Modify the elementwise op according to the kernel primitive API (#34456)

paddle/fluid/operators/elementwise/elementwise_op_broadcast.cu.h

@@ -15,10 +15,14 @@
 #pragma once
 
 #include "paddle/fluid/operators/elementwise/elementwise_op_impl.cu.h"
-
+#include "paddle/fluid/operators/kernel_primitives/kernel_primitives.h"
 namespace paddle {
 namespace operators {
 
+#define MAX_INPUT_NUM 3  // the max num of ET for BroadcacstConfig
+
+namespace kps = paddle::operators::kernel_primitives;
+
 struct DimensionsTransform {
   using DimVector = std::vector<int64_t>;
   typedef void (*MergeFunctor)(bool &, std::vector<DimVector> &, DimVector &,
@@ -161,202 +165,113 @@ struct DimensionsTransform {
   }
 };
 
-struct StridesCalculation {
-  std::vector<std::vector<uint32_t>> strides;
-  std::vector<platform::FastDivMod> divmoders;
-
- private:
-  // To calculate the strides of each input_tensor.
-  __inline__ void CalculateStrides(
-      int N, int dim_size, const std::vector<std::vector<int64_t>> &in_dims) {
-    for (int j = 0; j < N; ++j) {
-      for (int i = 0; i < dim_size; ++i) {
-        strides[j][i] = in_dims[j][i] == 1 ? 0 : strides[j][i];
-        strides[j][i] =
-            (i != 0 && strides[j][i] != 0)
-                ? std::accumulate(in_dims[j].begin(), in_dims[j].begin() + i, 1,
-                                  std::multiplies<int64_t>())
-                : strides[j][i];
-      }
-    }
-  }
-
- public:
-  explicit StridesCalculation(const int64_t &dim_size,
-                              const std::vector<std::vector<int64_t>> &in_dims,
-                              const std::vector<int64_t> &out_dims) {
-    const auto N = in_dims.size();
-    divmoders.resize(dim_size);
-    strides.resize(N, std::vector<uint32_t>(dim_size, 1));
-
-    for (int i = 0; i < dim_size; ++i) {
-      divmoders[i] = platform::FastDivMod(out_dims[i]);
-    }
-    CalculateStrides(N, dim_size, in_dims);
-  }
-};
-
-template <typename InT, typename OutT, typename Functor, ElementwiseType ET,
-          int VecSize, int kDims>
-struct BroadcastArgsWrapper {
-  using InVecType = platform::AlignedVector<InT, VecSize>;
-  using OutVecType = platform::AlignedVector<OutT, VecSize>;
-
-  OutT *out_data;
-  OutVecType *vec_out_data;
-  const InT *__restrict__ in_data[ET];
-  const InVecType *__restrict__ vec_in_data[ET];
-  bool no_broadcast[ET];
-  platform::FastDivMod divmoders[kDims];
-  uint32_t strides[ET][framework::DDim::kMaxRank];
-  uint32_t scalar_cal_offset;
-  Functor func;
-
-  HOSTDEVICE BroadcastArgsWrapper(
-      const std::vector<const framework::Tensor *> &ins, framework::Tensor *out,
-      int scalar_cal_offset, Functor func,
-      const StridesCalculation &offset_calculator)
-      : scalar_cal_offset(scalar_cal_offset), func(func) {
-    for (int j = 0; j < ET; ++j) {
-      in_data[j] = ins[j]->data<InT>();
-      vec_in_data[j] = reinterpret_cast<const InVecType *>(in_data[j]);
-      no_broadcast[j] = ins[j]->dims() == out->dims() ? true : false;
-      memcpy(strides[j], offset_calculator.strides[j].data(),
-             kDims * sizeof(uint32_t));
-    }
-    out_data = out->data<OutT>();
-    vec_out_data = reinterpret_cast<OutVecType *>(out_data);
-    memcpy(divmoders, offset_calculator.divmoders.data(),
-           kDims * sizeof(platform::FastDivMod));
-  }
-
-  __device__ __forceinline__ uint32_t GetOffsetByDivmod(int idx, int in_idx) {
-    uint32_t offset = 0;
-
-#pragma unroll(kDims)
-    for (int i = 0; i < kDims; ++i) {
-      auto fast_divmoder = divmoders[i].Divmod(idx);
-      idx = fast_divmoder.val[0];
-      offset += fast_divmoder.val[1] * strides[in_idx][i];
-    }
-    return offset;
-  }
-
-  __device__ __forceinline__ void LoadVectorizedDataCommon(
-      InVecType *vector_args, int tid, int idx) {
-    *vector_args = vec_in_data[idx][tid];
-  }
-
-  __device__ __forceinline__ void LoadVectorizedDataByDivmod(InT *scalar_args,
-                                                             int tid, int idx) {
-    int index = tid * VecSize;
-#pragma unroll(VecSize)
-    for (int i = 0; i < VecSize; ++i) {
-      uint32_t offset = GetOffsetByDivmod(index + i, idx);
-      scalar_args[i] = in_data[idx][offset];
-    }
-  }
-
-  __device__ __forceinline__ void LoadScalarizedDataCommon(InT args[], int tid,
-                                                           int idx) {
-    args[idx] = in_data[idx][tid + scalar_cal_offset];
-  }
-
-  __device__ __forceinline__ void LoadScalarizedDataByDivmod(InT args[],
-                                                             int tid, int idx) {
-    auto offset = GetOffsetByDivmod(tid + scalar_cal_offset, idx);
-    args[idx] = in_data[idx][offset];
-  }
-
-  __device__ __forceinline__ void LoadVectorizedData(InT (*args)[VecSize],
-                                                     int tid) {
-#pragma unroll(ET)
-    for (int j = 0; j < ET; ++j) {
-      if (no_broadcast[j]) {
-        InVecType *vector_args = reinterpret_cast<InVecType *>(args[j]);
-        LoadVectorizedDataCommon(vector_args, tid, j);
+template <typename T, int VecSize, int ShapeSize, bool IsBoundary = false>
+__device__ __forceinline__ void LoadData(
+    T *dst, const T *__restrict__ src, uint32_t block_offset,
+    const kps::details::BroadcastConfig<ShapeSize> &config, int numel, int num,
+    bool need_broadcast) {
+  // numel : whole num of output
+  // num: how many data will be deal with in this time
+  if (need_broadcast) {
+    kps::ReadDataBc<T, VecSize, 1, 1, ShapeSize, IsBoundary>(
+        dst, src, block_offset, config, numel, 1, 1);
   } else {
-        LoadVectorizedDataByDivmod(args[j], tid, j);
-      }
-    }
+    kps::ReadData<T, VecSize, 1, 1, IsBoundary>(dst, src + block_offset, num);
   }
+}
 
