chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/lu_kernel.cc

@@ -0,0 +1,90 @@
+// 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/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/full_kernel.h"
+#include "paddle/phi/kernels/funcs/lapack/lapack_function.h"
+
+#include "paddle/phi/kernels/impl/lu_kernel_impl.h"
+#include "paddle/phi/kernels/lu_kernel.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void LUKernel(const Context& dev_ctx,
+              const DenseTensor& x,
+              bool pivot,
+              DenseTensor* out,
+              DenseTensor* pivots,
+              DenseTensor* infos) {
+  PADDLE_ENFORCE_EQ(pivot,
+                    true,
+                    errors::InvalidArgument(
+                        "lu without pivoting is not implemented on the CPU, "
+                        "but got pivots=False"));
+
+  if (x.numel() == 0) {
+    Full<int, Context>(dev_ctx, infos->dims(), static_cast<int>(0), infos);
+    Full<int, Context>(dev_ctx, pivots->dims(), static_cast<int>(0), pivots);
+    Full<T, Context>(dev_ctx, out->dims(), static_cast<T>(0), out);
+    return;
+  }
+  *out = Transpose2DTo6D<Context, T>(dev_ctx, x);
+
+  auto outdims = out->dims();
+  auto outrank = outdims.size();
+
+  int m = static_cast<int>(outdims[outrank - 1]);
+  int n = static_cast<int>(outdims[outrank - 2]);
+  int lda = std::max(1, m);
+
+  auto ipiv_dims = slice_ddim(outdims, 0, outrank - 1);
+  ipiv_dims[outrank - 2] = std::min(m, n);
+  pivots->Resize(ipiv_dims);
+  dev_ctx.template Alloc<int>(pivots);
+  auto ipiv_data = pivots->data<int>();
+
+  auto info_dims = slice_ddim(outdims, 0, outrank - 2);
+  infos->Resize(info_dims);
+  dev_ctx.template Alloc<int>(infos);
+  auto info_data = infos->data<int>();
+
+  auto batchsize = product(info_dims);
+  batchsize = std::max(static_cast<int>(batchsize), 1);
+  dev_ctx.template Alloc<T>(out);
+  auto out_data = out->data<T>();
+  for (int b = 0; b < batchsize; b++) {
+    auto out_data_item = &out_data[b * m * n];
+    int* info_data_item = &info_data[b];
+    int* ipiv_data_item = &ipiv_data[b * std::min(m, n)];
+    funcs::lapackLu<T>(
+        m, n, out_data_item, lda, ipiv_data_item, info_data_item);
+  }
+  *out = Transpose2DTo6D<Context, T>(dev_ctx, *out);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(lu,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::LUKernel,
+                   float,
+                   double,
+                   phi::complex64,
+                   phi::complex128) {
+  kernel->OutputAt(1).SetDataType(phi::DataType::INT32);
+  kernel->OutputAt(2).SetDataType(phi::DataType::INT32);
+}