chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/lu_unpack_grad_kernel_impl.h

@@ -0,0 +1,111 @@
+// 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/impl/lu_kernel_impl.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void LUUnpackGradKernel(const Context& dev_ctx,
+                        const DenseTensor& x UNUSED,
+                        const DenseTensor& pivots UNUSED,
+                        const DenseTensor& l UNUSED,
+                        const DenseTensor& u UNUSED,
+                        const DenseTensor& pmat UNUSED,
+                        const DenseTensor& l_grad,
+                        const DenseTensor& u_grad,
+                        bool unpack_ludata UNUSED,
+                        bool unpack_pivots UNUSED,
+                        DenseTensor* x_grad) {
+  dev_ctx.template Alloc<T>(x_grad);
+  if (x_grad->numel() == 0) return;
+
+  DenseTensor dl_tril, du_triu;
+  const auto ldims = l_grad.dims();
+  dl_tril.Resize(ldims);
+  auto H = ldims[ldims.size() - 2];
+  auto W = ldims[ldims.size() - 1];
+  dev_ctx.template Alloc<T>(&dl_tril);
+  auto L_dataptr = dl_tril.data<T>();
+  funcs::ForRange<Context> l_for_range(dev_ctx, l_grad.numel());
+  funcs::TrilTriuCompute<T> tril_computer(
+      l_grad.data<T>(), -1, true, H, W, L_dataptr);
+  l_for_range(tril_computer);
+
+  const auto udims = u_grad.dims();
+  du_triu.Resize(udims);
+  H = udims[udims.size() - 2];
+  W = udims[udims.size() - 1];
+  dev_ctx.template Alloc<T>(&du_triu);
+  auto U_dataptr = du_triu.data<T>();
+  funcs::ForRange<Context> u_for_range(dev_ctx, u_grad.numel());
+  funcs::TrilTriuCompute<T> triu_computer(
+      u_grad.data<T>(), 0, false, H, W, U_dataptr);
+  u_for_range(triu_computer);
+
+  auto xdims = x_grad->dims();
+  int xrank = xdims.size();
+  int64_t m = xdims[xrank - 2];
+  int64_t n = xdims[xrank - 1];
+  int64_t k = std::min(m, n);
+
+  std::vector<int64_t> axes = {xrank - 2, xrank - 1};
+  std::vector<int64_t> slice_starts(2, 0);
+  std::vector<int64_t> slice_ends(2, 0);
+  auto valuedims = vectorize(xdims);
+
+  funcs::SetConstant<Context, T> setter;
+  setter(dev_ctx, x_grad, static_cast<T>(0));
+  if (m <= n) {
+    slice_starts[0] = 0;
+    slice_starts[1] = 0;
+    slice_ends[0] = k;
+    slice_ends[1] = k;
+    valuedims[xrank - 2] = k;
+    valuedims[xrank - 1] = k;
+    SetValueCompute_dispatch<Context, T>(dev_ctx,
+                                         x_grad,
+                                         &dl_tril,
+                                         x_grad,
+                                         axes,
+                                         &slice_starts,
+                                         &slice_ends,
+                                         valuedims,
+                                         xrank);
+
+    Tensor_Add<Context, T>(dev_ctx, *x_grad, du_triu, x_grad);
+  } else {
+    slice_starts[0] = 0;
+    slice_starts[1] = 0;
+    slice_ends[0] = k;
+    slice_ends[1] = k;
+    valuedims[xrank - 2] = k;
+    valuedims[xrank - 1] = k;
+    SetValueCompute_dispatch<Context, T>(dev_ctx,
+                                         x_grad,
+                                         &du_triu,
+                                         x_grad,
+                                         axes,
+                                         &slice_starts,
+                                         &slice_ends,
+                                         valuedims,
+                                         xrank);
+
+    Tensor_Add<Context, T>(dev_ctx, *x_grad, dl_tril, x_grad);
+  }
+}
+
+}  // namespace phi