-  __device__ __forceinline__ void LoadScalarizedData(InT args[], int tid) {
-#pragma unroll(ET)
-    for (int j = 0; j < ET; ++j) {
-      if (no_broadcast[j]) {
-        LoadScalarizedDataCommon(args, tid, j);
+template <ElementwiseType ET, typename InT, typename OutT, int ShapeSize,
+          int VecSize, typename Functor, bool IsBoundary = false>
+__device__ void DealSegment(
+    const framework::Array<const InT *__restrict__, ET> &in, OutT *out,
+    const framework::Array<bool, MAX_INPUT_NUM> &use_broadcast, uint32_t numel,
+    const framework::Array<kps::details::BroadcastConfig<ShapeSize>,
+                           MAX_INPUT_NUM> &configlists,
+    int num, Functor func) {
+  InT args[ET][VecSize];
+  OutT result[VecSize];
+  int block_offset = blockIdx.x * blockDim.x * VecSize;
+// load
+#pragma unroll
+  for (int i = 0; i < ET; i++) {
+    kps::Init<InT, VecSize>(args[i], static_cast<InT>(1.0f));
+    LoadData<InT, VecSize, ShapeSize, IsBoundary>(args[i], in[i], block_offset,
+                                                  configlists[i], numel, num,
+                                                  use_broadcast[i]);
+  }
+  // compute
+  if (ET == kUnary) {
+    kps::ElementwiseUnary<InT, OutT, VecSize, 1, 1, Functor>(result, args[0],
+                                                             func);
+  } else if (ET == kBinary) {
+    kps::ElementwiseBinary<InT, OutT, VecSize, 1, 1, Functor>(result, args[0],
+                                                              args[1], func);
   } else {
-        LoadScalarizedDataByDivmod(args, tid, j);
-      }
-    }
-  }
-
-  __device__ __forceinline__ void StoreVectorizedData(OutVecType vec_args_out,
-                                                      int tid) {
-    vec_out_data[tid] = vec_args_out;
+    kps::ElementwiseTernary<InT, OutT, VecSize, 1, 1, Functor>(
+        result, args[0], args[1], args[2], func);
   }
+  // compute
+  kps::WriteData<OutT, VecSize, 1, 1, IsBoundary>(out + block_offset, result,
+                                                  num);
+}
 
