chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/lu_unpack_kernel_impl.h

@@ -0,0 +1,96 @@
+// 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>
+struct LuUnpackEyeFunctor {
+  LuUnpackEyeFunctor(int64_t num_columns, T* output)
+      : num_columns_(num_columns), output_(output) {}
+
+  HOSTDEVICE void operator()(size_t idx) const {
+    output_[idx * num_columns_ + idx % num_columns_] = static_cast<T>(1);
+  }
+
+  int64_t num_columns_;
+  T* output_;
+};
+
+template <typename T, typename Context>
+void LUUnpackKernel(const Context& dev_ctx,
+                    const DenseTensor& x,
+                    const DenseTensor& pivots,
+                    bool unpack_ludata,
+                    bool unpack_pivots,
+                    DenseTensor* pmat,
+                    DenseTensor* l,
+                    DenseTensor* u) {
+  auto xdims = x.dims();
+  int xrank = xdims.size();
+  int64_t m = xdims[xrank - 2];
+  int64_t n = xdims[xrank - 1];
+  int64_t k = std::min(m, n);
+
+  if (unpack_ludata) {
+    dev_ctx.template Alloc<T>(l);
+    dev_ctx.template Alloc<T>(u);
+
+    if (x.numel() != 0) {
+      DenseTensor L, U;
+      LU_Unpack<Context, T>(dev_ctx, &x, &L, &U);
+
+      if (m >= n) {
+        Copy(dev_ctx, L, dev_ctx.GetPlace(), false, l);
+        Tensor_narrow<Context, T>(dev_ctx, &U, u, 0, k, 0, k);
+      } else {
+        Copy(dev_ctx, U, dev_ctx.GetPlace(), false, u);
+        Tensor_narrow<Context, T>(dev_ctx, &L, l, 0, k, 0, k);
+      }
+    }
+  }
+
+  if (unpack_pivots) {
+    dev_ctx.template Alloc<T>(pmat);
+    if (x.numel() == 0 || pivots.numel() == 0) {
+      // columns is the last dim.
+      auto pmat_dims = pmat->dims();
+      int64_t columns = pmat_dims[pmat_dims.size() - 1];
+      if (columns == 0) return;
+
+      T* pmat_data = pmat->data<T>();
+      funcs::SetConstant<Context, T> set_zero;
+      set_zero(dev_ctx, pmat, static_cast<T>(0));
+      int64_t rows = pmat->numel() / columns;
+      funcs::ForRange<Context> for_range(dev_ctx, rows);
+      LuUnpackEyeFunctor<T> functor(columns, pmat_data);
+      for_range(functor);
+      return;
+    }
+
+    PADDLE_ENFORCE_EQ(
+        pivots.dtype(),
+        DataType::INT32,
+        common::errors::InvalidArgument(
+            "The pivots of lu_unpack must be of type int32, but received [%s].",
+            pivots.dtype()));
+
+    Unpack_Pivot<Context, T>(dev_ctx, pivots, pmat, m, k);
+  }
+}
+
+}  // namespace phi