1 files changed, 71 insertions, 0 deletions
diff --git a/tensorflow/core/kernels/cholesky_op.cc b/tensorflow/core/kernels/cholesky_op.cc
new file mode 100644
index 0000000000..12632fb248
--- /dev/null
+++ b/tensorflow/core/kernels/cholesky_op.cc
@@ -0,0 +1,71 @@
+// 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