-  __device__ __forceinline__ void StoreScalarizedData(OutT args_out, int tid) {
-    out_data[scalar_cal_offset + tid] = args_out;
+template <ElementwiseType ET, typename InT, typename OutT, int ShapeSize,
+          int VecSize, typename Functor>
+__global__ void BroadcastKernel(
+    framework::Array<const InT *__restrict__, ET> in, OutT *out,
+    framework::Array<bool, MAX_INPUT_NUM> use_broadcast, uint32_t numel,
+    framework::Array<kps::details::BroadcastConfig<ShapeSize>, MAX_INPUT_NUM>
+        configlists,
+    int main_tid, int tail_tid, Functor func) {
+  int block_offset = blockIdx.x * blockDim.x * VecSize;
+  // data offset of this block
+  if (blockIdx.x < main_tid) {
+    int num = blockDim.x * VecSize;  // blockIdx.x < main_tid
+    DealSegment<ET, InT, OutT, ShapeSize, VecSize, Functor, false>(
+        in, out, use_broadcast, numel, configlists, num, func);
+  } else {  // reminder
+    int num = tail_tid;
+    DealSegment<ET, InT, OutT, ShapeSize, VecSize, Functor, true>(
+        in, out, use_broadcast, numel, configlists, num, func);
   }
-};
-
-template <typename InT, typename OutT, typename BroadcastArgsWrapper,
-          ElementwiseType ET>
-__device__ inline void ScalarizedBroadcastKernelImpl(
-    BroadcastArgsWrapper broadcast_wrapper, int tid) {
-  InT args[ET];
-  OutT args_out;
-  broadcast_wrapper.LoadScalarizedData(args, tid);
+}
 
-  // Calcualtion of the in_tensor data.
-  args_out = broadcast_wrapper.func(args);
+template <typename InT, typename OutT, ElementwiseType ET, int VecSize,
+          int Size, typename Functor>
+void LaunchKernel(const platform::CUDADeviceContext &ctx,
+                  const std::vector<const framework::Tensor *> &ins,
+                  framework::Tensor *out, Functor func,
+                  DimensionsTransform merge_dims) {
+  int numel = out->numel();
+  const int threads = 256;
+  int blocks = ((numel + VecSize - 1) / VecSize + threads - 1) / threads;
 
-  broadcast_wrapper.StoreScalarizedData(args_out, tid);
-}
+  int main_tid = numel / (VecSize * threads);
+  int tail_tid = numel % (VecSize * threads);
+  auto stream = ctx.stream();
+  OutT *out_data = out->data<OutT>();
 
