chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/cholesky_solve_grad_kernel_impl.h

@@ -0,0 +1,146 @@
+// 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/cholesky_solve_grad_kernel.h"
+#include "paddle/phi/kernels/cholesky_solve_kernel.h"
+#include "paddle/phi/kernels/complex_kernel.h"
+#include "paddle/phi/kernels/elementwise_add_kernel.h"
+#include "paddle/phi/kernels/empty_kernel.h"
+#include "paddle/phi/kernels/expand_kernel.h"
+#include "paddle/phi/kernels/full_kernel.h"
+#include "paddle/phi/kernels/funcs/blas/blas.h"
+#include "paddle/phi/kernels/funcs/common_shape.h"
+#include "paddle/phi/kernels/funcs/complex_functors.h"
+#include "paddle/phi/kernels/funcs/for_range.h"
+#include "paddle/phi/kernels/funcs/matrix_reduce.h"
+#include "paddle/phi/kernels/funcs/tril_triu_compute.h"
+#include "paddle/phi/kernels/transpose_kernel.h"
+namespace phi {
+
+template <typename T, typename Context>
+void CholeskySolveGradKernel(const Context& dev_ctx,
+                             const DenseTensor& x,
+                             const DenseTensor& y,
+                             const DenseTensor& out,
+                             const DenseTensor& dout,
+                             bool upper,
+                             DenseTensor* dx,
+                             DenseTensor* dy) {
+  if (dout.numel() == 0) {
+    if (dx) {
+      dev_ctx.template Alloc<T>(dx);
+      if (dx->numel() != 0) {
+        Full<T, Context>(dev_ctx, dx->dims(), 0, dx);
+      }
+    }
+    if (dy) {
+      dev_ctx.template Alloc<T>(dy);
+      if (dy->numel() != 0) {
+        Full<T, Context>(dev_ctx, dy->dims(), 0, dy);
+      }
+    }
+    return;
+  }
+  // get broadcast dim
+  std::vector<int64_t> x_bst_dims_vec;
+  std::vector<int64_t> y_bst_dims_vec;
+  std::tie(x_bst_dims_vec, y_bst_dims_vec) =
+      funcs::MatrixGetBroadcastDims(x, y);
+  IntArray x_bst_dims(x_bst_dims_vec);
+  IntArray y_bst_dims(y_bst_dims_vec);
+
+  // Tensor broadcast to temp 'y_bst'
+  DenseTensor y_bst = Empty<T, Context>(dev_ctx, y_bst_dims);
+  ExpandKernel<T, Context>(dev_ctx, y, y_bst_dims, &y_bst);
+
+  // reuse forward to calculate dx_bst, which is broad_cast of dx
+  DenseTensor dx_bst = Empty<T, Context>(dev_ctx, x_bst_dims);
+  CholeskySolveKernel<T, Context>(dev_ctx, dout, y_bst, upper, &dx_bst);
+
+  // get 'dx' according to 'dx_bst'
+  dx->Resize(x.dims());
+  dev_ctx.template Alloc<T>(dx);
+  if (dx_bst.dims() == x.dims()) {
+    Copy<Context>(dev_ctx, dx_bst, dev_ctx.GetPlace(), false, dx);
+  } else {
+    funcs::MatrixReduceSumFunctor<T, Context> functor;
+    functor(dev_ctx, dx_bst, dx);
+    dx->Resize(x.dims());
+  }
+
+  // calculate out's conjugate for complex
+  DenseTensor out_conj = Conj<T, Context>(dev_ctx, out);
+  out_conj = TransposeLast2Dim<T>(dev_ctx, out_conj);
+
+  DenseTensor commonterm = Empty<T, Context>(dev_ctx, y_bst_dims);
+  auto blas = funcs::GetBlas<Context, T>(dev_ctx);
+  blas.MatMul(dx_bst,
+              funcs::CreateMatrixDescriptor(dx_bst.dims(), 0, false),
+              out_conj,
+              funcs::CreateMatrixDescriptor(out_conj.dims(), 0, false),
+              static_cast<T>(1),
+              &commonterm,
+              static_cast<T>(0));
+
+  // calculate commonterm's conjugate for complex
+  DenseTensor commonterm_conj = Conj<T, Context>(dev_ctx, commonterm);
+  commonterm_conj = TransposeLast2Dim<T>(dev_ctx, commonterm_conj);
+
+  AddKernel<T>(dev_ctx, commonterm, commonterm_conj, &commonterm);
+
+  DenseTensor dy_bst = Empty<T, Context>(dev_ctx, y_bst_dims);
+  if (upper) {
+    blas.MatMul(y_bst,
+                funcs::CreateMatrixDescriptor(y_bst.dims(), 0, false),
+                commonterm,
+                funcs::CreateMatrixDescriptor(commonterm.dims(), 0, false),
+                static_cast<T>(-1),
+                &dy_bst,
+                static_cast<T>(0));
+  } else {
+    blas.MatMul(commonterm,
+                funcs::CreateMatrixDescriptor(commonterm.dims(), 0, false),
+                y_bst,
+                funcs::CreateMatrixDescriptor(y_bst.dims(), 0, false),
+                static_cast<T>(-1),
+                &dy_bst,
+                static_cast<T>(0));
+  }
+
+  // get upper or lower of 'dy_bst'
+  DenseTensor dy_bst_upper = Empty<T, Context>(dev_ctx, y_bst_dims);
+
+  int y_bst_ndim = y_bst_dims_vec.size();
+  const auto H = y_bst_dims_vec[y_bst_ndim - 2];
+  const auto W = y_bst_dims_vec[y_bst_ndim - 1];
+  funcs::ForRange<Context> y_for_range(dev_ctx, dy_bst.numel());
+  funcs::TrilTriuCompute<T> tril_triu_functor(
+      dy_bst.data<T>(), 0, !upper, H, W, dy_bst_upper.data<T>());
+  y_for_range(tril_triu_functor);
+
+  // get 'dy' according to 'dy_bst'
+  dy->Resize(y.dims());
+  dev_ctx.template Alloc<T>(dy);
+  if (dy_bst_upper.dims() == y.dims()) {
+    Copy<Context>(dev_ctx, dy_bst_upper, dev_ctx.GetPlace(), false, dy);
+  } else {
+    funcs::MatrixReduceSumFunctor<T, Context> functor;
+    functor(dev_ctx, dy_bst_upper, dy);
+    dy->Resize(y.dims());
+  }
+}
+
+}  // namespace phi