Merge branch 'main' into add_custom_order_to_cyclic_ctl_main

nvflare/app_opt/xgboost/tree_based/executor.py

@@ -26,7 +26,7 @@
 from nvflare.apis.signal import Signal
 from nvflare.app_common.app_constant import AppConstants
 from nvflare.app_opt.xgboost.data_loader import XGBDataLoader
-from nvflare.app_opt.xgboost.tree_based.shareable_generator import update_model
+from nvflare.app_opt.xgboost.tree_based.utils import update_model
 from nvflare.fuel.utils.import_utils import optional_import
 from nvflare.security.logging import secure_format_exception
 

nvflare/app_opt/xgboost/tree_based/shareable_generator.py

@@ -20,44 +20,7 @@
 from nvflare.app_common.abstract.model import ModelLearnable, ModelLearnableKey, model_learnable_to_dxo
 from nvflare.app_common.abstract.shareable_generator import ShareableGenerator
 from nvflare.app_common.app_constant import AppConstants
-
-
-def _get_xgboost_model_attr(xgb_model):
-    num_parallel_tree = int(
-        xgb_model["learner"]["gradient_booster"]["model"]["gbtree_model_param"]["num_parallel_tree"]
-    )
-    num_trees = int(xgb_model["learner"]["gradient_booster"]["model"]["gbtree_model_param"]["num_trees"])
-    return num_parallel_tree, num_trees
-
-
-def update_model(prev_model, model_update):
-    if not prev_model:
-        return model_update
-    else:
-        # Append all trees
-        # get the parameters
-        pre_num_parallel_tree, pre_num_trees = _get_xgboost_model_attr(prev_model)
-        cur_num_parallel_tree, add_num_trees = _get_xgboost_model_attr(model_update)
-
-        # check num_parallel_tree, should be consistent
-        if cur_num_parallel_tree != pre_num_parallel_tree:
-            raise ValueError(
-                f"add_num_parallel_tree should not change, previous {pre_num_parallel_tree}, current {add_num_parallel_tree}"
-            )
-        prev_model["learner"]["gradient_booster"]["model"]["gbtree_model_param"]["num_trees"] = str(
-            pre_num_trees + cur_num_parallel_tree
-        )
-        # append the new trees
-        append_info = model_update["learner"]["gradient_booster"]["model"]["trees"]
-        for tree_ct in range(cur_num_parallel_tree):
-            append_info[tree_ct]["id"] = pre_num_trees + tree_ct
-            prev_model["learner"]["gradient_booster"]["model"]["trees"].append(append_info[tree_ct])
-            prev_model["learner"]["gradient_booster"]["model"]["tree_info"].append(0)
-        # append iteration_indptr
-        prev_model["learner"]["gradient_booster"]["model"]["iteration_indptr"].append(
-            pre_num_trees + cur_num_parallel_tree
-        )
-        return prev_model
+from nvflare.app_opt.xgboost.tree_based.utils import update_model
 
 
 class XGBModelShareableGenerator(ShareableGenerator):

nvflare/app_opt/xgboost/tree_based/utils.py

@@ -0,0 +1,51 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+def _get_xgboost_model_attr(xgb_model):
+    num_parallel_tree = int(
+        xgb_model["learner"]["gradient_booster"]["model"]["gbtree_model_param"]["num_parallel_tree"]
+    )
+    num_trees = int(xgb_model["learner"]["gradient_booster"]["model"]["gbtree_model_param"]["num_trees"])
+    return num_parallel_tree, num_trees
+
+
+def update_model(prev_model, model_update):
+    if not prev_model:
+        return model_update
+    else:
+        # Append all trees
+        # get the parameters
+        pre_num_parallel_tree, pre_num_trees = _get_xgboost_model_attr(prev_model)
+        cur_num_parallel_tree, add_num_trees = _get_xgboost_model_attr(model_update)
+
+        # check num_parallel_tree, should be consistent
+        if cur_num_parallel_tree != pre_num_parallel_tree:
+            raise ValueError(
+                f"add_num_parallel_tree should not change, previous {pre_num_parallel_tree}, current {cur_num_parallel_tree}"
+            )
+        prev_model["learner"]["gradient_booster"]["model"]["gbtree_model_param"]["num_trees"] = str(
+            pre_num_trees + cur_num_parallel_tree
+        )
+        # append the new trees
+        append_info = model_update["learner"]["gradient_booster"]["model"]["trees"]
+        for tree_ct in range(cur_num_parallel_tree):
+            append_info[tree_ct]["id"] = pre_num_trees + tree_ct
+            prev_model["learner"]["gradient_booster"]["model"]["trees"].append(append_info[tree_ct])
+            prev_model["learner"]["gradient_booster"]["model"]["tree_info"].append(0)
+        # append iteration_indptr
+        prev_model["learner"]["gradient_booster"]["model"]["iteration_indptr"].append(
+            pre_num_trees + cur_num_parallel_tree
+        )
+        return prev_model