Add GPU support for self_adjoint_eig a.k.a. tf.linalg.eigh.

Clean up macros and template specializations in cuda_solvers.cc a bit.

PiperOrigin-RevId: 169715681

1 files changed, 4 insertions, 4 deletions

diff --git a/tensorflow/core/kernels/qr_op_impl.h b/tensorflow/core/kernels/qr_op_impl.h
index 431b083eef..b9843428a5 100644
--- a/tensorflow/core/kernels/qr_op_impl.h
+++ b/tensorflow/core/kernels/qr_op_impl.h
@@ -248,12 +248,12 @@ class QrOpGpu : public AsyncOpKernel {
       auto q_reshaped = q->flat_inner_dims<Scalar, 3>();
       eye(device, q_reshaped);
       for (int batch = 0; batch < batch_size; ++batch) {
-        // Notice: It appears that Ormqr does not write a zero into *info upon
+        // Notice: It appears that Unmqr does not write a zero into *info upon
         // success (probably a bug), so we simply re-use the info array already
         // zeroed by Geqrf above.
         OP_REQUIRES_OK_ASYNC(
             context,
-            solver.Ormqr(CUBLAS_SIDE_LEFT, CublasAdjointOp<Scalar>(), m, m,
+            solver.Unmqr(CUBLAS_SIDE_LEFT, CublasAdjointOp<Scalar>(), m, m,
                          min_size, &input_transposed_reshaped(batch, 0, 0), m,
                          &tau_matrix(batch, 0), &q_reshaped(batch, 0, 0), m,
                          dev_info.back().mutable_data() + batch),
@@ -266,12 +266,12 @@ class QrOpGpu : public AsyncOpKernel {
       }
     } else {
       // Generate m x n matrix Q. In this case we can use the more efficient
-      // algorithm in Orgqr to generate Q in place.
+      // algorithm in Ungqr to generate Q in place.
       dev_info.emplace_back(context, batch_size, "orgqr");
       for (int batch = 0; batch < batch_size; ++batch) {
         OP_REQUIRES_OK_ASYNC(
             context,
-            solver.Orgqr(
+            solver.Ungqr(
                 m, n, min_size, &input_transposed_reshaped(batch, 0, 0), m,
                 &tau_matrix(batch, 0), dev_info.back().mutable_data() + batch),
             done);