CPU evaluation for cat data. (#7393)

* Implementation for one hot based.
* Implementation for partition based. (LightGBM)

tests/cpp/categorical_helpers.h

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+/*!
+ * Copyright 2021 by XGBoost Contributors
+ *
+ * \brief Utilities for testing categorical data support.
+ */
+#include <numeric>
+#include <vector>
+
+#include "xgboost/span.h"
+#include "helpers.h"
+#include "../../src/common/categorical.h"
+
+namespace xgboost {
+inline std::vector<float> OneHotEncodeFeature(std::vector<float> x,
+                                              size_t num_cat) {
+  std::vector<float> ret(x.size() * num_cat, 0);
+  size_t n_rows = x.size();
+  for (size_t r = 0; r < n_rows; ++r) {
+    bst_cat_t cat = common::AsCat(x[r]);
+    ret.at(num_cat * r + cat) = 1;
+  }
+  return ret;
+}
+
+template <typename GradientSumT>
+void ValidateCategoricalHistogram(size_t n_categories,
+                                  common::Span<GradientSumT> onehot,
+                                  common::Span<GradientSumT> cat) {
+  auto cat_sum = std::accumulate(cat.cbegin(), cat.cend(), GradientPairPrecise{});
+  for (size_t c = 0; c < n_categories; ++c) {
+    auto zero = onehot[c * 2];
+    auto one = onehot[c * 2 + 1];
+
+    auto chosen = cat[c];
+    auto not_chosen = cat_sum - chosen;
+
+    ASSERT_LE(RelError(zero.GetGrad(), not_chosen.GetGrad()), kRtEps);
+    ASSERT_LE(RelError(zero.GetHess(), not_chosen.GetHess()), kRtEps);
+
+    ASSERT_LE(RelError(one.GetGrad(), chosen.GetGrad()), kRtEps);
+    ASSERT_LE(RelError(one.GetHess(), chosen.GetHess()), kRtEps);
+  }
+}
+} // namespace xgboost