Re-implement ROC-AUC. (#6747)

* Re-implement ROC-AUC.

* Binary
* MultiClass
* LTR
* Add documents.

This PR resolves a few issues:
  - Define a value when the dataset is invalid, which can happen if there's an
  empty dataset, or when the dataset contains only positive or negative values.
  - Define ROC-AUC for multi-class classification.
  - Define weighted average value for distributed setting.
  - A correct implementation for learning to rank task.  Previous
  implementation is just binary classification with averaging across groups,
  which doesn't measure ordered learning to rank.

tests/cpp/metric/test_auc.cu

@@ -0,0 +1,5 @@
+/*!
+ * Copyright 2021 XGBoost contributors
+ */
+// Dummy file to keep the CUDA conditional compile trick.
+#include "test_auc.cc"