[XGBoost,MetaSchedule] Support xgb set tree method

python/tvm/meta_schedule/cost_model/cost_model.py

@@ -127,9 +127,12 @@ def create(
         if kind == "xgb":
             return XGBModel(*args, **kwargs)  # type: ignore
 
-        if "num_tuning_cores" in kwargs:
-            # num_tuning_cores is only relevant for XGBModel.
-            kwargs.pop("num_tuning_cores")
+        # params only relevant to XGBModel
+        _xgb_params = ["num_tuning_cores", "tree_method"]
+
+        for param in _xgb_params:
+            if param in kwargs:
+                kwargs.pop(param)
 
         if kind == "random":
             return RandomModel(*args, **kwargs)  # type: ignore

python/tvm/meta_schedule/cost_model/xgb_model.py

@@ -21,6 +21,8 @@
 from itertools import chain as itertools_chain
 from typing import TYPE_CHECKING, Any, Callable, Dict, List, NamedTuple, Optional, Tuple
 
+from typing_extensions import Literal
+
 import numpy as np  # type: ignore
 
 from ...contrib.tar import tar, untar
@@ -202,6 +204,8 @@ def average_peak_score(
 class XGBConfig(NamedTuple):
     """XGBoost model configuration
 
+    Reference: https://xgboost.readthedocs.io/en/stable/parameter.html
+
     Parameters
     ----------
     max_depth : int
@@ -217,6 +221,8 @@ class XGBConfig(NamedTuple):
     nthread : Optional[int],
         The number of threads to use.
         Default is None, which means to use physical number of cores.
+    tree_method : Literal["auto", "exact", "approx", "hist", "gpu_hist"]
+        The tree construction algorithm used in XGBoost.
     """
 
     max_depth: int = 10
@@ -225,15 +231,19 @@ class XGBConfig(NamedTuple):
     eta: float = 0.2
     seed: int = 43
     nthread: Optional[int] = None
+    tree_method: Literal["auto", "exact", "approx", "hist", "gpu_hist"] = "auto"
 
     def to_dict(self):
+        """Convert to dict"""
+
         return {
             "max_depth": self.max_depth,
             "gamma": self.gamma,
             "min_child_weight": self.min_child_weight,
             "eta": self.eta,
             "seed": self.seed,
             "nthread": self.nthread,
+            "tree_method": self.tree_method,
         }
 
 
@@ -334,6 +344,7 @@ def __init__(
         average_peak_n: int = 32,
         adaptive_training: bool = True,
         num_tuning_cores: Optional[int] = None,
+        tree_method: Optional[Literal["auto", "exact", "approx", "hist", "gpu_hist"]] = None,
     ):
         super().__init__()
         if not isinstance(extractor, FeatureExtractor):
@@ -348,6 +359,9 @@ def __init__(
             else:
                 config = config._replace(nthread=num_tuning_cores)
 
+        if tree_method is not None:
+            config._replace(tree_method=tree_method)
+
         self.config = config
         # behavior of randomness
         self.num_warmup_samples = num_warmup_samples

python/tvm/meta_schedule/tune.py

@@ -108,7 +108,7 @@ def tune_tasks(
     elif not isinstance(database, Database):
         database = Database.create(database, module_equality=module_equality)
     if not isinstance(cost_model, CostModel):
-        cost_model = CostModel.create(cost_model, num_tuning_cores=num_cores)
+        cost_model = CostModel.create(cost_model, num_tuning_cores=num_cores, tree_method="auto")
     if isinstance(measure_callbacks, MeasureCallback):
         measure_callbacks = [measure_callbacks]
     elif measure_callbacks == "default":