chore: import upstream snapshot with attribution

paddle/phi/kernels/gpu/instance_norm_utils.h

@@ -0,0 +1,68 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <algorithm>
+#include <cfloat>
+#include <string>
+#include <vector>
+#include "paddle/phi/backends/gpu/gpu_dnn.h"
+#include "paddle/phi/common/amp_type_traits.h"
+#include "paddle/phi/kernels/funcs/cub.h"
+
+namespace phi {
+
+template <typename T>
+using CudnnDataType = backends::gpu::CudnnDataType<T>;
+template <typename T>
+using BatchNormParamType = typename CudnnDataType<T>::BatchNormParamType;
+
+template <typename T>
+static __global__ void repeat_param(const T *input,
+                                    T *output,
+                                    const int repeat_num,
+                                    const int C) {
+  CUDA_KERNEL_LOOP(i, repeat_num * C) {
+    int index = i % C;
+    output[i] = input[index];
+  }
+}
+
+template <typename T, int BlockDim, bool AVG>
+static __global__ void add_param(const T *input,
+                                 T *output,
+                                 const int repeat_num,
+                                 const int C) {
+  using MPType = typename MPTypeTrait<T>::Type;
+  typedef cub::BlockReduce<MPType, BlockDim> BlockReduce;
+  __shared__ typename BlockReduce::TempStorage ou_storage;
+  for (int i = blockIdx.x; i < C; i += gridDim.x) {
+    MPType ou = static_cast<MPType>(0);
+    for (int j = threadIdx.x; j < repeat_num; j += blockDim.x) {
+      const int index = j * C + i;
+      ou = ou + static_cast<MPType>(input[index]);
+    }
+    ou = BlockReduce(ou_storage).Reduce(ou, cub::Sum());
+    if (threadIdx.x == 0) {
+      output[i] = static_cast<T>(ou);
+    }
+    __syncthreads();
+
+    if (AVG) {
+      output[i] = static_cast<T>(static_cast<MPType>(output[i]) / repeat_num);
+    }
+  }
+}
+}  // namespace phi