chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/erfinv_kernel.cc

@@ -0,0 +1,53 @@
+// 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.
+
+#ifndef _USE_MATH_DEFINES
+#define _USE_MATH_DEFINES  // use M_2_SQRTPI on Windows
+#endif
+
+#include "paddle/phi/kernels/erfinv_kernel.h"
+
+#include <limits>
+
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void ErfinvKernel(const Context& dev_ctx,
+                  const DenseTensor& x,
+                  DenseTensor* out) {
+  dev_ctx.template Alloc<T>(out);
+  if (out && out->numel() == 0) {
+    return;
+  }
+  auto eigen_in = EigenVector<T>::Flatten(x);
+  auto eigen_out = EigenVector<T>::Flatten(*out);
+  auto& place = *dev_ctx.eigen_device();
+  constexpr T half = static_cast<T>(0.5);
+  constexpr T half_sqrt = static_cast<T>(M_SQRT1_2);
+  constexpr T one = static_cast<T>(1);
+  const T nan_val = std::numeric_limits<T>::quiet_NaN();
+  // erfinv is only defined on [-1, 1]; align with PyTorch/scipy by returning
+  // NaN for |x| > 1 (boundary +/-1 still yields +/-inf through ndtri).
+  eigen_out.device(place) =
+      (eigen_in.abs() > eigen_in.constant(one))
+          .select(eigen_in.constant(nan_val),
+                  (eigen_in * half + half).ndtri() * half_sqrt);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(erfinv, CPU, ALL_LAYOUT, phi::ErfinvKernel, float, double) {}