chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/abs_grad_kernel_impl.h

@@ -0,0 +1,141 @@
+// 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/kernels/abs_grad_kernel.h"
+#include "paddle/phi/kernels/funcs/complex_functors.h"
+#include "paddle/phi/kernels/funcs/elementwise_base.h"
+#include "paddle/phi/kernels/funcs/for_range.h"
+
+namespace phi {
+
+#if defined(__NVCC__)
+
+template <typename T>
+struct AbsGradCUDAFunctor {
+  HOSTDEVICE inline AbsGradCUDAFunctor() {}
+
+  HOSTDEVICE inline T operator()(const T x, const T dout) const {
+    T output;
+    if (x == T(0)) {
+      output = T(0);
+    } else {
+      output = T(dout) * (x / T(std::abs(x)));
+    }
+    return output;
+  }
+};
+
+template <>
+struct AbsGradCUDAFunctor<bfloat16> {
+  HOSTDEVICE inline AbsGradCUDAFunctor() {}
+
+  HOSTDEVICE inline bfloat16 operator()(const bfloat16 x,
+                                        const bfloat16 dout) const {
+    bfloat16 output;
+    if (x == bfloat16(0)) {
+      output = static_cast<bfloat16>(0);
+    } else {
+      output = (dout) * (x / abs(x));
+    }
+    return output;
+  }
+};
+
+template <>
+struct AbsGradCUDAFunctor<complex64> {
+  HOSTDEVICE inline AbsGradCUDAFunctor() {}
+  HOSTDEVICE inline complex64 operator()(const complex64 x,
+                                         const float dout) const {
+    complex64 output;
+    if (x == complex64(0)) {
+      output = complex64(0);
+    } else {
+      output = complex64(dout) * (x / complex64(abs(x)));
+    }
+    return output;
+  }
+};
+
+template <>
+struct AbsGradCUDAFunctor<complex128> {
+  HOSTDEVICE inline AbsGradCUDAFunctor() {}
+  HOSTDEVICE inline complex128 operator()(const complex128 x,
+                                          const double dout) const {
+    complex128 output;
+    if (x == complex128(0)) {
+      output = complex128(0);
+    } else {
+      output = complex128(dout) * (x / complex128(abs(x)));
+    }
+    return output;
+  }
+};
+
+template <typename T>
+void AbsGradKernelImpl(const GPUContext& dev_ctx,
+                       const DenseTensor& x,
+                       const DenseTensor& dout,
+                       DenseTensor* dx) {
+  std::vector<const DenseTensor*> ins = {&x, &dout};
+  std::vector<DenseTensor*> outs = {dx};
+  dev_ctx.Alloc<T>(dx);
+  AbsGradCUDAFunctor<T> abs_grad_cuda_functor;
+  funcs::ElementwiseKernel<T>(dev_ctx, ins, &outs, abs_grad_cuda_functor);
+}
+
+template <typename T, typename Context>
+void AbsGradKernel(const Context& dev_ctx,
+                   const DenseTensor& x,
+                   const DenseTensor& dout,
+                   DenseTensor* dx) {
+  AbsGradKernelImpl<T>(dev_ctx, x, dout, dx);
+}
+#else
+template <typename T, typename Context>
+void AbsGradKernel(const Context& dev_ctx,
+                   const DenseTensor& x,
+                   const DenseTensor& dout,
+                   DenseTensor* dx) {
+  auto numel = dout.numel();
+  auto* dout_data = dout.data<dtype::Real<T>>();
+  auto* x_data = x.data<T>();
+
+  dev_ctx.template Alloc<T>(dx, static_cast<size_t>(numel * sizeof(T)));
+  auto* dx_data = dx->data<T>();
+
+  funcs::ForRange<Context> for_range(dev_ctx, numel);
+  funcs::AbsGradFunctor<T> functor(dout_data, x_data, dx_data, numel);
+  for_range(functor);
+}
+
+#endif
+template <typename T, typename Context>
+void AbsDoubleGradKernel(const Context& dev_ctx,
+                         const DenseTensor& x,
+                         const DenseTensor& ddx,
+                         DenseTensor* ddout) {
+  auto numel = ddx.numel();
+  auto* ddx_data = ddx.data<T>();
+  auto* x_data = x.data<T>();
+  dev_ctx.template Alloc<T>(ddout, static_cast<size_t>(numel * sizeof(T)));
+  auto* ddout_data = ddout->data<T>();
+
+  funcs::ForRange<Context> for_range(dev_ctx, numel);
+  funcs::AbsGradGradFunctor<T> functor(ddx_data, x_data, ddout_data, numel);
+  for_range(functor);
+}
+
+}  // namespace phi