chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/eig_kernel.cc

@@ -0,0 +1,107 @@
+// 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.
+
+#include "paddle/phi/kernels/eig_kernel.h"
+#include "paddle/phi/kernels/cpu/eig.h"
+
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void EigKernel(const Context& dev_ctx,
+               const DenseTensor& x,
+               DenseTensor* out_w,
+               DenseTensor* out_v) {
+  dev_ctx.template Alloc<dtype::Complex<T>>(out_w);
+  dev_ctx.template Alloc<dtype::Complex<T>>(out_v);
+
+  if (x.numel() == 0) {
+    return;
+  }
+
+  if (!IsComplexType(x.dtype())) {
+    int batch_count = BatchCount(x);
+    int order = static_cast<int>(x.dims(-1));
+
+    PADDLE_ENFORCE_LT(0,
+                      order,
+                      errors::InvalidArgument(
+                          "The order of Input(X) should be greater than 0."));
+
+    DenseTensor out_w_real;
+    DenseTensor out_v_real;
+
+    // double the size of out_w_real, the first half stores the real part,
+    // the next half stores the imag part
+    std::vector<int64_t> real_w_dims = vectorize<int64_t>(out_w->dims());
+    real_w_dims.back() *= 2;
+    out_w_real.Resize(real_w_dims);
+    dev_ctx.template Alloc<dtype::Real<T>>(&out_w_real);
+    out_v_real.Resize(x.dims());
+    dev_ctx.template Alloc<dtype::Real<T>>(&out_v_real);
+
+    ApplyEigKernel<dtype::Real<T>, Context>(
+        x, &out_w_real, &out_v_real, dev_ctx);
+
+    // 1. extract real part & imag part from out_w_real
+    DenseTensor out_w_real_part =
+        funcs::Slice<T>(dev_ctx, out_w_real, {-1}, {0}, {order});
+    DenseTensor out_w_imag_part =
+        funcs::Slice<T>(dev_ctx, out_w_real, {-1}, {order}, {order * 2});
+
+    // 2. construct complex values
+    auto* out_w_real_part_ptr = out_w_real_part.data<dtype::Real<T>>();
+    auto* out_w_imag_part_ptr = out_w_imag_part.data<dtype::Real<T>>();
+    int out_w_numel = static_cast<int>(out_w->numel());
+
+    funcs::ForRange<Context> for_range(dev_ctx, out_w_numel);
+    funcs::RealImagToComplexFunctor<dtype::Complex<T>> functor(
+        out_w_real_part_ptr,
+        out_w_imag_part_ptr,
+        dev_ctx.template Alloc<dtype::Complex<T>>(out_w),
+        out_w_numel);
+
+    for_range(functor);
+
+    // 3. construct complex vectors
+    DenseTensor out_v_real_trans = TransposeLast2Dim<T>(dev_ctx, out_v_real);
+    DenseTensor out_v_trans;
+    out_v_trans.Resize(x.dims());
+    dev_ctx.template Alloc<dtype::Complex<T>>(&out_v_trans);
+    ConstructComplexVectors<dtype::Real<T>, dtype::Complex<T>, Context>(
+        &out_v_trans, *out_w, out_v_real_trans, dev_ctx, batch_count, order);
+    TransposeTwoAxis<dtype::Complex<T>, Context>(
+        out_v_trans, out_v, x.dims().size() - 1, x.dims().size() - 2, dev_ctx);
+  } else {
+    ApplyEigKernel<T, Context>(x, out_w, out_v, dev_ctx);
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(eig,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::EigKernel,
+                   float,
+                   double,
+                   phi::complex64,
+                   phi::complex128) {
+  if (kernel_key.dtype() == phi::DataType::FLOAT32 ||
+      kernel_key.dtype() == phi::DataType::FLOAT64) {
+    kernel->OutputAt(0).SetDataType(phi::dtype::ToComplex(kernel_key.dtype()));
+    kernel->OutputAt(1).SetDataType(phi::dtype::ToComplex(kernel_key.dtype()));
+  }
+}