Bound the size of the histogram cache. (#9440)

- A new histogram collection with a limit in size.
- Unify histogram building logic between hist, multi-hist, and approx.

tests/python/test_quantile_dmatrix.py

@@ -16,6 +16,7 @@ from xgboost.testing import (
     predictor_equal,
 )
 from xgboost.testing.data import check_inf, np_dtypes
+from xgboost.testing.data_iter import run_mixed_sparsity
 
 
 class TestQuantileDMatrix:
@@ -334,3 +335,6 @@ class TestQuantileDMatrix:
 
         with pytest.raises(ValueError, match="consistent"):
             xgb.train({}, Xy, num_boost_round=2, xgb_model=booster)
+
+    def test_mixed_sparsity(self) -> None:
+        run_mixed_sparsity("cpu")

tests/python/test_updaters.py

@@ -11,6 +11,7 @@ from xgboost import testing as tm
 from xgboost.testing.params import (
     cat_parameter_strategy,
     exact_parameter_strategy,
+    hist_cache_strategy,
     hist_multi_parameter_strategy,
     hist_parameter_strategy,
 )
@@ -40,14 +41,22 @@ class TestTreeMethodMulti:
     @given(
         exact_parameter_strategy,
         hist_parameter_strategy,
+        hist_cache_strategy,
         strategies.integers(1, 20),
         tm.multi_dataset_strategy,
     )
     @settings(deadline=None, print_blob=True)
-    def test_approx(self, param, hist_param, num_rounds, dataset):
+    def test_approx(
+        self, param: Dict[str, Any],
+        hist_param: Dict[str, Any],
+        cache_param: Dict[str, Any],
+        num_rounds: int,
+        dataset: tm.TestDataset,
+    ) -> None:
         param["tree_method"] = "approx"
         param = dataset.set_params(param)
         param.update(hist_param)
+        param.update(cache_param)
         result = train_result(param, dataset.get_dmat(), num_rounds)
         note(result)
         assert tm.non_increasing(result["train"][dataset.metric])
@@ -55,18 +64,25 @@ class TestTreeMethodMulti:
     @given(
         exact_parameter_strategy,
         hist_multi_parameter_strategy,
+        hist_cache_strategy,
         strategies.integers(1, 20),
         tm.multi_dataset_strategy,
     )
     @settings(deadline=None, print_blob=True)
     def test_hist(
-        self, param: dict, hist_param: dict, num_rounds: int, dataset: tm.TestDataset
+        self,
+        param: Dict[str, Any],
+        hist_param: Dict[str, Any],
+        cache_param: Dict[str, Any],
+        num_rounds: int,
+        dataset: tm.TestDataset,
     ) -> None:
         if dataset.name.endswith("-l1"):
             return
         param["tree_method"] = "hist"
         param = dataset.set_params(param)
         param.update(hist_param)
+        param.update(cache_param)
         result = train_result(param, dataset.get_dmat(), num_rounds)
         note(result)
         assert tm.non_increasing(result["train"][dataset.metric])
@@ -91,14 +107,23 @@ class TestTreeMethod:
     @given(
         exact_parameter_strategy,
         hist_parameter_strategy,
+        hist_cache_strategy,
         strategies.integers(1, 20),
         tm.make_dataset_strategy(),
     )
     @settings(deadline=None, print_blob=True)
-    def test_approx(self, param, hist_param, num_rounds, dataset):
+    def test_approx(
+        self,
+        param: Dict[str, Any],
+        hist_param: Dict[str, Any],
+        cache_param: Dict[str, Any],
+        num_rounds: int,
+        dataset: tm.TestDataset,
+    ) -> None:
         param["tree_method"] = "approx"
         param = dataset.set_params(param)
         param.update(hist_param)
+        param.update(cache_param)
         result = train_result(param, dataset.get_dmat(), num_rounds)
         note(result)
         assert tm.non_increasing(result["train"][dataset.metric])
@@ -130,17 +155,25 @@ class TestTreeMethod:
     @given(
         exact_parameter_strategy,
         hist_parameter_strategy,
+        hist_cache_strategy,
         strategies.integers(1, 20),
         tm.make_dataset_strategy()
     )
     @settings(deadline=None, print_blob=True)
-    def test_hist(self, param: dict, hist_param: dict, num_rounds: int, dataset: tm.TestDataset) -> None:
-        param['tree_method'] = 'hist'
+    def test_hist(
+        self, param: Dict[str, Any],
+        hist_param: Dict[str, Any],
+        cache_param: Dict[str, Any],
+        num_rounds: int,
+        dataset: tm.TestDataset,
+    ) -> None:
+        param["tree_method"] = "hist"
         param = dataset.set_params(param)
         param.update(hist_param)
+        param.update(cache_param)
         result = train_result(param, dataset.get_dmat(), num_rounds)
         note(result)
-        assert tm.non_increasing(result['train'][dataset.metric])
+        assert tm.non_increasing(result["train"][dataset.metric])
 
     def test_hist_categorical(self):
         # hist must be same as exact on all-categorial data