Add support for XGBoost UBJSON in FIL

dependencies.yaml

@@ -503,3 +503,4 @@ dependencies:
           - pandas
           - *scikit_learn
           - seaborn
+          - xgboost

notebooks/forest_inference_demo.ipynb

@@ -275,7 +275,7 @@
     "    algo='BATCH_TREE_REORG',\n",
     "    output_class=True,\n",
     "    threshold=0.50,\n",
-    "    model_type='xgboost'\n",
+    "    model_type='xgboost_ubj'\n",
     ")"
    ]
   },
@@ -507,7 +507,7 @@
     "       algo='BATCH_TREE_REORG',\n",
     "       output_class=True,\n",
     "       threshold=0.50,\n",
-    "       model_type='xgboost'\n",
+    "       model_type='xgboost_ubj'\n",
     "    )"
    ]
   },

python/cuml/cuml/experimental/fil/fil.pyx

@@ -803,7 +803,7 @@ class ForestInference(UniversalBase, CMajorInputTagMixin):
             only for models trained and double precision and when exact
             conformance between results from FIL and the original training
             framework is of paramount importance.
-        model_type : {'xgboost', 'xgboost_json', 'lightgbm',
+        model_type : {'xgboost_ubj', 'xgboost_json', 'xgboost', 'lightgbm',
             'treelite_checkpoint', None }, default=None
             The serialization format for the model file. If None, a best-effort
             guess will be made based on the file extension.
@@ -841,18 +841,26 @@ class ForestInference(UniversalBase, CMajorInputTagMixin):
             extension = pathlib.Path(path).suffix
             if extension == '.json':
                 model_type = 'xgboost_json'
+            elif extension == '.ubj':
+                model_type = 'xgboost_ubj'
             elif extension == '.model':
                 model_type = 'xgboost'
             elif extension == '.txt':
                 model_type = 'lightgbm'
             else:
                 model_type = 'treelite_checkpoint'
-        if model_type == 'treelite_checkpoint':
+        if model_type == "treelite_checkpoint":
             tl_model = treelite.frontend.Model.deserialize(path)
+        elif model_type == "xgboost_ubj":
+            tl_model = treelite.frontend.load_xgboost_model(path, format_choice="ubjson")
+        elif model_type == "xgboost_json":
+            tl_model = treelite.frontend.load_xgboost_model(path, format_choice="json")
+        elif model_type == "xgboost":
+            tl_model = treelite.frontend.load_xgboost_model_legacy_binary(path)
+        elif model_type == "lightgbm":
+            tl_model = treelite.frontend.load_lightgbm_model(path)
         else:
-            tl_model = treelite.frontend.Model.load(
-                path, model_type
-            )
+            raise ValueError(f"Unknown model type: {model_type}")
         if default_chunk_size is None:
             default_chunk_size = threads_per_tree
         return cls(

python/cuml/cuml/fil/fil.pyx

@@ -51,6 +51,9 @@ cdef extern from "treelite/c_api.h":
         size_t nitem
     ctypedef void* TreeliteModelHandle
     ctypedef void* TreeliteGTILConfigHandle
+    cdef int TreeliteLoadXGBoostModelUBJSON(const char* filename,
+                                            const char* config_json,
+                                            TreeliteModelHandle* out) except +
     cdef int TreeliteLoadXGBoostModelLegacyBinary(const char* filename,
                                                   const char* config_json,
                                                   TreeliteModelHandle* out) except +
@@ -188,7 +191,7 @@ cdef class TreeliteModel():
         return model
 
     @classmethod
-    def from_filename(cls, filename, model_type="xgboost"):
+    def from_filename(cls, filename, model_type="xgboost_ubj"):
         """
         Returns a TreeliteModel object loaded from `filename`
 
@@ -198,15 +201,15 @@ cdef class TreeliteModel():
             Path to treelite model file to load
 
         model_type : string
-            Type of model: 'xgboost', 'xgboost_json', or 'lightgbm'
+            Type of model: 'xgboost_ubj', 'xgboost_json', 'xgboost' or 'lightgbm'
         """
         cdef bytes filename_bytes = filename.encode("UTF-8")
         cdef bytes config_bytes = b"{}"
         cdef TreeliteModelHandle handle
         cdef int res
         cdef str err_msg
-        if model_type == "xgboost":
-            res = TreeliteLoadXGBoostModelLegacyBinary(filename_bytes, config_bytes, &handle)
+        if model_type == "xgboost_ubj":
+            res = TreeliteLoadXGBoostModelUBJSON(filename_bytes, config_bytes, &handle)
             if res < 0:
                 err_msg = TreeliteGetLastError().decode("UTF-8")
                 raise RuntimeError(f"Failed to load {filename} ({err_msg})")
@@ -215,6 +218,11 @@ cdef class TreeliteModel():
             if res < 0:
                 err_msg = TreeliteGetLastError().decode("UTF-8")
                 raise RuntimeError(f"Failed to load {filename} ({err_msg})")
+        elif model_type == "xgboost":
+            res = TreeliteLoadXGBoostModelLegacyBinary(filename_bytes, config_bytes, &handle)
+            if res < 0:
+                err_msg = TreeliteGetLastError().decode("UTF-8")
+                raise RuntimeError(f"Failed to load {filename} ({err_msg})")
         elif model_type == "lightgbm":
             logger.warn("Treelite currently does not support float64 model"
                         " parameters. Accuracy may degrade slightly relative"
@@ -953,7 +961,7 @@ class ForestInference(Base,
              n_items=0,
              compute_shape_str=False,
              precision='native',
-             model_type="xgboost",
+             model_type="xgboost_ubj",
              handle=None):
         """
         Returns a FIL instance containing the forest saved in `filename`
@@ -1018,9 +1026,14 @@ class ForestInference(Base,
               thresholds
             - ``'float64'``: always load in float64
 
-        model_type : string (default="xgboost")
-            Format of the saved treelite model to be load.
-            It can be 'xgboost', 'xgboost_json', 'lightgbm'.
+        model_type : string (default="xgboost_ubj")
+            Format of the saved tree model to be load.
+            It can be one of the following:
+
+            - ``'xgboost_ubj'``: XGBoost model, using the UBJSON format (default in XGBoost 2.1+)
+            - ``'xgboost_json'``: XGBoost model, using the JSON format
+            - ``'xgboost'``: XGBoost model, using the legacy binary format
+            - ``'lightgbm'``: LightGBM model
 
         Returns
         -------