-template <typename InT, typename OutT, typename BroadcastArgsWrapper,
-          ElementwiseType ET, int VecSize>
-__device__ inline void VectorizedBroadcastKernelImpl(
-    BroadcastArgsWrapper broadcast_wrapper, int tid) {
-  using OutVecType = platform::AlignedVector<OutT, VecSize>;
-  OutVecType args_out;
-  InT ins[ET];
-  InT args[ET][VecSize];
-  broadcast_wrapper.LoadVectorizedData(args, tid);
+  framework::Array<kps::details::BroadcastConfig<Size>, MAX_INPUT_NUM>
+      configlists;
+  framework::Array<bool, MAX_INPUT_NUM> use_broadcast;
+  framework::Array<const InT *__restrict__, ET> ins_data;
 
-#pragma unroll(VecSize)
-  for (int i = 0; i < VecSize; ++i) {
-#pragma unroll(ET)
-    for (int j = 0; j < ET; ++j) {
-      ins[j] = args[j][i];
+  for (int i = 0; i < ET; i++) {
+    use_broadcast[i] = (ins[i]->numel() != numel);
+    ins_data[i] = ins[i]->data<InT>();
+    if (use_broadcast[i]) {
+      // get the broadcast config,
+      // if data shape is[m, n], then you should set data_dim = {n, m}
+      // eg: out's shape [3, 45, 1]. then out_dims = {1, 45, 3}
+      configlists[i] = kps::details::BroadcastConfig<Size>(
+          merge_dims.out_dims, merge_dims.in_dims[i], merge_dims.dim_size);
     }
-    args_out.val[i] = broadcast_wrapper.func(ins);
   }
-  broadcast_wrapper.StoreVectorizedData(args_out, tid);
-}
 
-template <typename InT, typename OutT, typename BroadcastArgsWrapper,
-          ElementwiseType ET, int VecSize>
-__global__ void ElementwiseBroadcastKernel(
-    BroadcastArgsWrapper broadcast_wrapper, int main_tid, int tail_tid) {
-  int tid = threadIdx.x + blockIdx.x * blockDim.x;
-  // Vectorized calculation of major data whose length is the max multipler of
-  // VecSize,
-  // eg: Calcualting the front 1024-length data in total 1027 data once VecSize
-  // is 4.
-  if (tid < main_tid) {
-    VectorizedBroadcastKernelImpl<InT, OutT, BroadcastArgsWrapper, ET, VecSize>(
-        broadcast_wrapper, tid);
-  }
-  // Scalarzed calculation of rest data whose lenght cannot fulfill VecSize.
-  // eg: Calcualting the rest 3-length data in total 1027 data once VecSize is
-  // 4.
-  if (tid < tail_tid) {
-    ScalarizedBroadcastKernelImpl<InT, OutT, BroadcastArgsWrapper, ET>(
-        broadcast_wrapper, tid);
-  }
+  BroadcastKernel<ET, InT, OutT, Size, VecSize,
+                  Functor><<<blocks, threads, 0, stream>>>(
+      ins_data, out_data, use_broadcast, numel, configlists, main_tid, tail_tid,
+      func);
 }
 
 template <typename InT, typename OutT, ElementwiseType ET, int VecSize,
@@ -365,98 +280,24 @@ void LaunchBroadcastKernelForDifferentDimSize(
     const platform::CUDADeviceContext &ctx,
     const std::vector<const framework::Tensor *> &ins, framework::Tensor *out,
     int axis, Functor func) {
-  int numel = out->numel();
-  int threads = GetThreadsConfig(ctx, numel, VecSize);
-  int blocks = ((numel + VecSize - 1) / VecSize + threads - 1) / threads;
-  int main_tid = numel / VecSize;
-  int tail_tid = numel % VecSize;
-  int vec_len = main_tid * VecSize;
-  auto stream = ctx.stream();
-
   const auto merge_dims = DimensionsTransform(ins, out->dims(), axis);
-  const auto offset_calculator = StridesCalculation(
-      merge_dims.dim_size, merge_dims.in_dims, merge_dims.out_dims);
+#define DIM_SIZE(size)                                                       \
+  case size: {                                                               \
+    LaunchKernel<InT, OutT, ET, VecSize, size, Functor>(ctx, ins, out, func, \
+                                                        merge_dims);         \
+  } break;
 
