chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/atan2_kernel_impl.h

@@ -0,0 +1,110 @@
+// 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 "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/device_context.h"
+#include "paddle/phi/kernels/atan2_kernel.h"
+#include "paddle/phi/kernels/broadcast_tensors_kernel.h"
+#include "paddle/phi/kernels/funcs/common_shape.h"
+#include "paddle/phi/kernels/funcs/for_range.h"
+
+namespace phi {
+template <typename T>
+struct Atan2Out {
+  using type = T;
+};
+
+template <>
+struct Atan2Out<int32_t> {
+  using type = double;
+};
+
+template <>
+struct Atan2Out<int64_t> {
+  using type = double;
+};
+
+template <typename T>
+struct Atan2Functor {
+  Atan2Functor(const T* x1,
+               const T* x2,
+               typename Atan2Out<T>::type* out,
+               int64_t numel)
+      : x1_(x1), x2_(x2), out_(out), numel_(numel) {}
+
+  HOSTDEVICE void operator()(int64_t idx) const {
+    out_[idx] = static_cast<typename Atan2Out<T>::type>(
+        ::atan2f(static_cast<float>(x1_[idx]), static_cast<float>(x2_[idx])));
+  }
+
+  const T* x1_;
+  const T* x2_;
+  typename Atan2Out<T>::type* out_;
+  int64_t numel_;
+};
+
+template <>
+struct Atan2Functor<double> {
+  Atan2Functor(const double* x1, const double* x2, double* out, int64_t numel)
+      : x1_(x1), x2_(x2), out_(out), numel_(numel) {}
+
+  HOSTDEVICE void operator()(int64_t idx) const {
+    out_[idx] = ::atan2(x1_[idx], x2_[idx]);
+  }
+
+  const double* x1_;
+  const double* x2_;
+  double* out_;
+  int64_t numel_;
+};
+
+template <typename T, typename Context>
+void Atan2Kernel(const Context& dev_ctx,
+                 const DenseTensor& x,
+                 const DenseTensor& y,
+                 DenseTensor* out) {
+  dev_ctx.template Alloc<typename Atan2Out<T>::type>(out);
+  if (out->numel() == 0) return;
+
+  if (x.dims() == y.dims()) {
+    const auto numel = out->numel();
+    const auto* x_data = x.data<T>();
+    const auto* y_data = y.data<T>();
+
+    auto* out_data = out->data<typename Atan2Out<T>::type>();
+    funcs::ForRange<Context> for_range(dev_ctx, numel);
+    Atan2Functor<T> functor(x_data, y_data, out_data, numel);
+    for_range(functor);
+  } else {
+    DenseTensor b_x, b_y;
+    // Calculate broadcasted dims
+    b_x.Resize(out->dims());
+    b_y.Resize(out->dims());
+    std::vector<const DenseTensor*> inputs = {&x, &y};
+    std::vector<DenseTensor*> outputs = {&b_x, &b_y};
+    BroadcastTensorsKernel<T, Context>(dev_ctx, inputs, outputs);
+
+    const auto numel = out->numel();
+    const auto* x_data = b_x.data<T>();
+    const auto* y_data = b_y.data<T>();
+    auto* out_data = out->data<typename Atan2Out<T>::type>();
+    funcs::ForRange<Context> for_range(dev_ctx, numel);
+    Atan2Functor<T> functor(x_data, y_data, out_data, numel);
+    for_range(functor);
+  }
+}
+
+}  // namespace phi