chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/lu_solve_kernel.cc

@@ -0,0 +1,102 @@
+// Copyright (c) 2025 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/lu_solve_kernel.h"
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/enforce.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/lapack/lapack_function.h"
+#include "paddle/phi/kernels/impl/lu_kernel_impl.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void LuSolveKernel(const Context& dev_ctx,
+                   const DenseTensor& b,
+                   const DenseTensor& lu,
+                   const DenseTensor& pivots,
+                   const std::string& trans,
+                   DenseTensor* out) {
+  // Get lu matrix dimensions
+  auto lu_dims = lu.dims();
+  // Get x matrix dimensions
+  auto x_dims = b.dims();
+
+  // Allocate output tensor
+  dev_ctx.template Alloc<T>(out);
+  // Copy RHS data to output (will be overwritten with solution)
+  *out = Transpose2DTo6D<Context, T>(dev_ctx, b);
+  DenseTensor tem_lu = Transpose2DTo6D<Context, T>(dev_ctx, lu);
+
+  // Prepare LAPACK parameters
+  char trans_char = (trans == "N") ? 'N' : ((trans == "T") ? 'T' : 'C');
+  auto n_last_dim = lu_dims[lu_dims.size() - 1];
+  PADDLE_ENFORCE_LE_INT_MAX(
+      n_last_dim,
+      "TODO(large-tensor): LAPACK input n does not support int64 overflow.");
+  int n_int = static_cast<int>(n_last_dim);
+
+  auto nrhs_last_dim = x_dims[x_dims.size() - 1];
+  PADDLE_ENFORCE_LE_INT_MAX(nrhs_last_dim,
+                            "TODO(large-tensor): LAPACK nrhs does not "
+                            "support int64 overflow.");
+  int nrhs_int = static_cast<int>(nrhs_last_dim);
+  int lda = std::max(1, n_int);  // Leading dimension of A (LU matrix)
+  int ldb = std::max(1, n_int);  // Leading dimension of B (RHS/solution matrix)
+  int info = 0;
+
+  auto outdims = out->dims();
+  auto outrank = outdims.size();
+  auto batchsize_64 = product(slice_ddim(outdims, 0, outrank - 2));
+  PADDLE_ENFORCE_LE_INT_MAX(
+      batchsize_64,
+      "TODO(large-tensor): LAPACK batch size does not support int64 overflow.");
+  int batchsize = static_cast<int>(batchsize_64);
+  auto out_data = out->data<T>();
+  auto lu_data = tem_lu.data<T>();
+  auto pivots_data =
+      reinterpret_cast<int*>(const_cast<int*>(pivots.data<int>()));
+
+  for (int i = 0; i < batchsize; i++) {
+    auto* out_data_item = &out_data[i * lda * nrhs_int];
+    auto* lu_data_item = &lu_data[i * ldb * n_int];
+    auto* pivots_data_item = &pivots_data[i * n_int];
+    funcs::lapackLuSolve<T>(trans_char,
+                            n_int,
+                            nrhs_int,
+                            lu_data_item,
+                            lda,
+                            pivots_data_item,
+                            out_data_item,
+                            ldb,
+                            &info);
+    PADDLE_ENFORCE_EQ(
+        info,
+        0,
+        common::errors::PreconditionNotMet(
+            "LU solve failed with error code %d. Check if matrix is singular.",
+            info));
+  }
+  *out = Transpose2DTo6D<Context, T>(dev_ctx, *out);
+}
+}  // namespace phi
+
+PD_REGISTER_KERNEL(lu_solve,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::LuSolveKernel,
+                   float,
+                   double,
+                   phi::complex64,
+                   phi::complex128) {}