   switch (merge_dims.dim_size) {
-    case 1: {
-      auto broadcast_wrapper =
-          BroadcastArgsWrapper<InT, OutT, Functor, ET, VecSize, 1>(
-              ins, out, vec_len, func, offset_calculator);
-      ElementwiseBroadcastKernel<InT, OutT, decltype(broadcast_wrapper), ET,
-                                 VecSize><<<blocks, threads, 0, stream>>>(
-          broadcast_wrapper, main_tid, tail_tid);
-      break;
-    }
-    case 2: {
-      auto broadcast_wrapper =
-          BroadcastArgsWrapper<InT, OutT, Functor, ET, VecSize, 2>(
-              ins, out, vec_len, func, offset_calculator);
-      ElementwiseBroadcastKernel<InT, OutT, decltype(broadcast_wrapper), ET,
-                                 VecSize><<<blocks, threads, 0, stream>>>(
-          broadcast_wrapper, main_tid, tail_tid);
-      break;
-    }
-    case 3: {
-      auto broadcast_wrapper =
-          BroadcastArgsWrapper<InT, OutT, Functor, ET, VecSize, 3>(
-              ins, out, vec_len, func, offset_calculator);
-      ElementwiseBroadcastKernel<InT, OutT, decltype(broadcast_wrapper), ET,
-                                 VecSize><<<blocks, threads, 0, stream>>>(
-          broadcast_wrapper, main_tid, tail_tid);
-      break;
-    }
-    case 4: {
-      auto broadcast_wrapper =
-          BroadcastArgsWrapper<InT, OutT, Functor, ET, VecSize, 4>(
-              ins, out, vec_len, func, offset_calculator);
-      ElementwiseBroadcastKernel<InT, OutT, decltype(broadcast_wrapper), ET,
-                                 VecSize><<<blocks, threads, 0, stream>>>(
-          broadcast_wrapper, main_tid, tail_tid);
-      break;
-    }
-    case 5: {
-      auto broadcast_wrapper =
-          BroadcastArgsWrapper<InT, OutT, Functor, ET, VecSize, 5>(
-              ins, out, vec_len, func, offset_calculator);
-      ElementwiseBroadcastKernel<InT, OutT, decltype(broadcast_wrapper), ET,
-                                 VecSize><<<blocks, threads, 0, stream>>>(
-          broadcast_wrapper, main_tid, tail_tid);
-      break;
-    }
-    case 6: {
-      auto broadcast_wrapper =
-          BroadcastArgsWrapper<InT, OutT, Functor, ET, VecSize, 6>(
-              ins, out, vec_len, func, offset_calculator);
-      ElementwiseBroadcastKernel<InT, OutT, decltype(broadcast_wrapper), ET,
-                                 VecSize><<<blocks, threads, 0, stream>>>(
-          broadcast_wrapper, main_tid, tail_tid);
-      break;
-    }
-    case 7: {
-      auto broadcast_wrapper =
-          BroadcastArgsWrapper<InT, OutT, Functor, ET, VecSize, 7>(
-              ins, out, vec_len, func, offset_calculator);
-      ElementwiseBroadcastKernel<InT, OutT, decltype(broadcast_wrapper), ET,
-                                 VecSize><<<blocks, threads, 0, stream>>>(
-          broadcast_wrapper, main_tid, tail_tid);
-      break;
-    }
-    case 8: {
-      auto broadcast_wrapper =
-          BroadcastArgsWrapper<InT, OutT, Functor, ET, VecSize, 8>(
-              ins, out, vec_len, func, offset_calculator);
-      ElementwiseBroadcastKernel<InT, OutT, decltype(broadcast_wrapper), ET,
-                                 VecSize><<<blocks, threads, 0, stream>>>(
-          broadcast_wrapper, main_tid, tail_tid);
-      break;
-    }
-    default: {
-      PADDLE_THROW(platform::errors::InvalidArgument(
-          "The maximum dimension of input tensor is expected to be less than "
-          "%d, but recieved %d.\n",
-          merge_dims.dim_size, framework::DDim::kMaxRank));
-    }
-  }
+    DIM_SIZE(1);
+    DIM_SIZE(2);
+    DIM_SIZE(3);
+    DIM_SIZE(4);
+    DIM_SIZE(5);
+    DIM_SIZE(6);
+    DIM_SIZE(7);
+    DIM_SIZE(8);
+  }
+#undef DIM_SIZE
 }
 
