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// See docs in ../ops/linalg_ops.cc.
// TODO(konstantinos): Enable complex inputs. This will require additional tests
// and OP_REQUIRES.
#include <cmath>
#include "tensorflow/core/framework/kernel_def_builder.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/kernels/linalg_ops_common.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/port.h"
#include "tensorflow/core/public/tensor_shape.h"
#include "third_party/eigen3/Eigen/Cholesky"
namespace tensorflow {
template <class Scalar, bool SupportsBatchOperationT>
class CholeskyOp : public LinearAlgebraOp<Scalar, SupportsBatchOperationT> {
public:
explicit CholeskyOp(OpKernelConstruction* context)
: LinearAlgebraOp<Scalar, SupportsBatchOperationT>(context) {}
TensorShape GetOutputMatrixShape(
const TensorShape& input_matrix_shape) override {
return input_matrix_shape;
}
int64 GetCostPerUnit(const TensorShape& input_matrix_shape) override {
const int64 rows = input_matrix_shape.dim_size(0);
if (rows > (1LL << 20)) {
// A big number to cap the cost in case overflow.
return kint32max;
} else {
return rows * rows * rows;
}
}
using typename LinearAlgebraOp<Scalar, SupportsBatchOperationT>::MatrixMap;
using
typename LinearAlgebraOp<Scalar, SupportsBatchOperationT>::ConstMatrixMap;
void ComputeMatrix(OpKernelContext* context, const ConstMatrixMap& input,
MatrixMap* output) override {
OP_REQUIRES(context, input.rows() == input.cols(),
errors::InvalidArgument("Input matrix must be square."));
if (input.rows() == 0) {
// If X is an empty matrix (0 rows, 0 col), X * X' == X.
// Therefore, we return X.
return;
}
// Perform the actual LL^T Cholesky decomposition. This will only use
// the lower triangular part of data_in by default. The upper triangular
// part of the matrix will not be read.
Eigen::LLT<Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic,
Eigen::RowMajor>> llt_decomposition(input);
// Output the lower triangular in a dense form.
*output = llt_decomposition.matrixL();
OP_REQUIRES(context, llt_decomposition.info() == Eigen::Success,
errors::InvalidArgument("LLT decomposition was not successful. "
"The input might not be valid."));
}
};
REGISTER_LINALG_OP("Cholesky", (CholeskyOp<float, false>), float);
REGISTER_LINALG_OP("Cholesky", (CholeskyOp<double, false>), double);
REGISTER_LINALG_OP("BatchCholesky", (CholeskyOp<float, true>), float);
REGISTER_LINALG_OP("BatchCholesky", (CholeskyOp<double, true>), double);
} // namespace tensorflow
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