Ensure low_rank_cholesky works well with LinearOperators on non-TF …

…backends.

PiperOrigin-RevId: 566817003

tensorflow_probability/python/math/linalg.py

@@ -454,8 +454,9 @@ def low_rank_cholesky(matrix, max_rank, trace_atol=0, trace_rtol=0, name=None):
                                     dtype_hint=tf.float32)
     if not isinstance(matrix, tf.linalg.LinearOperator):
       matrix = tf.convert_to_tensor(matrix, name='matrix', dtype=dtype)
+      matrix = tf.linalg.LinearOperatorFullMatrix(matrix)
 
-    mtrace = tf.linalg.trace(matrix)
+    mtrace = matrix.trace()
     mrank = tensorshape_util.rank(matrix.shape)
     batch_dims = mrank - 2
 
@@ -485,7 +486,7 @@ def lr_cholesky_body(i, lr, residual_diag):
         matrix_row = tf.squeeze(matrix.row(max_j), axis=-2)
       else:
         matrix_row = tf.gather(
-            matrix, max_j, axis=-1, batch_dims=batch_dims)[..., 0]
+            matrix.to_dense(), max_j, axis=-1, batch_dims=batch_dims)[..., 0]
       # residual_matrix[max_j, :] = matrix_row[max_j, :] - (lr * lr^t)[max_j, :]
       # And (lr * lr^t)[max_j, :] = lr[max_j, :] * lr^t
       lr_row_maxj = tf.gather(lr, max_j, axis=-2, batch_dims=batch_dims)
@@ -530,7 +531,7 @@ def lr_cholesky_body(i, lr, residual_diag):
 
     lr = tf.zeros(matrix.shape, dtype=matrix.dtype)[..., :max_rank]
 
-    mdiag = tf.linalg.diag_part(matrix)
+    mdiag = matrix.diag_part()
     i, lr, residual_diag = tf.while_loop(
         cond=lr_cholesky_cond,
         body=lr_cholesky_body,