 template <ElementwiseType ET, typename InT, typename OutT, typename Functor>
@@ -528,5 +369,7 @@ void LaunchElementwiseCudaKernel(
   }
 }
 
+#undef MAX_INPUT_NUM
+
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/elementwise/elementwise_op_impl.cu.h

@@ -14,6 +14,7 @@ limitations under the License. */
 #pragma once
 
 #include "paddle/fluid/framework/tensor.h"
+#include "paddle/fluid/operators/kernel_primitives/kernel_primitives.h"
 #include "paddle/fluid/platform/cuda_device_function.h"
 #include "paddle/fluid/platform/fast_divmod.h"
 
@@ -26,6 +27,7 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
+namespace kps = paddle::operators::kernel_primitives;
 enum ElementwiseType { kUnary = 1, kBinary = 2, kTernary = 3 };
 
 /*
@@ -67,121 +69,74 @@ int GetVectorizedSizeForIO(const std::vector<const framework::Tensor *> &ins,
   return vec_size;
 }
 
-template <ElementwiseType ET, int VecSize, typename InT, typename OutT>
-struct ElementwiseDataWrapper {
-  using InVecType = platform::AlignedVector<InT, VecSize>;
-  using OutVecType = platform::AlignedVector<OutT, VecSize>;
-
-  const InT *__restrict__ in_data[ET];
-  OutT *out_data;
-  uint32_t scalar_cal_offset;
-
-  HOSTDEVICE ElementwiseDataWrapper(
-      const std::vector<const framework::Tensor *> &ins,
-      std::vector<framework::Tensor *> *outs, uint32_t scalar_cal_offset)
-      : scalar_cal_offset(scalar_cal_offset) {
+template <ElementwiseType ET, int VecSize, typename InT, typename OutT,
+          typename Functor, bool IsBoundary>
+__device__ void DealSegment(
+    const framework::Array<const InT *__restrict__, ET> &in, OutT *out, int num,
+    Functor func) {
+  int data_offset = VecSize * blockIdx.x * blockDim.x;
+  InT args[ET][VecSize];
+  OutT result[VecSize];
+// load data
 #pragma unroll
-    for (int i = 0; i < ET; ++i) {
-      in_data[i] = ins[i]->data<InT>();
-    }
-    out_data = (*outs)[0]->data<OutT>();
+  for (int i = 0; i < ET; i++) {
+    kps::Init<InT, VecSize>(args[i], static_cast<InT>(1.0f));
+    kps::ReadData<InT, VecSize, 1, 1, IsBoundary>(args[i], in[i] + data_offset,
+                                                  num);
   }
 
