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/* Copyright 2015 The TensorFlow 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 "third_party/eigen3/Eigen/Core"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/tensor_types.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/kernels/linalg_ops_common.h"
namespace tensorflow {
template <typename Scalar>
class CholeskyGrad : public LinearAlgebraOp<Scalar> {
public:
typedef LinearAlgebraOp<Scalar> Base;
explicit CholeskyGrad(OpKernelConstruction* context) : Base(context) {}
using TensorShapes = typename Base::TensorShapes;
using Matrix = typename Base::Matrix;
using MatrixMap = typename Base::MatrixMap;
using MatrixMaps = typename Base::MatrixMaps;
using ConstMatrixMap = typename Base::ConstMatrixMap;
using ConstMatrixMaps = typename Base::ConstMatrixMaps;
using ConstRef = Eigen::Ref<const Matrix>;
using Ref = Eigen::Ref<Matrix>;
void ValidateInputMatrixShapes(
OpKernelContext* context,
const TensorShapes& input_matrix_shapes) const final {
OP_REQUIRES(context, input_matrix_shapes.size() == 2,
errors::InvalidArgument("Expected two input matrices, got %d.",
input_matrix_shapes.size()));
OP_REQUIRES(context, input_matrix_shapes[0] == input_matrix_shapes[1],
errors::InvalidArgument(
"Inputs (L and grad) must have the same shape."));
OP_REQUIRES(context,
TensorShapeUtils::IsSquareMatrix(input_matrix_shapes[0]),
errors::InvalidArgument("Inputs must be a square matrices."));
}
TensorShapes GetOutputMatrixShapes(
const TensorShapes& input_matrix_shapes) const final {
return TensorShapes({input_matrix_shapes[0]});
}
void ComputeMatrix(OpKernelContext* context, const ConstMatrixMaps& inputs,
MatrixMaps* outputs) final {
const ConstMatrixMap& input_matrix_l_full = inputs[0];
const ConstMatrixMap& input_matrix_grad = inputs[1];
MatrixMap output_matrix = outputs->at(0);
// Algorithm only depends on lower triangular half on input_matrix_l.
const Matrix input_matrix_l =
input_matrix_l_full.template triangularView<Eigen::Lower>();
// Algorithm only depends on lower triangular half on input_matrix_grad.
output_matrix = input_matrix_grad.template triangularView<Eigen::Lower>();
const int64 kMatrixSize = input_matrix_l.rows();
const int64 kMaxBlockSize = 32;
for (int64 block_end = kMatrixSize; block_end > 0;
block_end -= kMaxBlockSize) {
/* This shows the block structure.
/ \
| |
| R D |
\ B C /
Variables names representing the derivative matrix have a trailing '_bar'.
*/
const int64 block_begin = std::max(int64{0}, block_end - kMaxBlockSize);
const int64 block_size = block_end - block_begin;
const int64 trailing_size = kMatrixSize - block_end;
auto B = input_matrix_l.block(block_end, 0, trailing_size, block_begin);
auto B_bar =
output_matrix.block(block_end, 0, trailing_size, block_begin);
auto C = input_matrix_l.block(block_end, block_begin, trailing_size,
block_size);
auto C_bar = output_matrix.block(block_end, block_begin, trailing_size,
block_size);
auto D = input_matrix_l.block(block_begin, block_begin, block_size,
block_size);
auto D_bar =
output_matrix.block(block_begin, block_begin, block_size, block_size);
auto R = input_matrix_l.block(block_begin, 0, block_size, block_begin);
auto R_bar = output_matrix.block(block_begin, 0, block_size, block_begin);
C_bar = D.adjoint()
.template triangularView<Eigen::Upper>()
.solve(C_bar.adjoint())
.adjoint();
D_bar -= (C_bar.adjoint() * C).template triangularView<Eigen::Lower>();
B_bar -= C_bar * R;
R_bar -= C_bar.adjoint() * B;
CholeskyGradUnblocked(D, D_bar);
R_bar -= (D_bar + D_bar.adjoint()) * R;
}
output_matrix = (0.5 * (output_matrix + output_matrix.transpose())).eval();
}
private:
void CholeskyGradUnblocked(const ConstRef& l_block, Ref grad_block) {
const int64 kMatrixSize = l_block.rows();
for (int64 k = kMatrixSize - 1; k >= 0; k--) {
/* This shows the block structure.
/ \
| |
| r d |
\ B c /
Variables names representing the derivative matrix have a trailing '_bar'.
*/
const int64 number_rows_B = kMatrixSize - (k + 1);
const int64 number_rows_r_stack_B = number_rows_B + 1;
auto r = l_block.block(k, 0, 1, k);
auto r_bar = grad_block.block(k, 0, 1, k);
auto d = l_block(k, k); // This needs to be a scalar rather than a view.
auto d_bar = grad_block.block(k, k, 1, 1);
// B is not included explicitly because it is not used on its own.
auto B_bar = grad_block.block(k + 1, 0, number_rows_B, k);
auto c = l_block.block(k + 1, k, number_rows_B, 1);
auto c_bar = grad_block.block(k + 1, k, number_rows_B, 1);
// Result of vertical stacking d_bar and c_bar.
auto d_stack_c_bar = grad_block.block(k, k, number_rows_r_stack_B, 1);
// Result of vertical stacking of r and B.
auto r_stack_B = l_block.block(k, 0, number_rows_r_stack_B, k);
d_bar -= (c.adjoint() * c_bar) / d;
d_stack_c_bar /= d;
r_bar -= d_stack_c_bar.adjoint() * r_stack_B;
B_bar -= c_bar * r;
d_bar /= 2.;
}
}
};
REGISTER_LINALG_OP("CholeskyGrad", (CholeskyGrad<float>), float);
REGISTER_LINALG_OP("CholeskyGrad", (CholeskyGrad<double>), double);
REGISTER_LINALG_OP("BatchCholeskyGrad", (CholeskyGrad<float>), float);
REGISTER_LINALG_OP("BatchCholeskyGrad", (CholeskyGrad<double>), double);
} // namespace tensorflow
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