+    'lookup_table_v2',
     'top_k',
     'pool2d',
     'pool3d',
@@ -123,6 +125,7 @@ gray_list = {
     'flatten2',
     'stack',
     'unstack',
+    'uniform_random',
     'uniform_random_batch_size_like',
     'gaussian_random',
     'gaussian_random_batch_size_like',
@@ -192,7 +195,6 @@ unsupported_fp16_list = {
     'sequence_concat',
     'sequence_slice',
     'data_norm',
-    'layer_norm',
     'group_norm',
     'spectral_norm',
     'depthwise_conv2d_transpose',

python/paddle/fluid/contrib/mixed_precision/fp16_utils.py

@@ -76,7 +76,7 @@ def _insert_cast_op(block, op, idx, src_dtype, dest_dtype):
 
     for in_name in op.input_names:
         if src_dtype == core.VarDesc.VarType.FP32 and op.type in [
-                'batch_norm', 'fused_bn_add_activation'
+                'batch_norm', 'fused_bn_add_activation', 'layer_norm'
         ]:
             if in_name not in {'X', 'Z'}:
                 continue
@@ -110,7 +110,8 @@ def _insert_cast_op(block, op, idx, src_dtype, dest_dtype):
                     op._set_attr('in_dtype', dest_dtype)
     if src_dtype == core.VarDesc.VarType.FP32 and dest_dtype == core.VarDesc.VarType.FP16:
         for out_name in op.output_names:
-            if op.type in ['batch_norm', 'fused_bn_add_activation'
+            if op.type in [
+                    'batch_norm', 'fused_bn_add_activation', 'layer_norm'
             ] and out_name != 'Y':
                 continue
             for out_var_name in op.output(out_name):

python/paddle/fluid/tests/unittests/test_layer_norm_op.py

@@ -15,6 +15,7 @@
 from __future__ import print_function
 import unittest
 import numpy as np
+import paddle
 
 from operator import mul
 import paddle.fluid.core as core
@@ -310,6 +311,8 @@ class TestLayerNormAPI(unittest.TestCase):
 class TestDygraphLayerNormAPIError(unittest.TestCase):
     def test_errors(self):
         with program_guard(Program(), Program()):
+            paddle.enable_static()
+
             layer_norm = fluid.LayerNorm([32, 32])
             # the input of LayerNorm must be Variable.
             x1 = np.random.random((3, 32, 32)).astype('float32')

python/paddle/nn/functional/norm.py

@@ -299,7 +299,8 @@ def layer_norm(x,
                                             'begin_norm_axis', begin_norm_axis)
         return dygraph_utils._append_activation_in_dygraph(pre_act, act=None)
 
-    check_variable_and_dtype(x, 'input', ['float32', 'float64'], 'LayerNorm')
+    check_variable_and_dtype(x, 'input', ['float16', 'float32', 'float64'],
+                             'LayerNorm')
 
     inputs = dict()
     inputs['X'] = [x]
@@ -311,11 +312,13 @@ def layer_norm(x,
 
     # create output
     helper = LayerHelper('layer_norm', **locals())
+
+    dtype = x.dtype
     mean_out = helper.create_variable_for_type_inference(
-        dtype=x.dtype, stop_gradient=True)
+        dtype=dtype, stop_gradient=True)
     variance_out = helper.create_variable_for_type_inference(
-        dtype=x.dtype, stop_gradient=True)
-    layer_norm_out = helper.create_variable_for_type_inference(x.dtype)
+        dtype=dtype, stop_gradient=True)
+    layer_norm_out = helper.create_variable_for_type_inference(dtype)
 
     helper.append_op(
         type="layer_norm",