-  inline __device__ void LoadVectorizedData(InVecType vec_args[], int tid) {
-#pragma unroll
-    for (int i = 0; i < ET; ++i) {
-      const InVecType *in_vec_data =
-          reinterpret_cast<const InVecType *>(in_data[i]);
-      vec_args[i] = in_vec_data[tid];
-    }
-  }
-
-  inline __device__ void LoadScalarizedData(InT args[], int tid) {
-#pragma unroll
-    for (int i = 0; i < ET; ++i) {
-      args[i] = in_data[i][tid + scalar_cal_offset];
-    }
-  }
-
-  inline __device__ void StoreVectorizedData(OutVecType res, int tid) {
-    OutVecType *out_vec = reinterpret_cast<OutVecType *>(out_data);
-    out_vec[tid] = res;
-  }
-
-  inline __device__ void StoreScalarizedData(OutT res, int tid) {
-    out_data[tid + scalar_cal_offset] = res;
-  }
-};
-
-template <ElementwiseType ET, int VecSize, typename ElementwiseWrapper,
-          typename InT, typename OutT, typename Functor>
-__device__ inline void VectorizedKernelImpl(ElementwiseWrapper data,
-                                            Functor func, int tid) {
-  using InVecType = platform::AlignedVector<InT, VecSize>;
-  using OutVecType = platform::AlignedVector<OutT, VecSize>;
-  InVecType ins_vec[ET];
-  OutVecType out_vec;
-  InT *ins_ptr[ET];
-  InT ins[ET];
-#pragma unroll
-  for (int i = 0; i < ET; ++i) {
-    ins_ptr[i] = reinterpret_cast<InT *>(&(ins_vec[i]));
-  }
-  // load
-  data.LoadVectorizedData(ins_vec, tid);
-
-// compute
-#pragma unroll
-  for (int i = 0; i < VecSize; ++i) {
-#pragma unroll
-    for (int j = 0; j < ET; ++j) {
-      ins[j] = ins_ptr[j][i];
-    }
-    out_vec.val[i] = func(ins);
+  // compute
+  if (ET == kUnary) {
+    kps::ElementwiseUnary<InT, OutT, VecSize, 1, 1, Functor>(result, args[0],
+                                                             func);
+  } else if (ET == kBinary) {
+    kps::ElementwiseBinary<InT, OutT, VecSize, 1, 1, Functor>(result, args[0],
+                                                              args[1], func);
+  } else {
+    kps::ElementwiseTernary<InT, OutT, VecSize, 1, 1, Functor>(
+        result, args[0], args[1], args[2], func);
   }
-  // store
-  data.StoreVectorizedData(out_vec, tid);
-}
 
-template <ElementwiseType ET, typename ElementwiseWrapper, typename InT,
-          typename OutT, typename Functor>
-__device__ inline void ScalarKernelImpl(ElementwiseWrapper data, Functor func,
-                                        int tid) {
-  InT ins[ET];
-  OutT out;
-
-  // load
-  data.LoadScalarizedData(ins, tid);
-  // compute
-  out = func(ins);
   // store
-  data.StoreScalarizedData(out, tid);
+  kps::WriteData<OutT, VecSize, 1, 1, IsBoundary>(out + data_offset, result,
+                                                  num);
 }
 
-template <ElementwiseType ET, typename ElementwiseWrapper, typename InT,
-          typename OutT, int VecSize, typename Functor>
-__global__ void VectorizedKernel(ElementwiseWrapper data, int main_tid,
-                                 int tail_tid, Functor func) {
-  int tid = blockIdx.x * blockDim.x + threadIdx.x;
-
-  if (tid < main_tid) {
-    VectorizedKernelImpl<ET, VecSize, ElementwiseWrapper, InT, OutT, Functor>(
-        data, func, tid);
-  }
-  if (tid < tail_tid) {
-    ScalarKernelImpl<ET, ElementwiseWrapper, InT, OutT, Functor>(data, func,
-                                                                 tid);
+template <ElementwiseType ET, int VecSize, typename InT, typename OutT,
+          typename Functor>
+__global__ void ElementVectorizeKernel(
+    framework::Array<const InT *__restrict__, ET> in, OutT *out, int size,
+    Functor func) {
+  int data_offset = VecSize * blockIdx.x * blockDim.x;
+  int num = size - data_offset;
+  // the num this time have to deal with
+  if (VecSize * blockDim.x > num) {  // reminder segment
+    DealSegment<ET, VecSize, InT, OutT, Functor, true>(in, out, num, func);
+  } else {  // complete segment
+    DealSegment<ET, VecSize, InT, OutT, Functor, false>(in, out, num, func);
   }
 }
 
-template <ElementwiseType ET, typename ElementwiseWrapper, typename InT,
-          typename OutT, typename Functor>
-__global__ void ScalarKernel(ElementwiseWrapper data, int numel, Functor func) {
-  int tid = blockIdx.x * blockDim.x + threadIdx.x;
-  if (tid < numel) {
-    ScalarKernelImpl<ET, ElementwiseWrapper, InT, OutT, Functor>(data, func,
-                                                                 tid);
-  }
+template <ElementwiseType ET, typename InT, typename OutT, typename Functor,
+          int VecSize>
+void ElementwiseCudaKernel(const platform::CUDADeviceContext &ctx,
+                           const std::vector<const framework::Tensor *> &ins,
+                           std::vector<framework::Tensor *> *outs,
+                           Functor func) {
+  auto numel = ins[0]->numel();
+  int block_size = GetThreadsConfig(ctx, numel, VecSize);
+  int grid_size =
+      ((numel + VecSize - 1) / VecSize + block_size - 1) / block_size;
+
+  auto stream = ctx.stream();
+  OutT *out = (*outs)[0]->data<OutT>();
+  framework::Array<const InT *__restrict__, ET> in;
+  for (int i = 0; i < ET; i++) {
+    in[i] = ins[i]->data<InT>();
+  }
+  ElementVectorizeKernel<ET, VecSize, InT, OutT,
+                         Functor><<<grid_size, block_size, 0, stream>>>(
+      in, out, numel, func);
 }
 
 template <ElementwiseType ET, typename InT, typename OutT, typename Functor>
@@ -190,43 +145,17 @@ void LaunchSameDimsElementwiseCudaKernel(
     const std::vector<const framework::Tensor *> &ins,
     std::vector<framework::Tensor *> *outs, Functor func) {
   // calculate the max vec_size for all ins and outs
-  auto numel = ins[0]->numel();
   int vec_size = GetVectorizedSizeForIO<InT, OutT>(ins, *outs);
-  int block_size = GetThreadsConfig(ctx, numel, vec_size);
-  int grid_size =
-      ((numel + vec_size - 1) / vec_size + block_size - 1) / block_size;
-  int main_tid = numel / vec_size;
-  int tail_tid = numel % vec_size;
-  uint32_t vec_len = main_tid * vec_size;
-
-  // cuda kernel
-  auto stream = ctx.stream();
-
   switch (vec_size) {
-    case 4: {
-      auto data_wrapper =
-          ElementwiseDataWrapper<ET, 4, InT, OutT>(ins, outs, vec_len);
-      VectorizedKernel<ET, decltype(data_wrapper), InT, OutT,
-                       4><<<grid_size, block_size, 0, stream>>>(
-          data_wrapper, main_tid, tail_tid, func);
+    case 4:
+      ElementwiseCudaKernel<ET, InT, OutT, Functor, 4>(ctx, ins, outs, func);
       break;
-    }
-    case 2: {
-      auto data_wrapper =
-          ElementwiseDataWrapper<ET, 2, InT, OutT>(ins, outs, vec_len);
-      VectorizedKernel<ET, decltype(data_wrapper), InT, OutT,
-                       2><<<grid_size, block_size, 0, stream>>>(
-          data_wrapper, main_tid, tail_tid, func);
+    case 2:
+      ElementwiseCudaKernel<ET, InT, OutT, Functor, 2>(ctx, ins, outs, func);
       break;
-    }
-    case 1: {
-      auto data_wrapper =
-          ElementwiseDataWrapper<ET, 1, InT, OutT>(ins, outs, 0);
-      ScalarKernel<ET, decltype(data_wrapper), InT,
-                   OutT><<<grid_size, block_size, 0, stream>>>(data_wrapper,
-                                                               numel, func);
+    case 1:
+      ElementwiseCudaKernel<ET, InT, OutT, Functor, 1>(ctx, ins, outs, func);
       break;
-    }
     default: {
       PADDLE_THROW(platform::errors::Unimplemented(
           "Unsupported vectorized size: %d !", vec_size));